[Comp-neuro] Key papers in computational neuroscience

Cyrus Omar comar2 at illinois.edu
Fri Jul 18 18:56:56 CEST 2008


Here is the email without all the extra spaces:

This is a collection of references obtained in response to a request for key
papers from the computational neuroscience community. I have excluded
self-citations (but many of those excluded papers actually appear in my own
list of key papers below). I have removed the names of respondents, but have
left their comments in, as these can be very useful.
Many thanks to all those who contributed to this wide-ranging collection.
Jim Stone, 18th July 2008.
--
JV Stone's key papers:
SB Laughlin. A simple coding procedure enhances a neuron's
informationcapacity. Z Naturforsch, 36c:910{912, 1981.See other papers by
Laughlin which cover similar material.
Lettvin, J.Y., Maturana, H.R., McCulloch, W.S., and Pitts, W.H., What the
Frog¼s Eye Tells the Frog's Brain, Proc. Inst. Radio Engr. 47:1940-1951,
1959.
Ballard, DH, Cortical connections and parallel processing: Structure and
function, in Vision, in Brain and cooperative computation, pp 563-621, 1987,
Arbib, MA and Hanson AR (Eds).
Y Weiss, EP Simoncelli, and EH Adelson. Motion illusions as optimal
percepts. Nature Neuroscience, 5(6):598 604, 2002.
BA Olshausen and DJ Field. Sparse coding of sensory inputs. Current Opinion
in Neurobiology, 14:481 487, 2004.
T Poggio, V Torre, and C Koch. Computational vision and regularization
theory. Nature, 317:314 319, 1985.
AA Stocker and EP Simoncelli. Noise characteristics and prior expectations
in human visual speed perception. Nature Neuroscience, 9(4):578 585, 2006.
Marr, D., and T. Poggio. <
http://cbcl.mit.edu/people/poggio/journals/marr-poggio-science-1976.pdf>Cooperative
Computation of Stereo Disparity, Science, 194, 283-287, 1976.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. Learning
representations by back-propagating errors. Nature, 323, 533--536.
Hinton, G. E. and Nowlan, S. J. How learning can guide evolution. Complex
Systems, 1, 495--502.
Hinton, G. E. and Plaut, D. C. Using fast weights to deblur old memories.
Proceedings of the Ninth Annual Conference of the Cognitive Science Society,
Seattle, WA
Becker, S. and Hinton, G. E. A self-organizing neural network that discovers
surfaces in random-dot stereograms. Nature, 355:6356, 161-163
Ackley, D. H., Hinton, G. E., and Sejnowski, T. J. A learning algorithm for
Boltzmann machines. Cognitive Science, 9, 147-169.
@article{DURBIN_WILLSHAW_TSP, author ="Durbin, R and Willshaw, D", title =
"An analogue approach to the travelling salesman problem using an elastic
net method", journal = "Nature", volume = "326", number = "6114", pages =
"689-691", month = "", year = "1987" }
@article{DOUGLAS_CANONICAL_89, author = "Douglas, RJ and Martin, KAC and
Whitteridge, D", title = "A Canonical Microcircuit for Neocortex", journal =
"Neural Computation", volume = "1", number = "", pages = "480-488", month =
"", year = "1989" }
@article{SWINDALE82, author = "Swindale, NV", title = "A model for the
formation of orientation columns", journal = "Proceedings Royal Society
London B", volume = "215", number = "", pages = "211-230", month = "", year
= "1982" }

Zohary, E, Shadlen, MN and Newsome, WT (1994). Correlated neuronal discharge
rate and its implications for psychophysical performance. Nature
370:140-143.
Hopfield's papers (see below).
--
Hodgkin and Huxley 1952d (the modeling paper)
--
Song and Abbott: Cortical development and remapping through spike
timing-dependent plasticity.Neuron 32:339-50, 2001
and
Buonomano and Merzevich: Temporal information transformed into a spatial
code by a neural network with realistic properties.Science. 1995 Feb
17;267(5200):1028-30.
--
Wilson HR, Cowan JD.Excitatory and inhibitory interactions in localized
populations of modelneurons. Biophys J. 1972 Jan;12(1):1-24.
H.B. Barlow, The mechanical mind.Ann. Rev. Neurosci. 13 15-24 (1990)It is
about a simple model of consciousness.

--
>From the cognitive side of computational neuroscience and I recommend:
Pouget A, Deneve S, Duhamel JR (2002) A computational perspective on the
neural basis of multisensory spatial representations. Nat Rev Neurosci. 3:
741-747.
Hamker, F.H., Zirnsak, M., Calow, D., Lappe, M. (2008)ÝThe peri-saccadic
perception of objects and space.ÝPLOS Computational Biology 4(2):e21
Olshausen BA, Field DJ. 1996. Emergence of simple-cell receptive field
properties by learning a sparse code for natural images. Nature 381:607-9.
--
I was really influenced by
@article{Atick92, Author = {Atick, Joseph J.}, Journal = {Network:
{C}omputation in {N}eural {S}ystems}, Number = {2}, Pages = {213--52}, Title
= {Could {I}nformation {T}heory {P}rovide an {E}cological {T}heory of
{S}ensory {P}rocessing?}, Volume = {3}, Year = {1992}}
which is a review paper rather related to the seminal papers from Barlow and
Marr.
--
Wilson HR, Cowan JD.Excitatory and inhibitory interactions in localized
populations of modelneurons.Biophys J. 1972 Jan;12(1):1-24.
--
Wiring Optimization Dmitri B. ChklovskiiTraub's CA1 model/Pinsky Rinzel 2
compartmental modelsErik De Schutter's Purkinje cell modelsHenry Markram's
Cortical ModelsRolls & Treves - Hippocampal NetworkPolsky & Mel - 2layer
pyramidal cell modelTerry Sejnowski - Synapse, modeldbUpinder S Bhalla -
Million Synapses / Bistable systems
--
These papers introduced accurate models of calcium dynamics and
neuromodulatory effects on ion channel activity.
Bhalla US, Iyengar R.Emergent properties of networks of biological signaling
pathways.Science. 1999 Jan 15;283(5400):381-7.
Zador A, Koch C, Brown TH.Biophysical model of a Hebbian synapse.Proc Natl
Acad Sci U S A. 1990 Sep;87(17):6718-22.
Holmes WR, Levy WB.AbstractInsights into associative long-term potentiation
from computational models of NMDA receptor-mediated calcium influx and
intracellular calcium concentration changes.J Neurophysiol. 1990
May;63(5):1148-68.
--
There are two theoretical papers which, in my opinion, have had a strong
influence on the way we think about synaptic transmission and short term
plasticity today:
A W Liley and K A North. An electrical investigation of effects of
repetitivestimulation on mammalian neuromuscular junction. J Neurophysiol,
16(5):509 527, Sep 1953.
W J Betz. Depression of transmitter release at the neuromuscular junction of
thefrog. J Physiol, 206(3):629 644, 1970.
These were, of course, published before the term "computation neuroscience"
was used. The first proposed a mathematical model for vesicle pool
depletion, which is still in use today. The second was the first to extend
this with the release probability as a dynamic variable. These ideas were
then further popularised by these classic papers:
L F Abbott, J A Varela, K Sen, and S B Nelson. Synaptic depression and
corticalgain control. Science, 275(5297):220 224, Jan 1997.
M V Tsodyks and H Markram. The neural code between neocortical
pyramidalneurons depends on neurotransmitter release probability. Proc Natl
Acad Sci U SA, 94(2):719 723, Jan 1997.
What I found have during my collaborations with biologists was that not so
much the precise mathematical formulation, but the very basic ideas and
concepts explored in these papers have made a strong impact in the whole
field, and have certainly cleared the way for numerous further theoretical
studies.
Another paper I have come across just recently which I would consider as
rather important and useful is this:
J J Hopfield and A V M Herz. Rapid Local Synchronization of Action
Potentials: Toward Computation with Coupled Integrate-and-Fire Neurons. Proc
Natl Acad Sci U SA, 92(15): 6655-6662, Jul 1995.
Cited more than 150 times, it contains some strong results regarding the
behaviour of recurrent networks, and also anticipates a number of results
shown more recently.

--
Here is my top 12 papers, in chronological order. I have gone for ones that
make my science heart sing, that introduce a big idea, useful tool, connect
experiment and theory in a satisfying way, or are an example ofwork on a
topic that has been mysteriously under-represented.
I have tried to briefly qualify why they could be thought of as classic by
the wider community.
1) Willshaw and von der Malsburg (1979). Future hot topic: modelling
development Excellent interaction between theory and experiment - predicted
ephrins and eph receptors. http://www.jstor.org/stable/pdfplus/2418226.pdf2)
Laughlin (1981) Z. Naturforsch. C 36:910-2 Big idea: coding matches stimulus
statistics.
http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&TermToSearch=73038233)
Srinivasan et al. (1982) Proc. Roy. Soc. B 216(1205):427-59 Excellent
interaction between theory and experiment: predicts responses of first order
visual interneurons if they exploit spatial and temporal correlations to
reduce redundancy.
http://www.kyb.tuebingen.mpg.de/bethgegroup/teaching/ws0708_sem_retina_whitening/Srinivasan_et_al_1982.pdf
4) Buchsbaum and Gottschalk (1983). Proc. R. Soc. B 220:89-113 Excellent
interaction between theory and experiment: uses PCA to accurately calculate
the colour channels that maximise information transmission. Deserves to be
more widely known. http://www.jstor.org/stable/pdfplus/35873.pdf

5) Bialek et al. (1991) Science Useful application for theorist: neat method
for calculating stimulus filters in the response.
http://www2.hawaii.edu/~sstill/neural_code_91.pdf

6) Treves and Rolls (1992) Hippocampus 2(2):189-99 Excellent interaction
between theory and experiment: identified the function of the dentate gyrus
in the hippocampus, and matched network organisation to function far more
successfully that Marr.
http://www3.interscience.wiley.com/cgi-bin/fulltext/109711333/PDFSTART7) Van
Hateren (1992) J. Comp Phys. A 171:157-170 Excellent interaction between
theory and experiment: predicts visual spatiotemporal receptive fields of
cells connected to photoreceptors in the fly so as to maximise information
about natural images from first principles, with stunning success.
http://www.springerlink.com/content/h4681x344j378229/fulltext.pdf

8) Wolpert et al. (1995) Science 269(5232):1880-2 Big idea: internal models
and the use of priors.
http://keck.ucsf.edu/~houde/sensorimotor_jc/DMWolpert95a.pdf

9) Zemel et al. (1998) Neur. Comp. 10(2):403-30 Big idea: neurons encode
distributions, not single values
http://www.gatsby.ucl.ac.uk/~dayan/papers/zdp98.pdf

10) Van Rossum et al. (2000) J. Neuro. 20(23):8812-21 Excellent interaction
between theory and experiment: Simple application of Fokker-Planck equation
physics to explain functional consequences to the network of cellular level
experimental data. http://www.jneurosci.org/cgi/reprint/20/23/8812.pdf

11) Brunel (2000) J. Comp. Neuro 8:183-208 Useful application for theorist:
calculations of the population activity of a network of integrate-and-fire
neurons. http://www.springerlink.com/content/u446l5722lp03677/fulltext.pdf

12) Schreiber (2000) Physical Review Letters 85(2):461-64 Future hot topic:
Current best method to infer causal relationships between neurons using
information theory. http://prola.aps.org/pdf/PRL/v85/i2/p461_1
--
Here are the most important papers in 3 subjects, plasticity andsimple
neuron models and network dynamicsOf course, there are other categories in
Computational neuroscience(detailed neuron model, cortex modeling, vision,
audition etc) on which others will report.
1) In plasticity:
Hebb, 1949 (book)
Bienenstock, Cooper Munro, J. Neurosci.1982 (BCM rule)
Kohonen Neural Networks1993 (Kohonen algo in comp neuro perspective other
papers of him would also do)Hopfield, PNAS, 1982 (Hopfield model)
Amit Gutfreund Sompolinksy, Phys Rev A, 1985 (Analysis of Hopfield model)
Linsker PNAS, 1986 (emergence of field)
MacKay and Miller 1990 Neural Comput. (analysis of Linskers rule)
Miller and MacKay 1994 Neural Comput. the role of constraints
Gerstner et al, Nature 1996 (first paper on STDP)
Kempter et al. Phys Rev E, 1999 (first analysis of STDP)
Lisman, PNAS, 1999 (first model of plasticity based on calcium dynamics)
Song Miller Abbott, Nat. Neurosci, 2000 (popular paper on STDP)
Rossum et al. 2000, J. Neuroscie (STDP with soft bounds for the weights)
Fusi, Biological Cybernetics, 2002 (some general problems of Hebbian rules -
nice review of work of Fusi)_
Shouval et al., PNAS, 2002 (calcium model of plasticity)
Senn Tsodyks, Markram, Neural. comp. 2001 (STDP algorithm)
Fusi, Drew, Abbott Nat. Neuroscience 2005 (Cascade model)
Toyoizumi et al. PNAS 2005 (BCM rule for spiking neuron also optimized
information)

2) In simplified neuron models
Lapicque 07 (often cited as first integrate-and-fire model, even though it
does not show reset)
FitzHugh 1961, Biophys. Journal (2-dim neuron model)
Stein 1967, Biophys. Journal (some models of neural variability -
integrate-and-fire model with noise)
Ermentrout 1996, Neural Comput., Canonical type I model, quadratic
integrate-and-fire
Kistler et al. 1997, Neural Computation (systematic reduction to a threshold
model/Spike Response Model)
Latham 2000, J. Neurophys. quadratic integrate-and-fire
Izhikevich 2003, IEEE, 2-dim. neuron model
Fourcaud et al. 2003, J. Neurosci. exp. integrate-and-fire model
Jolivet et al. 2006, J. comput. Neurosci. -- spiking in real neurons can be
explained by threshold models
Badel et al. 2008, J. Neurophysiol. -- real neurons are exponential
integrate-and-fire models, this is a very recent paper, but it is really
important for the discussion of simple neuron models

3) Network dynamics
Wilson and Cowan, 1972
Amari 1974
Brunel and Hakim, 1999 Neural Computation
Gerstner 2000 Neural Computation
Brunel 2000 Comput. Neurosci

--
Finally, I am attaching a list of great papers. If I were trying to get
outsiders excited, I'd definitely use the Andy Schwartz paper on neural
prosthetics. Also think I would do Olshausen & Field as it really kicked
people off on thinking about natural images. The Hopfield paper is the
greatest of the bunch but is likely too old for what you're looking for.
Spike-timing-dependent plasticity is a hot topic and I think carries on a
great tradition of computational neuroscientists connecting cellular
plasticity to larger network functions; and I think Peter Dayan (and
Montague's in the original paper) work is some of the first that really puts
a framework in place for thinking about neuromodulators. But they're all
great, and I tried to hit many different contributions (maybe this is the
greatest message--that computational neuroscience pervades so many fields
from single-neuron computation to neuromodulators to models of memory).
1. Montague PR, Dayan P, Sejnowski TJ A framework for mesencephalic dopamine
systems based on predictive Hebbian learning. J Neurosci. 1996 Mar
1;16(5):1936-47.Abstract: We develop a theoretical framework that shows how
mesencephalic dopamine systems could distribute to their targets a signal
that represents information about future expectations. In particular, we
show how activity in the cerebral cortex can make predictions about future
receipt of reward and how fluctuations in the activity levels of neurons in
diffuse dopamine systems above and below baseline levels would represent
errors in these predictions that are delivered to cortical and subcortical
targets. We present a model for how such errors could be constructed in a
real brain that is consistent with physiological results for a subset of
dopaminergic neurons located in the ventral tegmental area and surrounding
dopaminergic neurons. The theory also makes testable predictions about human
choice behavior on a simple decision-making task. Furthermore, we show that,
through a simple influence on synaptic plasticity, fluctuations in dopamine
release can act to change the predictions in an appropriate manner.This
paper is the first of a series of papers setting up a framework for how
mesencephalic dopamine neurons represent reward and can serve as the basis
for temporal difference -based reward learning in which the reward is
offered at a delayed time.
*2. Strong, S., Koberle, R., de Ruyter van Steveninck, R. and Bialek, W.
1998. Entropy and information in neural spike trains, Physical Review
Letters 80: 197-200.Abstract. The nervous system represents time dependent
signals in sequences of discrete, identical action potentials or spikes;
information is carried only in the spike arrival times. We show how to
quantify this information, in bits, free from any assumptions about which
features of the spike train or input signal are most important, and we apply
this approach to the analysis of experiments on a motion sensitive neuron in
the fly visual system. This neuron transmits information about the visual
stimulus at rates of up to 90 bits/s, within a factor of 2 of the physical
limit set by the entropy of the spike train itself.This paper ushered in a
new set of techniques for characterizing spike trains using the methods of
information theory, and also illustrated that there was information on much
smaller time scales (~a couple ms) than had typically been assumed
previously.
3a. Abbott LF, Varela JA, Sen K, Nelson SB. Synaptic depression and cortical
gain control.Science. 1997 Jan 10;275(5297):220-4Abstract. Cortical neurons
receive synaptic inputs from thousands of afferents that fire action
potentials at rates ranging from less than 1 hertz to more than 200 hertz.
Both the number of afferents and their large dynamic range can mask changes
in the spatial and temporal pattern of synaptic activity, limiting the
ability of a cortical neuron to respond to its inputs. Modeling work based
on experimental measurements indicates that short-term depression of
intracortical synapses provides a dynamic gain-control mechanism that allows
equal percentage rate changes on rapidly and slowly firing afferents to
produce equal postsynaptic responses. Unlike inhibitory and adaptive
mechanisms that reduce responsiveness to all inputs, synaptic depression is
input-specific, leading to a dramatic increase in the sensitivity of a
neuron to subtle changes in the firing patterns of its afferents.

-AND-
3b. Markram H, Tsodyks M. Redistribution of synaptic efficacy between
neocortical pyramidal neurons. Nature. 1996 Aug
29;382(6594):807-10.Abstract. Experience-dependent potentiation and
depression of synaptic strength has been proposed to subserve learning and
memory by changing the gain of signals conveyed between neurons. Here we
examine synaptic plasticity between individual neocortical layer-5 pyramidal
neurons. We show that an increase in the synaptic response, induced by
pairing action-potential activity in pre- and postsynaptic neurons, was only
observed when synaptic input occurred at low frequencies. This
frequency-dependent increase in synaptic responses arises because of a
redistribution of the available synaptic efficacy and not because of an
increase in the efficacy. Redistribution of synaptic efficacy could
represent a mechanism to change the content, rather than the gain, of
signals conveyed between neurons.These 2 papers connected short-term
synaptic plasticity to important computational implications.
4a. Hopfield JJ. Neural networks and physical systems with emergent
collective computational abilities. Proc Natl Acad Sci U S A. 1982
Apr;79(8):2554-8Abstract. Computational properties of use of biological
organisms or to the construction of computers can emerge as collective
properties of systems having a large number of simple equivalent components
(or neurons). The physical meaning of content-addressable memory is
described by an appropriate phase space flow of the state of a system. A
model of such a system is given, based on aspects of neurobiology but
readily adapted to integrated circuits. The collective properties of this
model produce a content-addressable memory which correctly yields an entire
memory from any subpart of sufficient size. The algorithm for the time
evolution of the state of the system is based on asynchronous parallel
processing. Additional emergent collective properties include some capacity
for generalization, familiarity recognition, categorization, error
correction, and time sequence retention. The collective properties are only
weakly sensitive to details of the modeling or the failure of individual
devices.This classic paper illustrated the idea of attractor models and a
correspondence with energy surfaces. It is now universally permeates
discussions of long-term memory storage in networks, especially in the
hippocampus. It was followed more recently by the article below, which
expanded the idea of attractor models to continuous attractors this now is
the framework for discussion of many networks storing short-term memories
(the other set of models being the so-called ring models but i am not sure
of the original reference for those).
4b. Seung HS. How the brain keeps the eyes still. Proc Natl Acad Sci U S A.
1996 Nov 12;93(23):13339-44.Abstract. The brain can hold the eyes still
because it stores a memory of eye position. The brain's memory of horizontal
eye position appears to be represented by persistent neural activity in a
network known as the neural integrator, which is localized in the brainstem
and cerebellum. Existing experimental data are reinterpreted as evidence for
an "attractor hypothesis" that the persistent patterns of activity observed
in this network form an attractive line of fixed points in its state space.
Line attractor dynamics can be produced in linear or nonlinear neural
networks by learning mechanisms that precisely tune positive feedback.
5a. Song S, Miller KD, Abbott LF. Competitive Hebbian learning through
spike-timing-dependent synaptic plasticity. Nat Neurosci. 2000
Sep;3(9):919-26.Abstract. Hebbian models of development and learning require
both activity-dependent synaptic plasticity and a mechanism that induces
competition between different synapses. One form of experimentally observed
long-term synaptic plasticity, which we call spike-timing-dependent
plasticity (STDP), depends on the relative timing of pre- and postsynaptic
action potentials. In modeling studies, we find that this form of synaptic
modification can automatically balance synaptic strengths to make
postsynaptic firing irregular but more sensitive to presynaptic spike
timing. It has been argued that neurons in vivo operate in such a balanced
regime. Synapses modifiable by STDP compete for control of the timing of
postsynaptic action potentials. Inputs that fire the postsynaptic neuron
with short latency or that act in correlated groups are able to compete most
successfully and develop strong synapses, while synapses of longer-latency
or less-effective inputs are weakened.
-AND-

5b. Song S, Abbott LF.Neuron. Cortical development and remapping through
spike timing-dependent plasticity. 2001 Oct 25;32(2):339-50Abstract.
Long-term modification of synaptic efficacy can depend on the timing of pre-
and postsynaptic action potentials. In model studies, such spike
timing-dependent plasticity (STDP) introduces the desirable features of
competition among synapses and regulation of postsynaptic firing
characteristics. STDP strengthens synapses that receive correlated input,
which can lead to the formation of stimulus-selective columns and the
development, refinement, and maintenance of selectivity maps in network
models. The temporal asymmetry of STDP suppresses strong destabilizing
self-excitatory loops and allows a group of neurons that become selective
early in development to direct other neurons to become similarly selective.
STDP, acting alone without further hypothetical global constraints or
additional forms of plasticity, can also reproduce the remapping seen in
adult cortex following afferent lesions.The papers above have been seminal
in illustrating the implications for learning of spike-timing-dependent
synaptic plasticity
6. Polsky A, Mel BW, Schiller J. Nat Neurosci. 2004 Jun;7(6):621-7. Epub
2004 May 23.Computational subunits in thin dendrites of pyramidal
cells.Abstract. The thin basal and oblique dendrites of cortical pyramidal
neurons receive most of the synaptic inputs from other cells, but their
integrative properties remain uncertain. Previous studies have most often
reported global linear or sublinear summation. An alternative view,
supported by biophysical modeling studies, holds that thin dendrites provide
a layer of independent computational 'subunits' that sigmoidally modulate
their inputs prior to global summation. To distinguish these possibilities,
we combined confocal imaging and dual-site focal synaptic stimulation of
identified thin dendrites in rat neocortical pyramidal neurons. We found
that nearby inputs on the same branch summed sigmoidally, whereas widely
separated inputs or inputs to different branches summed linearly. This
strong spatial compartmentalization effect is incompatible with a global
summation rule and provides the first experimental support for a two-layer
'neural network' model of pyramidal neuron thin-branch integration. Our
findings could have important implications for the computing and
memory-related functions of cortical tissue.This paper, as well as previous
theoretical work, suggests that dendrites might enable single neurons to
behave as feedforward neural networks.
7. Medina JF, Nores WL, Mauk MD. Nature. 2002 Mar
21;416(6878):330-3.Inhibition of climbing fibres is a signal for the
extinction of conditioned eyelid responses.Abstract. A fundamental tenet of
cerebellar learning theories asserts that climbing fibre afferents from the
inferior olive provide a teaching signal that promotes the gradual
adaptation of movements. Data from several forms of motor learning provide
support for this tenet. In pavlovian eyelid conditioning, for example, where
a tone is repeatedly paired with a reinforcing unconditioned stimulus like
periorbital stimulation, the unconditioned stimulus promotes acquisition of
conditioned eyelid responses by activating climbing fibres. Climbing fibre
activity elicited by an unconditioned stimulus is inhibited during the
expression of conditioned responses-consistent with the inhibitory
projection from the cerebellum to inferior olive. Here, we show that
inhibition of climbing fibres serves as a teaching signal for extinction,
where learning not to respond is signalled by presenting a tone without the
unconditioned stimulus. We used reversible infusion of synaptic receptor
antagonists to show that blocking inhibitory input to the climbing fibres
prevents extinction of the conditioned response, whereas blocking excitatory
input induces extinction. These results, combined with analysis of climbing
fibre activity in a computer simulation of the cerebellar-olivary system,
suggest that transient inhibition of climbing fibres below their background
level is the signal that drives extinction.This is one of several
computational studies by Mauk and collaborators that are enhancing our
knowledge of cerebellar processing (also see similar papers by Raymond &
Lisberger applied to the VOR).
8. Olshausen BA, Field DJ. Nature. Emergence of simple-cell receptive field
properties by learning a sparse code for natural images. 1996 Jun
13;381(6583):607-9Abstract.The receptive fields of simple cells in mammalian
primary visual cortex can be characterized as being spatially localized,
oriented and bandpass (selective to structure at different spatial scales),
comparable to the basis functions of wavelet transforms. One approach to
understanding such response properties of visual neurons has been to
consider their relationship to the statistical structure of natural images
in terms of efficient coding. Along these lines, a number of studies have
attempted to train unsupervised learning algorithms on natural images in the
hope of developing receptive fields with similar properties, but none has
succeeded in producing a full set that spans the image space and contains
all three of the above properties. Here we investigate the proposal that a
coding strategy that maximizes sparseness is sufficient to account for these
properties. We show that a learning algorithm that attempts to find sparse
linear codes for natural scenes will develop a complete family of localized,
oriented, bandpass receptive fields, similar to those found in the primary
visual cortex. The resulting sparse image code provides a more efficient
representation for later stages of processing because it possesses a higher
degree of statistical independence among its outputs.This now classic study
suggests how the statistical structure of natural images may determine the
response properties of V1 cells, and set the stage for many later studies
discussing the concept of sparse coding of images.
9. Taylor DM, Tillery SI, Schwartz AB. Direct cortical control of 3D
neuroprosthetic devices. Science. 2002 Jun 7;296(5574):1829-32. Abstract.
Three-dimensional (3D) movement of neuroprosthetic devices can be controlled
by the activity of cortical neurons when appropriate algorithms are used to
decode intended movement in real time. Previous studies assumed that neurons
maintain fixed tuning properties, and the studies used subjects who were
unaware of the movements predicted by their recorded units. In this study,
subjects had real-time visual feedback of their brain-controlled
trajectories. Cell tuning properties changed when used for brain-controlled
movements. By using control algorithms that track these changes, subjects
made long sequences of 3D movements using far fewer cortical units than
expected. Daily practice improved movement accuracy and the directional
tuning of these units.This represents some of the seminal work decoding
cortical activity to control neural prosthetics.
10. Van Vreeswijk C, Abbott LF, Ermentrout GB. When inhibition not
excitation synchronizes neural firing. J Comput Neurosci. 1994
Dec;1(4):313-21.Abstract. Excitatory and inhibitory synaptic coupling can
have counter-intuitive effects on the synchronization of neuronal firing.
While it might appear that excitatory coupling would lead to
synchronization, we show that frequently inhibition rather than excitation
synchronizes firing. We study two identical neurons described by
integrate-and-fire models, general phase-coupled models or the
Hodgkin-Huxley model with mutual, non-instantaneous excitatory or inhibitory
synapses between them. We find that if the rise time of the synapse is
longer than the duration of an action potential, inhibition not excitation
leads to synchronized firing.
 If I were to update, I think I would add papers from:
1) Neuroeconomics & Reinforcement Learning-- in addition to the seminal work
by Dayan & Schultz (already in attached), perhaps Loewenstein/Seung paper on
matching behavior as a generic consequence of correlational learning rules.
2) Bayesian networks -- maybe Ma, Beck, Latham, Pouget or others on idea
that the brain may encode & compute with probabilities
--ENDOFFILE

Cyrus

On Fri, Jul 18, 2008 at 05:09, Jim Stone <j.v.stone at sheffield.ac.uk> wrote:

>
> T h i s   i s   a   c o l l e c t i o n   o f  r e f e r e n c e s   o b t
> a i n e d   i n  r e s p o n s e   t o   a   r e q u e s t  f o r   k e y
> p a p e r s   f r o m   t h e  c o m p u t a t i o n a l  n e u r o s c i e
> n c e   c o m m u n i t y .  I   h a v e   e x c l u d e d  s e l f - c i t
> a t i o n s   ( b u t   m a n y  o f   t h o s e   e x c l u d e d   p a p e
> r s  a c t u a l l y   a p p e a r   i n   m y  o w n   l i s t   o f   k e
> y   p a p e r s  b e l o w ) .   I   h a v e   r e m o v e d  t h e   n a m
> e s   o f  r e s p o n d e n t s ,   b u t   h a v e  l e f t   t h e i r
> c o m m e n t s   i n ,  a s   t h e s e   c a n   b e   v e r y  u s e f u
> l .
> M a n y   t h a n k s   t o   a l l   t h o s e  w h o   c o n t r i b u t
> e d   t o   t h i s  w i d e - r a n g i n g   c o l l e c t i o n .
> J i m   S t o n e ,   1 8 t h   J u l y   2 0 0 8 .
> - -
> J V   S t o n e ' s   k e y   p a p e r s :
> S B   L a u g h l i n .   A   s i m p l e  c o d i n g   p r o c e d u r e
>  e n h a n c e s   a   n e u r o n ' s  i n f o r m a t i o n c a p a c i t
> y .   Z   N a t u r f o r s c h ,  3 6 c : 9 1 0 { 9 1 2 ,   1 9 8 1 . S e e
>   o t h e r   p a p e r s   b y  L a u g h l i n   w h i c h   c o v e r  s
> i m i l a r   m a t e r i a l .
> L e t t v i n ,   J . Y . ,   M a t u r a n a ,  H . R . ,   M c C u l l o
> c h ,   W . S . ,  a n d   P i t t s ,   W . H . ,   W h a t  t h e   F r o
> g ¼ s   E y e   T e l l s   t h e  F r o g ' s   B r a i n ,   P r o c .  I
> n s t .   R a d i o   E n g r .  4 7 : 1 9 4 0 - 1 9 5 1 ,   1 9 5 9 .
> B a l l a r d ,   D H ,   C o r t i c a l  c o n n e c t i o n s   a n d
> p a r a l l e l  p r o c e s s i n g :   S t r u c t u r e  a n d   f u n c
> t i o n ,   i n   V i s i o n ,  i n   B r a i n   a n d   c o o p e r a t i
> v e  c o m p u t a t i o n ,   p p   5 6 3 - 6 2 1 ,  1 9 8 7 ,   A r b i b
> ,   M A   a n d  H a n s o n   A R   ( E d s ) .
> Y   W e i s s ,   E P   S i m o n c e l l i ,  a n d   E H   A d e l s o n
> .   M o t i o n  i l l u s i o n s   a s   o p t i m a l  p e r c e p t s .
>   N a t u r e   N e u r o s c i e n c e ,  5 ( 6 ) : 5 9 8   6 0 4 ,   2 0 0
> 2 .
> B A   O l s h a u s e n   a n d   D J  F i e l d .   S p a r s e   c o d i
> n g   o f  s e n s o r y   i n p u t s .   C u r r e n t  O p i n i o n   i
> n   N e u r o b i o l o g y ,  1 4 : 4 8 1   4 8 7 ,   2 0 0 4 .
> T   P o g g i o ,   V   T o r r e ,   a n d   C  K o c h .   C o m p u t a
> t i o n a l  v i s i o n   a n d  r e g u l a r i z a t i o n   t h e o r y
> .  N a t u r e ,   3 1 7 : 3 1 4   3 1 9 ,  1 9 8 5 .
> A A   S t o c k e r   a n d   E P  S i m o n c e l l i .   N o i s e  c h a
> r a c t e r i s t i c s   a n d  p r i o r   e x p e c t a t i o n s   i n
>  h u m a n   v i s u a l   s p e e d  p e r c e p t i o n .   N a t u r e  N
> e u r o s c i e n c e ,   9 ( 4 ) : 5 7 8  5 8 5 ,   2 0 0 6 .
> M a r r ,   D . ,   a n d   T .   P o g g i o .  < h t t p : / / c b c l .
> m i t . e d u / p e o p l e / p o g g i o / j o u r n a l s / m a r r - p o
> g g i o - s c i e n c e - 1 9 7 6 . p d f > C o o p e r a t i v e   C o m p
> u t a t i o n  o f   S t e r e o   D i s p a r i t y ,  S c i e n c e ,   1
> 9 4 ,   2 8 3 - 2 8 7 ,  1 9 7 6 .
> R u m e l h a r t ,   D .   E . ,  H i n t o n ,   G .   E . ,   a n d  W i
> l l i a m s ,   R .   J .   L e a r n i n g  r e p r e s e n t a t i o n s
> b y  b a c k - p r o p a g a t i n g   e r r o r s .  N a t u r e ,   3 2 3
> ,   5 3 3 - - 5 3 6 .
> H i n t o n ,   G .   E .   a n d  N o w l a n ,   S .   J .   H o w  l e a
> r n i n g   c a n   g u i d e  e v o l u t i o n .   C o m p l e x  S y s t
> e m s ,   1 ,   4 9 5 - - 5 0 2 .
> H i n t o n ,   G .   E .   a n d   P l a u t ,  D .   C .     U s i n g
> f a s t  w e i g h t s   t o   d e b l u r   o l d  m e m o r i e s .   P r
> o c e e d i n g s   o f   t h e   N i n t h  A n n u a l   C o n f e r e n c
> e   o f   t h e  C o g n i t i v e   S c i e n c e  S o c i e t y ,   S e a
> t t l e ,   W A
> B e c k e r ,   S .   a n d   H i n t o n ,  G .   E .     A   s e l f - o
> r g a n i z i n g  n e u r a l   n e t w o r k   t h a t  d i s c o v e r s
>   s u r f a c e s   i n  r a n d o m - d o t   s t e r e o g r a m s .  N a
> t u r e ,   3 5 5 : 6 3 5 6 ,  1 6 1 - 1 6 3
> A c k l e y ,   D .   H . ,   H i n t o n ,  G .   E . ,   a n d   S e j n
> o w s k i ,   T .  J .     A   l e a r n i n g   a l g o r i t h m  f o r
> B o l t z m a n n   m a c h i n e s .   C o g n i t i v e   S c i e n c e ,
>   9 ,   1 4 7 - 1 6 9 .
> @ a r t i c l e { D U R B I N _ W I L L S H A W _ T S P ,        a u t h o
> r     = " D u r b i n ,   R  a n d   W i l l s h a w ,   D " ,        t i t
> l e       =   " A n  a n a l o g u e   a p p r o a c h   t o   t h e  t r a
> v e l l i n g   s a l e s m a n  p r o b l e m   u s i n g   a n   e l a s t
> i c  n e t   m e t h o d " ,        j o u r n a l   =   " N a t u r e " ,
>      v o l u m e     =   " 3 2 6 " ,        n u m b e r     =   " 6 1 1 4 "
> ,        p a g e s       =   " 6 8 9 - 6 9 1 " ,        m o n t h       =
> " " ,        y e a r         =   " 1 9 8 7 "        }
> @ a r t i c l e { D O U G L A S _ C A N O N I C A L _ 8 9 ,        a u t h
> o r     =   " D o u g l a s ,  R J   a n d   M a r t i n ,   K A C   a n d
>  W h i t t e r i d g e ,   D " ,        t i t l e       =   " A  C a n o n i
> c a l   M i c r o c i r c u i t  f o r   N e o c o r t e x " ,        j o u
> r n a l   =   " N e u r a l   C o m p u t a t i o n " ,        v o l u m e
>   =   " 1 " ,        n u m b e r     =   " " ,        p a g e s       =   "
> 4 8 0 - 4 8 8 " ,        m o n t h       =   " " ,        y e a r         =
>   " 1 9 8 9 "        }
> @ a r t i c l e { S W I N D A L E 8 2 ,        a u t h o r     =   " S w i
> n d a l e ,   N V " ,        t i t l e       =   " A   m o d e l  f o r   t
> h e   f o r m a t i o n   o f  o r i e n t a t i o n   c o l u m n s " ,
>    j o u r n a l   =  " P r o c e e d i n g s   R o y a l  S o c i e t y   L
> o n d o n   B " ,        v o l u m e     =   " 2 1 5 " ,        n u m b e r
>     =   " " ,        p a g e s       =   " 2 1 1 - 2 3 0 " ,        m o n t
> h       =   " " ,        y e a r         =   " 1 9 8 2 "        }
>
> Z o h a r y ,   E ,   S h a d l e n ,   M N  a n d   N e w s o m e ,   W T
>   ( 1 9 9 4 ) .  C o r r e l a t e d   n e u r o n a l  d i s c h a r g e
> r a t e   a n d   i t s  i m p l i c a t i o n s   f o r  p s y c h o p h y
> s i c a l  p e r f o r m a n c e .   N a t u r e  3 7 0 : 1 4 0 - 1 4 3 .
> H o p f i e l d 's   p a p e r s   ( s e e   b e l o w ) .
> - -
> H o d g k i n   a n d   H u x l e y   1 9 5 2 d  ( t h e   m o d e l i n g
>   p a p e r )
> - -
> S o n g   a n d   A b b o t t :  C o r t i c a l   d e v e l o p m e n t
> a n d  r e m a p p i n g   t h r o u g h   s p i k e  t i m i n g - d e p e
> n d e n t  p l a s t i c i t y . N e u r o n   3 2 : 3 3 9 - 5 0 ,   2 0 0 1
>
> a n d
> B u o n o m a n o   a n d   M e r z e v i c h :  T e m p o r a l   i n f o
> r m a t i o n  t r a n s f o r m e d   i n t o   a  s p a t i a l   c o d e
>   b y   a   n e u r a l  n e t w o r k   w i t h   r e a l i s t i c  p r o
> p e r t i e s . S c i e n c e .   1 9 9 5   F e b   1 7 ; 2 6 7 ( 5 2 0 0 )
> : 1 0 2 8 - 3 0 .
> - -
> W i l s o n   H R ,   C o w a n   J D . E x c i t a t o r y   a n d  i n h
> i b i t o r y   i n t e r a c t i o n s  i n   l o c a l i z e d   p o p u l
> a t i o n s  o f   m o d e l n e u r o n s .   B i o p h y s   J .   1 9 7 2
>  J a n ; 1 2 ( 1 ) : 1 - 2 4 .
> H . B .   B a r l o w ,   T h e   m e c h a n i c a l   m i n d . A n n .
> R e v .   N e u r o s c i .   1 3   1 5 - 2 4   ( 1 9 9 0 ) I t   i s   a b
> o u t   a   s i m p l e  m o d e l   o f   c o n s c i o u s n e s s .
>
> - -
> F r o m   t h e   c o g n i t i v e   s i d e  o f   c o m p u t a t i o n
> a l  n e u r o s c i e n c e   a n d   I  r e c o m m e n d :
> P o u g e t   A ,   D e n e v e   S ,  D u h a m e l   J R   ( 2 0 0 2 )
> A  c o m p u t a t i o n a l  p e r s p e c t i v e   o n   t h e  n e u r a
> l   b a s i s   o f  m u l t i s e n s o r y   s p a t i a l  r e p r e s e
> n t a t i o n s .   N a t   R e v  N e u r o s c i .   3 :   7 4 1 - 7 4 7 .
>
> H a m k e r ,   F . H . ,   Z i r n s a k ,  M . ,   C a l o w ,   D . ,
> L a p p e ,   M .  ( 2 0 0 8 ) Ý T h e   p e r i - s a c c a d i c  p e r c
> e p t i o n   o f   o b j e c t s  a n d   s p a c e . Ý P L O S  C o m p u
> t a t i o n a l   B i o l o g y  4 ( 2 ) : e 2 1
> O l s h a u s e n   B A ,   F i e l d   D J .  1 9 9 6 .   E m e r g e n c
> e   o f  s i m p l e - c e l l   r e c e p t i v e  f i e l d   p r o p e r
> t i e s   b y  l e a r n i n g   a   s p a r s e   c o d e  f o r   n a t u
> r a l   i m a g e s .  N a t u r e   3 8 1 : 6 0 7 - 9 .
> - -
> I   w a s   r e a l l y   i n f l u e n c e d   b y
> @ a r t i c l e { A t i c k 9 2 ,         A u t h o r   =   { A t i c k ,
> J o s e p h   J . } ,         J o u r n a l   =   { N e t w o r k :  { C } o
> m p u t a t i o n   i n  { N } e u r a l   { S } y s t e m s } ,         N u
> m b e r   =   { 2 } ,         P a g e s   =   { 2 1 3 - - 5 2 } ,         T
> i t l e   =   { C o u l d  { I } n f o r m a t i o n   { T } h e o r y  { P
> } r o v i d e   a n  { E } c o l o g i c a l   { T } h e o r y   o f  { S }
> e n s o r y  { P } r o c e s s i n g ? } ,         V o l u m e   =   { 3 } ,
>         Y e a r   =   { 1 9 9 2 } }
> w h i c h   i s   a   r e v i e w   p a p e r  r a t h e r   r e l a t e d
>   t o   t h e  s e m i n a l   p a p e r s   f r o m  B a r l o w   a n d
> M a r r .
> - -
> W i l s o n   H R ,   C o w a n   J D . E x c i t a t o r y   a n d  i n h
> i b i t o r y   i n t e r a c t i o n s  i n   l o c a l i z e d   p o p u l
> a t i o n s  o f   m o d e l n e u r o n s . B i o p h y s   J .   1 9 7 2
> J a n ; 1 2 ( 1 ) : 1 - 2 4 .
> - -
> W i r i n g   O p t i m i z a t i o n  D m i t r i   B .   C h k l o v s k
> i i T r a u b ' s   C A 1   m o d e l / P i n s k y  R i n z e l   2   c o m
> p a r t m e n t a l  m o d e l s E r i k   D e   S c h u t t e r ' s   P u r
> k i n j e   c e l l   m o d e l s H e n r y   M a r k r a m ' s   C o r t i
> c a l   M o d e l s R o l l s   &   T r e v e s   -   H i p p o c a m p a l
>   N e t w o r k P o l s k y   &   M e l   -   2 l a y e r  p y r a m i d a l
>   c e l l   m o d e l T e r r y   S e j n o w s k i   -   S y n a p s e ,
> m o d e l d b U p i n d e r   S   B h a l l a   -  M i l l i o n   S y n a p
> s e s   /  B i s t a b l e   s y s t e m s
> - -
> T h e s e   p a p e r s   i n t r o d u c e d  a c c u r a t e   m o d e l
> s   o f  c a l c i u m   d y n a m i c s   a n d  n e u r o m o d u l a t o
> r y   e f f e c t s  o n   i o n   c h a n n e l   a c t i v i t y .
> B h a l l a   U S ,   I y e n g a r   R . E m e r g e n t   p r o p e r t i
> e s   o f  n e t w o r k s   o f   b i o l o g i c a l  s i g n a l i n g
> p a t h w a y s . S c i e n c e .   1 9 9 9   J a n   1 5 ; 2 8 3 ( 5 4 0 0
> ) : 3 8 1 - 7 .
> Z a d o r   A ,   K o c h   C ,   B r o w n   T H . B i o p h y s i c a l
> m o d e l   o f   a   H e b b i a n   s y n a p s e . P r o c   N a t l   A
> c a d   S c i   U   S  A .   1 9 9 0  S e p ; 8 7 ( 1 7 ) : 6 7 1 8 - 2 2 .
> H o l m e s   W R ,   L e v y   W B . A b s t r a c t I n s i g h t s   i n
> t o  a s s o c i a t i v e   l o n g - t e r m  p o t e n t i a t i o n   f
> r o m  c o m p u t a t i o n a l   m o d e l s   o f  N M D A   r e c e p t
> o r - m e d i a t e d  c a l c i u m   i n f l u x   a n d  i n t r a c e l
> l u l a r   c a l c i u m  c o n c e n t r a t i o n   c h a n g e s . J   N
> e u r o p h y s i o l .   1 9 9 0   M a y ; 6 3 ( 5 ) : 1 1 4 8 - 6 8 .
> - -
> T h e r e   a r e   t w o  t h e o r e t i c a l   p a p e r s  w h i c h ,
>   i n   m y   o p i n i o n ,  h a v e   h a d   a   s t r o n g  i n f l u
> e n c e   o n   t h e   w a y   w e  t h i n k   a b o u t   s y n a p t i c
>  t r a n s m i s s i o n   a n d   s h o r t  t e r m   p l a s t i c i t y
>   t o d a y :
> A   W   L i l e y   a n d   K   A   N o r t h .  A n   e l e c t r i c a l
>  i n v e s t i g a t i o n   o f   e f f e c t s  o f   r e p e t i t i v e
> s t i m u l a t i o n   o n   m a m m a l i a n  n e u r o m u s c u l a r
> j u n c t i o n .  J   N e u r o p h y s i o l ,  1 6 ( 5 ) : 5 0 9   5 2 7
> ,   S e p   1 9 5 3 .
> W   J   B e t z .   D e p r e s s i o n   o f  t r a n s m i t t e r   r e
> l e a s e   a t  t h e   n e u r o m u s c u l a r  j u n c t i o n   o f
> t h e f r o g .   J   P h y s i o l ,   2 0 6 ( 3 ) : 6 2 9   6 4 4 ,   1 9
> 7 0 .
> T h e s e   w e r e ,   o f   c o u r s e ,  p u b l i s h e d   b e f o r
> e   t h e  t e r m   " c o m p u t a t i o n  n e u r o s c i e n c e "   w
> a s   u s e d .  T h e   f i r s t   p r o p o s e d   a  m a t h e m a t i
> c a l   m o d e l   f o r  v e s i c l e   p o o l   d e p l e t i o n ,  w
> h i c h   i s   s t i l l   i n   u s e  t o d a y .   T h e   s e c o n d
> w a s  t h e   f i r s t   t o   e x t e n d   t h i s  w i t h   t h e   r
> e l e a s e  p r o b a b i l i t y   a s   a   d y n a m i c  v a r i a b l
> e .   T h e s e   i d e a s  w e r e   t h e n   f u r t h e r  p o p u l a
> r i s e d   b y   t h e s e  c l a s s i c   p a p e r s :
> L   F   A b b o t t ,   J   A   V a r e l a ,  K   S e n ,   a n d   S   B
>   N e l s o n .  S y n a p t i c   d e p r e s s i o n   a n d  c o r t i c
> a l g a i n   c o n t r o l .   S c i e n c e ,  2 7 5 ( 5 2 9 7 ) : 2 2 0
> 2 2 4 ,   J a n  1 9 9 7 .
> M   V   T s o d y k s   a n d   H  M a r k r a m .   T h e   n e u r a l
> c o d e  b e t w e e n   n e o c o r t i c a l  p y r a m i d a l n e u r o
> n s   d e p e n d s   o n  n e u r o t r a n s m i t t e r   r e l e a s e
>  p r o b a b i l i t y .   P r o c   N a t l  A c a d   S c i   U   S A ,
> 9 4 ( 2 ) : 7 1 9   7 2 3 ,   J a n   1 9 9 7 .
> W h a t   I   f o u n d   h a v e   d u r i n g  m y   c o l l a b o r a t
> i o n s   w i t h  b i o l o g i s t s   w a s   t h a t   n o t  s o   m u
> c h   t h e   p r e c i s e  m a t h e m a t i c a l  f o r m u l a t i o n
> ,   b u t   t h e  v e r y   b a s i c   i d e a s   a n d  c o n c e p t s
>   e x p l o r e d   i n  t h e s e   p a p e r s   h a v e   m a d e   a  s
> t r o n g   i m p a c t   i n   t h e  w h o l e   f i e l d ,   a n d   h a
> v e  c e r t a i n l y   c l e a r e d   t h e  w a y   f o r   n u m e r o
> u s   f u r t h e r  t h e o r e t i c a l   s t u d i e s .
> A n o t h e r   p a p e r   I   h a v e  c o m e   a c r o s s   j u s t  r
> e c e n t l y   w h i c h   I   w o u l d  c o n s i d e r   a s   r a t h e
> r  i m p o r t a n t   a n d   u s e f u l   i s  t h i s :
> J   J   H o p f i e l d   a n d   A   V   M  H e r z .   R a p i d   L o c
> a l  S y n c h r o n i z a t i o n   o f  A c t i o n   P o t e n t i a l s
> :  T o w a r d   C o m p u t a t i o n   w i t h  C o u p l e d  I n t e g r
> a t e - a n d - F i r e  N e u r o n s .   P r o c   N a t l   A c a d  S c
> i   U   S A ,   9 2 ( 1 5 ) :   6 6 5 5 - 6 6 6 2 ,   J u l   1 9 9 5 .
> C i t e d   m o r e   t h a n   1 5 0  t i m e s ,   i t   c o n t a i n s
>   s o m e  s t r o n g   r e s u l t s   r e g a r d i n g  t h e   b e h a
> v i o u r   o f  r e c u r r e n t   n e t w o r k s ,   a n d  a l s o   a
> n t i c i p a t e s   a  n u m b e r   o f   r e s u l t s   s h o w n  m o
> r e   r e c e n t l y .
>
> - -
> H e r e   i s   m y   t o p   1 2  p a p e r s ,   i n   c h r o n o l o g
> i c a l  o r d e r .   I   h a v e   g o n e   f o r  o n e s   t h a t   m
> a k e   m y  s c i e n c e   h e a r t   s i n g ,   t h a t  i n t r o d u
> c e   a   b i g   i d e a ,  u s e f u l   t o o l ,   c o n n e c t  e x p
> e r i m e n t   a n d   t h e o r y   i n  a   s a t i s f y i n g   w a y ,
>   o r   a r e  a n   e x a m p l e   o f w o r k   o n   a   t o p i c   t h
> a t   h a s  b e e n   m y s t e r i o u s l y  u n d e r - r e p r e s e n
> t e d .
> I   h a v e   t r i e d   t o   b r i e f l y  q u a l i f y   w h y   t h
> e y   c o u l d  b e   t h o u g h t   o f   a s   c l a s s i c  b y   t h
> e   w i d e r   c o m m u n i t y .
> 1 )   W i l l s h a w   a n d   v o n   d e r  M a l s b u r g   ( 1 9 7 9
> ) .         F u t u r e   h o t   t o p i c :  m o d e l l i n g   d e v e l
> o p m e n t           E x c e l l e n t  i n t e r a c t i o n   b e t w e e
> n  t h e o r y   a n d   e x p e r i m e n t   -           p r e d i c t e d
>   e p h r i n s  a n d   e p h   r e c e p t o r s .           h t t p : / /
> w w w . j s t o r . o r g / s t a b l e / p d f p l u s / 2 4 1 8 2 2 6 . p
> d f 2 )   L a u g h l i n   ( 1 9 8 1 )   Z .  N a t u r f o r s c h .   C
> 3 6 : 9 1 0 - 2         B i g   i d e a :   c o d i n g  m a t c h e s   s t
> i m u l u s  s t a t i s t i c s .         h t t p : / / w w w . n c b i . n
> l m . n i h . g o v / s i t e s / e n t r e z ? D b = p u b m e d & T e r m
> T o S e a r c h = 7 3 0 3 8 2 3 3 )   S r i n i v a s a n   e t   a l .  ( 1
> 9 8 2 )   P r o c .   R o y .   S o c .   B  2 1 6 ( 1 2 0 5 ) : 4 2 7 - 5 9
>         E x c e l l e n t  i n t e r a c t i o n   b e t w e e n  t h e o r
> y   a n d   e x p e r i m e n t :         p r e d i c t s   r e s p o n s e
> s  o f   f i r s t   o r d e r   v i s u a l  i n t e r n e u r o n s
>   i f   t h e y   e x p l o i t  s p a t i a l   a n d   t e m p o r a l  c
> o r r e l a t i o n s   t o         r e d u c e   r e d u n d a n c y .
>     h t t p : / / w w w . k y b . t u e b i n g e n . m p g . d e / b e t h
> g e g r o u p / t e a c h i n g / w s 0 7 0 8 _ s e m _ r e t i n a _ w h i
> t e n i n g / S r i n i v a s a n _ e t _ a l _ 1 9 8 2 . p d f
> 4 )   B u c h s b a u m   a n d  G o t t s c h a l k   ( 1 9 8 3 ) .   P r
> o c .  R .   S o c .   B   2 2 0 : 8 9 - 1 1 3         E x c e l l e n t  i
> n t e r a c t i o n   b e t w e e n  t h e o r y   a n d   e x p e r i m e n
> t :         u s e s   P C A   t o  a c c u r a t e l y   c a l c u l a t e
> t h e  c o l o u r   c h a n n e l s         t h a t   m a x i m i s e  i n
> f o r m a t i o n  t r a n s m i s s i o n .   D e s e r v e s  t o   b e
>         m o r e   w i d e l y   k n o w n .           h t t p : / / w w w .
> j s t o r . o r g / s t a b l e / p d f p l u s / 3 5 8 7 3 . p d f
>
> 5 )   B i a l e k   e t   a l .   ( 1 9 9 1 )   S c i e n c e         U s e
> f u l   a p p l i c a t i o n  f o r   t h e o r i s t :   n e a t  m e t h
> o d   f o r         c a l c u l a t i n g   s t i m u l u s  f i l t e r s
> i n   t h e   r e s p o n s e .           h t t p : / / w w w 2 . h a w a i
> i . e d u / ~ s s t i l l / n e u r a l _ c o d e _ 9 1 . p d f
>
> 6 )   T r e v e s   a n d   R o l l s  ( 1 9 9 2 )   H i p p o c a m p u s
>  2 ( 2 ) : 1 8 9 - 9 9         E x c e l l e n t  i n t e r a c t i o n   b
> e t w e e n  t h e o r y   a n d   e x p e r i m e n t :         i d e n t i
> f i e d   t h e  f u n c t i o n   o f   t h e   d e n t a t e  g y r u s
> i n   t h e         h i p p o c a m p u s ,   a n d  m a t c h e d   n e t w
> o r k  o r g a n i s a t i o n   t o         f u n c t i o n   f a r   m o r
> e  s u c c e s s f u l l y   t h a t   M a r r .         h t t p : / / w w w
> 3 . i n t e r s c i e n c e . w i l e y . c o m / c g i - b i n / f u l l t
> e x t / 1 0 9 7 1 1 3 3 3 / P D F S T A R T 7 )   V a n   H a t e r e n   (
> 1 9 9 2 )   J .  C o m p   P h y s .   A   1 7 1 : 1 5 7 - 1 7 0         E x
> c e l l e n t  i n t e r a c t i o n   b e t w e e n  t h e o r y   a n d
> e x p e r i m e n t :         p r e d i c t s   v i s u a l  s p a t i o t e
> m p o r a l   r e c e p t i v e  f i e l d s   o f   c e l l s         c o n
> n e c t e d   t o  p h o t o r e c e p t o r s   i n   t h e  f l y   s o
> a s   t o   m a x i m i s e           i n f o r m a t i o n   a b o u t  n a
> t u r a l   i m a g e s   f r o m  f i r s t   p r i n c i p l e s ,
>   w i t h   s t u n n i n g   s u c c e s s .         h t t p : / / w w w .
> s p r i n g e r l i n k . c o m / c o n t e n t / h 4 6 8 1 x 3 4 4 j 3 7 8
> 2 2 9 / f u l l t e x t . p d f
>
> 8 )   W o l p e r t   e t   a l .   ( 1 9 9 5 )  S c i e n c e   2 6 9 ( 5
> 2 3 2 ) : 1 8 8 0 - 2         B i g   i d e a :   i n t e r n a l  m o d e l
> s   a n d   t h e   u s e   o f  p r i o r s .           h t t p : / / k e c
> k . u c s f . e d u / ~ h o u d e / s e n s o r i m o t o r _ j c / D M W o
> l p e r t 9 5 a . p d f
>
> 9 )   Z e m e l   e t   a l .   ( 1 9 9 8 )  N e u r .   C o m p .   1 0 (
> 2 ) : 4 0 3 - 3 0         B i g   i d e a :   n e u r o n s  e n c o d e   d
> i s t r i b u t i o n s ,  n o t   s i n g l e   v a l u e s           h t t
> p : / / w w w . g a t s b y . u c l . a c . u k / ~ d a y a n / p a p e r s
> / z d p 9 8 . p d f
>
> 1 0 )   V a n   R o s s u m   e t   a l .  ( 2 0 0 0 )   J .   N e u r o .
>  2 0 ( 2 3 ) : 8 8 1 2 - 2 1         E x c e l l e n t  i n t e r a c t i o
> n   b e t w e e n  t h e o r y   a n d   e x p e r i m e n t :         S i m
> p l e   a p p l i c a t i o n  o f   F o k k e r - P l a n c k  e q u a t i
> o n   p h y s i c s   t o         e x p l a i n   f u n c t i o n a l  c o n
> s e q u e n c e s   t o   t h e  n e t w o r k   o f           c e l l u l a
> r   l e v e l   e x p e r i m e n t a l   d a t a .           h t t p : / /
> w w w . j n e u r o s c i . o r g / c g i / r e p r i n t / 2 0 / 2 3 / 8 8
> 1 2 . p d f
>
> 1 1 )   B r u n e l   ( 2 0 0 0 )   J .  C o m p .   N e u r o   8 : 1 8 3
> - 2 0 8         U s e f u l   a p p l i c a t i o n  f o r   t h e o r i s t
> :  c a l c u l a t i o n s   o f   t h e           p o p u l a t i o n   a c
> t i v i t y  o f   a   n e t w o r k   o f  i n t e g r a t e - a n d - f i
> r e  n e u r o n s .           h t t p : / / w w w . s p r i n g e r l i n k
> . c o m / c o n t e n t / u 4 4 6 l 5 7 2 2 l p 0 3 6 7 7 / f u l l t e x t
> . p d f
>
> 1 2 )   S c h r e i b e r   ( 2 0 0 0 )  P h y s i c a l   R e v i e w   L
> e t t e r s  8 5 ( 2 ) : 4 6 1 - 6 4         F u t u r e   h o t   t o p i c
> :  C u r r e n t   b e s t   m e t h o d   t o  i n f e r   c a u s a l
>       r e l a t i o n s h i p s  b e t w e e n   n e u r o n s   u s i n g
>  i n f o r m a t i o n   t h e o r y .           h t t p : / / p r o l a . a
> p s . o r g / p d f / P R L / v 8 5 / i 2 / p 4 6 1 _ 1
> - -
> H e r e   a r e   t h e   m o s t  i m p o r t a n t   p a p e r s   i n
> 3    s u b j e c t s ,   p l a s t i c i t y   a n d s i m p l e   n e u r o
> n   m o d e l s   a n d  n e t w o r k   d y n a m i c s O f   c o u r s e ,
>   t h e r e   a r e  o t h e r   c a t e g o r i e s   i n  C o m p u t a t
> i o n a l  n e u r o s c i e n c e ( d e t a i l e d   n e u r o n   m o d e
> l ,  c o r t e x   m o d e l i n g ,   v i s i o n ,  a u d i t i o n   e t
> c )   o n   w h i c h  o t h e r s   w i l l   r e p o r t .
> 1 )   I n   p l a s t i c i t y :
> H e b b ,   1 9 4 9   ( b o o k )
> B i e n e n s t o c k ,   C o o p e r  M u n r o ,   J .   N e u r o s c i
> . 1 9 8 2  ( B C M   r u l e )
> K o h o n e n   N e u r a l  N e t w o r k s 1 9 9 3   ( K o h o n e n  a l
> g o   i n   c o m p   n e u r o  p e r s p e c t i v e
>                                                                            o
> t h e r   p a p e r s   o f   h i m  w o u l d   a l s o   d o ) H o p f i e
> l d ,   P N A S ,   1 9 8 2   ( H o p f i e l d   m o d e l )
> A m i t   G u t f r e u n d  S o m p o l i n k s y ,   P h y s   R e v   A
> ,  1 9 8 5   ( A n a l y s i s   o f  H o p f i e l d   m o d e l )
> L i n s k e r   P N A S ,   1 9 8 6   ( e m e r g e n c e   o f   f i e l d
> )
> M a c K a y   a n d   M i l l e r   1 9 9 0  N e u r a l   C o m p u t .
> ( a n a l y s i s  o f   L i n s k e r s   r u l e )
> M i l l e r   a n d   M a c K a y   1 9 9 4  N e u r a l   C o m p u t .
> t h e   r o l e  o f   c o n s t r a i n t s
> G e r s t n e r   e t   a l ,   N a t u r e  1 9 9 6   ( f i r s t   p a p
> e r   o n  S T D P )
> K e m p t e r   e t   a l .   P h y s   R e v  E ,   1 9 9 9   ( f i r s t
>   a n a l y s i s  o f   S T D P )
> L i s m a n ,   P N A S ,   1 9 9 9  ( f i r s t   m o d e l   o f  p l a s
> t i c i t y   b a s e d   o n  c a l c i u m   d y n a m i c s )
> S o n g   M i l l e r   A b b o t t ,   N a t .  N e u r o s c i ,   2 0 0
> 0   ( p o p u l a r  p a p e r   o n   S T D P )
> R o s s u m   e t   a l .   2 0 0 0 ,   J .  N e u r o s c i e   ( S T D P
>   w i t h  s o f t   b o u n d s   f o r   t h e  w e i g h t s )
> F u s i ,   B i o l o g i c a l  C y b e r n e t i c s ,   2 0 0 2   ( s o
> m e  g e n e r a l   p r o b l e m s   o f  H e b b i a n   r u l e s   -
> n i c e  r e v i e w   o f   w o r k   o f   F u s i ) _
> S h o u v a l   e t   a l . ,   P N A S ,  2 0 0 2   ( c a l c i u m   m o
> d e l   o f  p l a s t i c i t y )
> S e n n   T s o d y k s ,   M a r k r a m ,  N e u r a l .   c o m p .   2
> 0 0 1   ( S T D P  a l g o r i t h m )
> F u s i ,   D r e w ,   A b b o t t   N a t .  N e u r o s c i e n c e   2
> 0 0 5  ( C a s c a d e   m o d e l )
> T o y o i z u m i   e t   a l .   P N A S  2 0 0 5   ( B C M   r u l e   f
> o r  s p i k i n g   n e u r o n   a l s o  o p t i m i z e d   i n f o r m
> a t i o n )
>
> 2 )   I n   s i m p l i f i e d   n e u r o n   m o d e l s
> L a p i c q u e   0 7   ( o f t e n   c i t e d  a s   f i r s t  i n t e g
> r a t e - a n d - f i r e  m o d e l ,   e v e n   t h o u g h   i t  d o e
> s   n o t   s h o w   r e s e t )
> F i t z H u g h   1 9 6 1 ,   B i o p h y s .  J o u r n a l   ( 2 - d i m
>   n e u r o n  m o d e l )
> S t e i n   1 9 6 7 ,   B i o p h y s .  J o u r n a l     ( s o m e   m o
> d e l s   o f  n e u r a l   v a r i a b i l i t y   -  i n t e g r a t e -
> a n d - f i r e   m o d e l  w i t h   n o i s e )
> E r m e n t r o u t   1 9 9 6 ,   N e u r a l  C o m p u t . ,   C a n o n
> i c a l   t y p e  I   m o d e l ,   q u a d r a t i c  i n t e g r a t e -
> a n d - f i r e
> K i s t l e r   e t   a l .   1 9 9 7 ,  N e u r a l   C o m p u t a t i o
> n  ( s y s t e m a t i c   r e d u c t i o n   t o  a   t h r e s h o l d
> m o d e l / S p i k e  R e s p o n s e   M o d e l )
> L a t h a m   2 0 0 0 ,   J .  N e u r o p h y s .   q u a d r a t i c  i n
> t e g r a t e - a n d - f i r e
> I z h i k e v i c h   2 0 0 3 ,   I E E E ,  2 - d i m .   n e u r o n   m
> o d e l
> F o u r c a u d   e t   a l .   2 0 0 3 ,   J .  N e u r o s c i .   e x p
> .  i n t e g r a t e - a n d - f i r e   m o d e l
> J o l i v e t   e t   a l .   2 0 0 6 ,   J .  c o m p u t .   N e u r o s
> c i .   - -  s p i k i n g   i n   r e a l   n e u r o n s  c a n   b e   e
> x p l a i n e d   b y  t h r e s h o l d   m o d e l s
> B a d e l   e t   a l .   2 0 0 8 ,   J .  N e u r o p h y s i o l .   - -
>   r e a l  n e u r o n s   a r e   e x p o n e n t i a l  i n t e g r a t e
> - a n d - f i r e  m o d e l s ,   t h i s   i s   a   v e r y  r e c e n t
>   p a p e r ,
>                                                  b u t   i t   i s   r e a l
> l y  i m p o r t a n t   f o r   t h e  d i s c u s s i o n   o f   s i m p
> l e  n e u r o n   m o d e l s
>
> 3 )   N e t w o r k   d y n a m i c s
> W i l s o n   a n d   C o w a n ,   1 9 7 2
> A m a r i   1 9 7 4
> B r u n e l   a n d   H a k i m ,   1 9 9 9  N e u r a l   C o m p u t a t
> i o n
> G e r s t n e r   2 0 0 0   N e u r a l   C o m p u t a t i o n
> B r u n e l   2 0 0 0   C o m p u t .   N e u r o s c i
>
> - -
> F i n a l l y ,   I   a m   a t t a c h i n g  a   l i s t   o f   g r e a
> t   p a p e r s .    I f   I   w e r e   t r y i n g   t o   g e t  o u t s
> i d e r s   e x c i t e d ,   I ' d  d e f i n i t e l y   u s e   t h e   A
> n d y  S c h w a r t z   p a p e r   o n   n e u r a l  p r o s t h e t i c
> s .     A l s o   t h i n k  I   w o u l d   d o   O l s h a u s e n   &  F
> i e l d   a s   i t   r e a l l y  k i c k e d   p e o p l e   o f f   o n
>  t h i n k i n g   a b o u t   n a t u r a l  i m a g e s .     T h e   H o
> p f i e l d  p a p e r   i s   t h e   g r e a t e s t   o f  t h e   b u n
> c h   b u t   i s   l i k e l y  t o o   o l d   f o r   w h a t   y o u ' r
> e  l o o k i n g   f o r .    S p i k e - t i m i n g - d e p e n d e n t  p
> l a s t i c i t y   i s   a   h o t  t o p i c   a n d   I   t h i n k  c a
> r r i e s   o n   a   g r e a t  t r a d i t i o n   o f  c o m p u t a t i
> o n a l  n e u r o s c i e n t i s t s  c o n n e c t i n g   c e l l u l a
> r  p l a s t i c i t y   t o   l a r g e r  n e t w o r k   f u n c t i o n
> s ;   a n d   I  t h i n k   P e t e r   D a y a n   ( a n d  M o n t a g u
> e ' s   i n   t h e  o r i g i n a l   p a p e r )   w o r k   i s  s o m e
>   o f   t h e   f i r s t   t h a t  r e a l l y   p u t s   a   f r a m e w
> o r k  i n   p l a c e   f o r   t h i n k i n g  a b o u t   n e u r o m o
> d u l a t o r s .    B u t   t h e y ' r e   a l l   g r e a t ,  a n d   I
>   t r i e d   t o   h i t   m a n y  d i f f e r e n t   c o n t r i b u t i
> o n s  ( m a y b e   t h i s   i s   t h e  g r e a t e s t   m e s s a g e
> - - t h a t  c o m p u t a t i o n a l  n e u r o s c i e n c e   p e r v a
> d e s   s o  m a n y   f i e l d s   f r o m  s i n g l e - n e u r o n  c o
> m p u t a t i o n   t o  n e u r o m o d u l a t o r s   t o  m o d e l s
> o f   m e m o r y ) .
> 1 .     M o n t a g u e   P R ,   D a y a n  P ,   S e j n o w s k i   T J
>   A  f r a m e w o r k   f o r  m e s e n c e p h a l i c   d o p a m i n e
>  s y s t e m s   b a s e d   o n  p r e d i c t i v e   H e b b i a n  l e a
> r n i n g .     J   N e u r o s c i .  1 9 9 6   M a r  1 ; 1 6 ( 5 ) : 1 9
> 3 6 - 4 7 . A b s t r a c t :   W e   d e v e l o p   a  t h e o r e t i c a
> l   f r a m e w o r k  t h a t   s h o w s   h o w  m e s e n c e p h a l i
> c   d o p a m i n e  s y s t e m s   c o u l d   d i s t r i b u t e  t o
> t h e i r   t a r g e t s   a  s i g n a l   t h a t   r e p r e s e n t s
>  i n f o r m a t i o n   a b o u t   f u t u r e  e x p e c t a t i o n s .
>   I n  p a r t i c u l a r ,   w e   s h o w   h o w  a c t i v i t y   i n
>   t h e   c e r e b r a l  c o r t e x   c a n   m a k e  p r e d i c t i o
> n s   a b o u t   f u t u r e  r e c e i p t   o f   r e w a r d   a n d  h
> o w   f l u c t u a t i o n s   i n   t h e  a c t i v i t y   l e v e l s
> o f  n e u r o n s   i n   d i f f u s e  d o p a m i n e   s y s t e m s
> a b o v e  a n d   b e l o w   b a s e l i n e  l e v e l s   w o u l d   r
> e p r e s e n t  e r r o r s   i n   t h e s e  p r e d i c t i o n s   t h
> a t   a r e  d e l i v e r e d   t o   c o r t i c a l  a n d   s u b c o r
> t i c a l   t a r g e t s .  W e   p r e s e n t   a   m o d e l   f o r  h
> o w   s u c h   e r r o r s   c o u l d   b e  c o n s t r u c t e d   i n
> a   r e a l  b r a i n   t h a t   i s   c o n s i s t e n t  w i t h   p h
> y s i o l o g i c a l  r e s u l t s   f o r   a   s u b s e t   o f  d o p
> a m i n e r g i c   n e u r o n s  l o c a t e d   i n   t h e   v e n t r a
> l  t e g m e n t a l   a r e a   a n d  s u r r o u n d i n g   d o p a m i
> n e r g i c  n e u r o n s .   T h e   t h e o r y   a l s o  m a k e s   t
> e s t a b l e  p r e d i c t i o n s   a b o u t   h u m a n  c h o i c e
> b e h a v i o r   o n   a  s i m p l e   d e c i s i o n - m a k i n g  t a
> s k .   F u r t h e r m o r e ,   w e  s h o w   t h a t ,   t h r o u g h
> a  s i m p l e   i n f l u e n c e   o n  s y n a p t i c   p l a s t i c i
> t y ,  f l u c t u a t i o n s   i n   d o p a m i n e  r e l e a s e   c a
> n   a c t   t o  c h a n g e   t h e   p r e d i c t i o n s  i n   a n   a
> p p r o p r i a t e  m a n n e r . T h i s   p a p e r   i s   t h e   f i r
> s t  o f   a   s e r i e s   o f   p a p e r s  s e t t i n g   u p   a   f
> r a m e w o r k  f o r   h o w   m e s e n c e p h a l i c  d o p a m i n e
>   n e u r o n s  r e p r e s e n t   r e w a r d   a n d   c a n  s e r v e
>   a s   t h e   b a s i s   f o r   t e m p o r a l   d i f f e r e n c e  -
> b a s e d   r e w a r d   l e a r n i n g  i n   w h i c h   t h e   r e w a
> r d   i s  o f f e r e d   a t   a   d e l a y e d  t i m e .
> * 2 .     S t r o n g ,   S . ,  K o b e r l e ,   R . ,   d e   R u y t e
> r  v a n   S t e v e n i n c k ,   R .   a n d  B i a l e k ,   W .   1 9 9
> 8 .   E n t r o p y  a n d   i n f o r m a t i o n   i n  n e u r a l   s p
> i k e   t r a i n s ,  P h y s i c a l   R e v i e w   L e t t e r s  8 0 :
>   1 9 7 - 2 0 0 . A b s t r a c t .   T h e   n e r v o u s  s y s t e m   r
> e p r e s e n t s   t i m e  d e p e n d e n t   s i g n a l s   i n  s e q
> u e n c e s   o f   d i s c r e t e ,  i d e n t i c a l   a c t i o n  p o
> t e n t i a l s   o r   s p i k e s ;  i n f o r m a t i o n   i s   c a r r
> i e d  o n l y   i n   t h e   s p i k e  a r r i v a l   t i m e s .   W e
>   s h o w  h o w   t o   q u a n t i f y   t h i s  i n f o r m a t i o n ,
>   i n   b i t s ,  f r e e   f r o m   a n y  a s s u m p t i o n s   a b o
> u t   w h i c h  f e a t u r e s   o f   t h e   s p i k e  t r a i n   o r
>   i n p u t   s i g n a l  a r e   m o s t   i m p o r t a n t ,   a n d  w
> e   a p p l y   t h i s   a p p r o a c h  t o   t h e   a n a l y s i s   o
> f  e x p e r i m e n t s   o n   a   m o t i o n  s e n s i t i v e   n e u
> r o n   i n   t h e  f l y   v i s u a l   s y s t e m .   T h i s  n e u r
> o n   t r a n s m i t s  i n f o r m a t i o n   a b o u t   t h e  v i s u
> a l   s t i m u l u s   a t   r a t e s  o f   u p   t o   9 0   b i t s / s
> ,  w i t h i n   a   f a c t o r   o f   2   o f  t h e   p h y s i c a l
> l i m i t   s e t  b y   t h e   e n t r o p y   o f   t h e  s p i k e   t
> r a i n   i t s e l f . T h i s   p a p e r   u s h e r e d   i n   a  n e w
>   s e t   o f   t e c h n i q u e s  f o r   c h a r a c t e r i z i n g   s
> p i k e  t r a i n s   u s i n g   t h e   m e t h o d s  o f   i n f o r m
> a t i o n   t h e o r y ,  a n d   a l s o   i l l u s t r a t e d  t h a t
>   t h e r e   w a s  i n f o r m a t i o n   o n   m u c h  s m a l l e r
> t i m e   s c a l e s   ( ~ a  c o u p l e   m s )   t h a n   h a d  t y p
> i c a l l y   b e e n   a s s u m e d  p r e v i o u s l y .
> 3 a .   A b b o t t   L F ,   V a r e l a  J A ,   S e n   K ,   N e l s o
> n   S B .  S y n a p t i c   d e p r e s s i o n   a n d  c o r t i c a l
> g a i n  c o n t r o l . S c i e n c e .   1 9 9 7  J a n   1 0 ; 2 7 5 ( 5
> 2 9 7 ) : 2 2 0 - 4 A b s t r a c t .   C o r t i c a l  n e u r o n s   r e
> c e i v e   s y n a p t i c  i n p u t s   f r o m   t h o u s a n d s   o f
>  a f f e r e n t s   t h a t   f i r e  a c t i o n   p o t e n t i a l s
> a t  r a t e s   r a n g i n g   f r o m   l e s s  t h a n   1   h e r t z
>   t o   m o r e  t h a n   2 0 0   h e r t z .   B o t h   t h e  n u m b e
> r   o f   a f f e r e n t s   a n d  t h e i r   l a r g e   d y n a m i c
>  r a n g e   c a n   m a s k   c h a n g e s  i n   t h e   s p a t i a l
> a n d  t e m p o r a l   p a t t e r n   o f  s y n a p t i c   a c t i v i
> t y ,  l i m i t i n g   t h e   a b i l i t y   o f  a   c o r t i c a l
> n e u r o n   t o  r e s p o n d   t o   i t s   i n p u t s .  M o d e l i
> n g   w o r k   b a s e d   o n  e x p e r i m e n t a l  m e a s u r e m e
> n t s   i n d i c a t e s  t h a t   s h o r t - t e r m  d e p r e s s i o
> n   o f  i n t r a c o r t i c a l   s y n a p s e s  p r o v i d e s   a
> d y n a m i c  g a i n - c o n t r o l   m e c h a n i s m  t h a t   a l l
> o w s   e q u a l  p e r c e n t a g e   r a t e   c h a n g e s  o n   r a
> p i d l y   a n d   s l o w l y  f i r i n g   a f f e r e n t s   t o  p r
> o d u c e   e q u a l  p o s t s y n a p t i c   r e s p o n s e s .  U n l
> i k e   i n h i b i t o r y   a n d  a d a p t i v e   m e c h a n i s m s
> t h a t  r e d u c e   r e s p o n s i v e n e s s   t o  a l l   i n p u t
> s ,   s y n a p t i c  d e p r e s s i o n   i s  i n p u t - s p e c i f i
> c ,   l e a d i n g  t o   a   d r a m a t i c   i n c r e a s e  i n   t h
> e   s e n s i t i v i t y   o f   a  n e u r o n   t o   s u b t l e   c h a
> n g e s  i n   t h e   f i r i n g   p a t t e r n s  o f   i t s   a f f e
> r e n t s .
>
> - A N D -
> 3 b .   M a r k r a m   H ,   T s o d y k s  M .   R e d i s t r i b u t i
> o n   o f  s y n a p t i c   e f f i c a c y  b e t w e e n   n e o c o r t
> i c a l  p y r a m i d a l   n e u r o n s .  N a t u r e .   1 9 9 6   A u
> g  2 9 ; 3 8 2 ( 6 5 9 4 ) : 8 0 7 - 1 0 . A b s t r a c t .  E x p e r i e
> n c e - d e p e n d e n t  p o t e n t i a t i o n   a n d  d e p r e s s i
> o n   o f   s y n a p t i c  s t r e n g t h   h a s   b e e n  p r o p o s
> e d   t o   s u b s e r v e  l e a r n i n g   a n d   m e m o r y   b y  c
> h a n g i n g   t h e   g a i n   o f  s i g n a l s   c o n v e y e d   b e
> t w e e n  n e u r o n s .   H e r e   w e   e x a m i n e  s y n a p t i c
>   p l a s t i c i t y  b e t w e e n   i n d i v i d u a l  n e o c o r t i
> c a l   l a y e r - 5  p y r a m i d a l   n e u r o n s .   W e  s h o w
> t h a t   a n   i n c r e a s e   i n  t h e   s y n a p t i c   r e s p o n
> s e ,  i n d u c e d   b y   p a i r i n g  a c t i o n - p o t e n t i a l
>  a c t i v i t y   i n   p r e -   a n d  p o s t s y n a p t i c   n e u r
> o n s ,  w a s   o n l y   o b s e r v e d   w h e n  s y n a p t i c   i n
> p u t   o c c u r r e d  a t   l o w   f r e q u e n c i e s .   T h i s  f
> r e q u e n c y - d e p e n d e n t  i n c r e a s e   i n   s y n a p t i c
>  r e s p o n s e s   a r i s e s   b e c a u s e  o f   a   r e d i s t r i
> b u t i o n   o f  t h e   a v a i l a b l e   s y n a p t i c  e f f i c a
> c y   a n d   n o t   b e c a u s e  o f   a n   i n c r e a s e   i n   t h
> e  e f f i c a c y .   R e d i s t r i b u t i o n  o f   s y n a p t i c
> e f f i c a c y  c o u l d   r e p r e s e n t   a  m e c h a n i s m   t o
>   c h a n g e   t h e  c o n t e n t ,   r a t h e r   t h a n   t h e  g a
> i n ,   o f   s i g n a l s  c o n v e y e d   b e t w e e n  n e u r o n s
> . T h e s e   2   p a p e r s   c o n n e c t e d  s h o r t - t e r m   s y
> n a p t i c  p l a s t i c i t y   t o   i m p o r t a n t  c o m p u t a t
> i o n a l  i m p l i c a t i o n s .
> 4 a .   H o p f i e l d   J J .   N e u r a l  n e t w o r k s   a n d   p
> h y s i c a l  s y s t e m s   w i t h   e m e r g e n t  c o l l e c t i v
> e   c o m p u t a t i o n a l  a b i l i t i e s .   P r o c   N a t l  A c
> a d   S c i   U   S   A .   1 9 8 2  A p r ; 7 9 ( 8 ) : 2 5 5 4 - 8 A b s t
> r a c t .   C o m p u t a t i o n a l  p r o p e r t i e s   o f   u s e   o
> f  b i o l o g i c a l   o r g a n i s m s   o r  t o   t h e   c o n s t r
> u c t i o n   o f  c o m p u t e r s   c a n   e m e r g e   a s  c o l l e
> c t i v e   p r o p e r t i e s   o f  s y s t e m s   h a v i n g   a   l a
> r g e  n u m b e r   o f   s i m p l e  e q u i v a l e n t   c o m p o n e
> n t s  ( o r   n e u r o n s ) .   T h e  p h y s i c a l   m e a n i n g
> o f  c o n t e n t - a d d r e s s a b l e  m e m o r y   i s   d e s c r i
> b e d   b y  a n   a p p r o p r i a t e   p h a s e  s p a c e   f l o w
> o f   t h e   s t a t e  o f   a   s y s t e m .   A   m o d e l   o f  s u
> c h   a   s y s t e m   i s   g i v e n ,  b a s e d   o n   a s p e c t s
> o f  n e u r o b i o l o g y   b u t   r e a d i l y  a d a p t e d   t o
> i n t e g r a t e d  c i r c u i t s .   T h e   c o l l e c t i v e  p r o
> p e r t i e s   o f   t h i s   m o d e l  p r o d u c e   a  c o n t e n t
> - a d d r e s s a b l e  m e m o r y   w h i c h   c o r r e c t l y  y i e
> l d s   a n   e n t i r e   m e m o r y  f r o m   a n y   s u b p a r t   o
> f  s u f f i c i e n t   s i z e .   T h e  a l g o r i t h m   f o r   t h
> e   t i m e  e v o l u t i o n   o f   t h e   s t a t e  o f   t h e   s y
> s t e m   i s   b a s e d  o n   a s y n c h r o n o u s   p a r a l l e l
>  p r o c e s s i n g .   A d d i t i o n a l  e m e r g e n t   c o l l e c
> t i v e  p r o p e r t i e s   i n c l u d e   s o m e  c a p a c i t y   f
> o r  g e n e r a l i z a t i o n ,  f a m i l i a r i t y   r e c o g n i t
> i o n ,  c a t e g o r i z a t i o n ,   e r r o r  c o r r e c t i o n ,
> a n d   t i m e  s e q u e n c e   r e t e n t i o n .   T h e  c o l l e c
> t i v e   p r o p e r t i e s  a r e   o n l y   w e a k l y  s e n s i t i
> v e   t o   d e t a i l s   o f  t h e   m o d e l i n g   o r   t h e  f a
> i l u r e   o f   i n d i v i d u a l  d e v i c e s . T h i s   c l a s s i
> c   p a p e r  i l l u s t r a t e d   t h e   i d e a   o f  a t t r a c t
> o r   m o d e l s   a n d   a  c o r r e s p o n d e n c e   w i t h  e n e
> r g y   s u r f a c e s .     I t   i s  n o w   u n i v e r s a l l y  p e
> r m e a t e s   d i s c u s s i o n s   o f  l o n g - t e r m   m e m o r y
>   s t o r a g e  i n   n e t w o r k s ,   e s p e c i a l l y  i n   t h e
>   h i p p o c a m p u s .     I t  w a s   f o l l o w e d   m o r e  r e c
> e n t l y   b y   t h e   a r t i c l e  b e l o w ,   w h i c h   e x p a n
> d e d  t h e   i d e a   o f   a t t r a c t o r  m o d e l s   t o   c o n
> t i n u o u s  a t t r a c t o r s   t h i s   n o w   i s  t h e   f r a m
> e w o r k   f o r  d i s c u s s i o n   o f   m a n y  n e t w o r k s   s
> t o r i n g  s h o r t - t e r m   m e m o r i e s   ( t h e  o t h e r   s
> e t   o f   m o d e l s  b e i n g   t h e   s o - c a l l e d   r i n g   m
> o d e l s     b u t   i   a m  n o t   s u r e   o f   t h e   o r i g i n a
> l  r e f e r e n c e   f o r   t h o s e ) .
> 4 b .   S e u n g   H S .   H o w   t h e  b r a i n   k e e p s   t h e
> e y e s  s t i l l .   P r o c   N a t l   A c a d  S c i   U   S   A .   1
> 9 9 6   N o v  1 2 ; 9 3 ( 2 3 ) : 1 3 3 3 9 - 4 4 . A b s t r a c t .   T h
> e   b r a i n   c a n  h o l d   t h e   e y e s   s t i l l  b e c a u s e
>   i t   s t o r e s   a  m e m o r y   o f   e y e   p o s i t i o n .  T h
> e   b r a i n ' s   m e m o r y   o f  h o r i z o n t a l   e y e   p o s i
> t i o n  a p p e a r s   t o   b e  r e p r e s e n t e d   b y  p e r s i s
> t e n t   n e u r a l  a c t i v i t y   i n   a   n e t w o r k  k n o w n
>   a s   t h e   n e u r a l  i n t e g r a t o r ,   w h i c h   i s  l o c
> a l i z e d   i n   t h e  b r a i n s t e m   a n d  c e r e b e l l u m .
>   E x i s t i n g  e x p e r i m e n t a l   d a t a   a r e  r e i n t e r
> p r e t e d   a s  e v i d e n c e   f o r   a n  " a t t r a c t o r   h y
> p o t h e s i s "  t h a t   t h e   p e r s i s t e n t  p a t t e r n s
> o f   a c t i v i t y  o b s e r v e d   i n   t h i s   n e t w o r k  f o
> r m   a n   a t t r a c t i v e   l i n e  o f   f i x e d   p o i n t s   i
> n   i t s  s t a t e   s p a c e .   L i n e  a t t r a c t o r   d y n a m
> i c s   c a n  b e   p r o d u c e d   i n   l i n e a r   o r  n o n l i n
> e a r   n e u r a l  n e t w o r k s   b y   l e a r n i n g  m e c h a n i
> s m s   t h a t  p r e c i s e l y   t u n e   p o s i t i v e  f e e d b a
> c k .
> 5 a .   S o n g   S ,   M i l l e r   K D ,  A b b o t t   L F .   C o m p
> e t i t i v e  H e b b i a n   l e a r n i n g   t h r o u g h  s p i k e -
> t i m i n g - d e p e n d e n t  s y n a p t i c   p l a s t i c i t y .   N
> a t  N e u r o s c i .   2 0 0 0  S e p ; 3 ( 9 ) : 9 1 9 - 2 6 . A b s t r
> a c t .   H e b b i a n   m o d e l s  o f   d e v e l o p m e n t   a n d
>  l e a r n i n g   r e q u i r e   b o t h  a c t i v i t y - d e p e n d e
> n t  s y n a p t i c   p l a s t i c i t y   a n d  a   m e c h a n i s m
> t h a t   i n d u c e s  c o m p e t i t i o n   b e t w e e n  d i f f e r
> e n t   s y n a p s e s .   O n e  f o r m   o f   e x p e r i m e n t a l l
> y  o b s e r v e d   l o n g - t e r m  s y n a p t i c   p l a s t i c i t
> y ,  w h i c h   w e   c a l l  s p i k e - t i m i n g - d e p e n d e n t
>  p l a s t i c i t y   ( S T D P ) ,  d e p e n d s   o n   t h e   r e l a
> t i v e  t i m i n g   o f   p r e -   a n d  p o s t s y n a p t i c   a c
> t i o n  p o t e n t i a l s .   I n   m o d e l i n g  s t u d i e s ,   w
> e   f i n d   t h a t  t h i s   f o r m   o f   s y n a p t i c  m o d i f
> i c a t i o n   c a n  a u t o m a t i c a l l y   b a l a n c e  s y n a p
> t i c   s t r e n g t h s   t o  m a k e   p o s t s y n a p t i c   f i r i
> n g  i r r e g u l a r   b u t   m o r e  s e n s i t i v e   t o   p r e s
> y n a p t i c  s p i k e   t i m i n g .   I t   h a s  b e e n   a r g u e
> d   t h a t   n e u r o n s  i n   v i v o   o p e r a t e   i n   s u c h
>  a   b a l a n c e d   r e g i m e .  S y n a p s e s   m o d i f i a b l e
>   b y  S T D P   c o m p e t e   f o r   c o n t r o l  o f   t h e   t i m
> i n g   o f  p o s t s y n a p t i c   a c t i o n  p o t e n t i a l s .
> I n p u t s   t h a t  f i r e   t h e   p o s t s y n a p t i c  n e u r o
> n   w i t h   s h o r t  l a t e n c y   o r   t h a t   a c t   i n  c o r
> r e l a t e d   g r o u p s   a r e  a b l e   t o   c o m p e t e   m o s t
>  s u c c e s s f u l l y   a n d   d e v e l o p  s t r o n g   s y n a p s
> e s ,   w h i l e  s y n a p s e s   o f  l o n g e r - l a t e n c y   o r
>  l e s s - e f f e c t i v e   i n p u t s  a r e   w e a k e n e d .
> - A N D -
>
> 5 b .   S o n g   S ,   A b b o t t  L F . N e u r o n .   C o r t i c a l
>  d e v e l o p m e n t   a n d  r e m a p p i n g   t h r o u g h   s p i k
> e  t i m i n g - d e p e n d e n t  p l a s t i c i t y .   2 0 0 1   O c t
>  2 5 ; 3 2 ( 2 ) : 3 3 9 - 5 0 A b s t r a c t .   L o n g - t e r m  m o d
> i f i c a t i o n   o f   s y n a p t i c  e f f i c a c y   c a n   d e p e
> n d   o n  t h e   t i m i n g   o f   p r e -   a n d  p o s t s y n a p t
> i c   a c t i o n  p o t e n t i a l s .   I n   m o d e l  s t u d i e s ,
>   s u c h   s p i k e  t i m i n g - d e p e n d e n t  p l a s t i c i t y
>   ( S T D P )  i n t r o d u c e s   t h e   d e s i r a b l e  f e a t u r
> e s   o f   c o m p e t i t i o n  a m o n g   s y n a p s e s   a n d  r e
> g u l a t i o n   o f  p o s t s y n a p t i c   f i r i n g  c h a r a c t
> e r i s t i c s .   S T D P  s t r e n g t h e n s   s y n a p s e s  t h a
> t   r e c e i v e   c o r r e l a t e d  i n p u t ,   w h i c h   c a n   l
> e a d   t o  t h e   f o r m a t i o n   o f  s t i m u l u s - s e l e c t
> i v e  c o l u m n s   a n d   t h e  d e v e l o p m e n t ,   r e f i n e
> m e n t ,  a n d   m a i n t e n a n c e   o f  s e l e c t i v i t y   m a
> p s   i n  n e t w o r k   m o d e l s .   T h e  t e m p o r a l   a s y m
> m e t r y   o f  S T D P   s u p p r e s s e s   s t r o n g  d e s t a b i
> l i z i n g  s e l f - e x c i t a t o r y   l o o p s  a n d   a l l o w s
>   a   g r o u p   o f  n e u r o n s   t h a t   b e c o m e  s e l e c t i
> v e   e a r l y   i n  d e v e l o p m e n t   t o   d i r e c t  o t h e r
>   n e u r o n s   t o   b e c o m e  s i m i l a r l y   s e l e c t i v e .
>  S T D P ,   a c t i n g   a l o n e  w i t h o u t   f u r t h e r  h y p o
> t h e t i c a l   g l o b a l  c o n s t r a i n t s   o r  a d d i t i o n
> a l   f o r m s   o f  p l a s t i c i t y ,   c a n   a l s o  r e p r o d
> u c e   t h e   r e m a p p i n g  s e e n   i n   a d u l t   c o r t e x
>  f o l l o w i n g   a f f e r e n t  l e s i o n s . T h e   p a p e r s
> a b o v e     h a v e  b e e n   s e m i n a l   i n  i l l u s t r a t i n
> g   t h e  i m p l i c a t i o n s   f o r  l e a r n i n g   o f  s p i k e
> - t i m i n g - d e p e n d e n t  s y n a p t i c   p l a s t i c i t y
> 6 .   P o l s k y   A ,   M e l   B W ,  S c h i l l e r   J .   N a t  N e
> u r o s c i .   2 0 0 4  J u n ; 7 ( 6 ) : 6 2 1 - 7 .   E p u b  2 0 0 4
> M a y   2 3 . C o m p u t a t i o n a l   s u b u n i t s  i n   t h i n   d
> e n d r i t e s   o f  p y r a m i d a l   c e l l s . A b s t r a c t .   T
> h e   t h i n   b a s a l  a n d   o b l i q u e   d e n d r i t e s   o f
>  c o r t i c a l   p y r a m i d a l  n e u r o n s   r e c e i v e   m o s
> t   o f  t h e   s y n a p t i c   i n p u t s   f r o m  o t h e r   c e l
> l s ,   b u t   t h e i r  i n t e g r a t i v e   p r o p e r t i e s  r e
> m a i n   u n c e r t a i n .  P r e v i o u s   s t u d i e s   h a v e  m
> o s t   o f t e n   r e p o r t e d  g l o b a l   l i n e a r   o r  s u b
> l i n e a r   s u m m a t i o n .   A n  a l t e r n a t i v e   v i e w ,
>  s u p p o r t e d   b y   b i o p h y s i c a l  m o d e l i n g   s t u d
> i e s ,   h o l d s  t h a t   t h i n   d e n d r i t e s  p r o v i d e
> a   l a y e r   o f  i n d e p e n d e n t  c o m p u t a t i o n a l   ' s
> u b u n i t s '  t h a t   s i g m o i d a l l y  m o d u l a t e   t h e i
> r   i n p u t s  p r i o r   t o   g l o b a l  s u m m a t i o n .   T o  d
> i s t i n g u i s h   t h e s e  p o s s i b i l i t i e s ,   w e  c o m b
> i n e d   c o n f o c a l  i m a g i n g   a n d   d u a l - s i t e  f o c
> a l   s y n a p t i c  s t i m u l a t i o n   o f  i d e n t i f i e d   t
> h i n  d e n d r i t e s   i n   r a t  n e o c o r t i c a l   p y r a m i
> d a l  n e u r o n s .   W e   f o u n d   t h a t  n e a r b y   i n p u t
> s   o n   t h e  s a m e   b r a n c h   s u m m e d  s i g m o i d a l l y
> ,   w h e r e a s  w i d e l y   s e p a r a t e d   i n p u t s  o r   i n
> p u t s   t o   d i f f e r e n t  b r a n c h e s   s u m m e d  l i n e a
> r l y .   T h i s   s t r o n g  s p a t i a l  c o m p a r t m e n t a l i
> z a t i o n  e f f e c t   i s   i n c o m p a t i b l e  w i t h   a   g l
> o b a l   s u m m a t i o n  r u l e   a n d   p r o v i d e s   t h e  f i
> r s t   e x p e r i m e n t a l  s u p p o r t   f o r   a   t w o - l a y e
> r  ' n e u r a l   n e t w o r k '   m o d e l  o f   p y r a m i d a l   n
> e u r o n  t h i n - b r a n c h   i n t e g r a t i o n .  O u r   f i n d
> i n g s   c o u l d   h a v e  i m p o r t a n t   i m p l i c a t i o n s
>  f o r   t h e   c o m p u t i n g   a n d  m e m o r y - r e l a t e d   f
> u n c t i o n s  o f   c o r t i c a l   t i s s u e . T h i s   p a p e r ,
>   a s   w e l l   a s  p r e v i o u s   t h e o r e t i c a l  w o r k ,
> s u g g e s t s   t h a t  d e n d r i t e s   m i g h t   e n a b l e  s i
> n g l e   n e u r o n s   t o   b e h a v e  a s   f e e d f o r w a r d   n
> e u r a l  n e t w o r k s .
> 7 .   M e d i n a   J F ,   N o r e s   W L ,  M a u k   M D .   N a t u r
> e .   2 0 0 2  M a r   2 1 ; 4 1 6 ( 6 8 7 8 ) : 3 3 0 - 3 . I n h i b i t i
> o n   o f   c l i m b i n g  f i b r e s   i s   a   s i g n a l   f o r  t
> h e   e x t i n c t i o n   o f  c o n d i t i o n e d   e y e l i d  r e s
> p o n s e s . A b s t r a c t .   A   f u n d a m e n t a l  t e n e t   o f
>   c e r e b e l l a r  l e a r n i n g   t h e o r i e s  a s s e r t s   t
> h a t   c l i m b i n g  f i b r e   a f f e r e n t s   f r o m   t h e  i
> n f e r i o r   o l i v e   p r o v i d e   a  t e a c h i n g   s i g n a l
>   t h a t  p r o m o t e s   t h e   g r a d u a l  a d a p t a t i o n   o
> f   m o v e m e n t s .  D a t a   f r o m   s e v e r a l   f o r m s  o f
>   m o t o r   l e a r n i n g  p r o v i d e   s u p p o r t   f o r   t h i
> s  t e n e t .   I n   p a v l o v i a n  e y e l i d   c o n d i t i o n i
> n g ,   f o r  e x a m p l e ,   w h e r e   a   t o n e   i s  r e p e a t
> e d l y   p a i r e d   w i t h   a  r e i n f o r c i n g  u n c o n d i t
> i o n e d   s t i m u l u s  l i k e   p e r i o r b i t a l  s t i m u l a
> t i o n ,   t h e  u n c o n d i t i o n e d   s t i m u l u s  p r o m o t
> e s   a c q u i s i t i o n   o f  c o n d i t i o n e d   e y e l i d  r e
> s p o n s e s   b y   a c t i v a t i n g  c l i m b i n g   f i b r e s .
>  C l i m b i n g   f i b r e   a c t i v i t y  e l i c i t e d   b y   a n
>  u n c o n d i t i o n e d   s t i m u l u s  i s   i n h i b i t e d   d u
> r i n g   t h e  e x p r e s s i o n   o f  c o n d i t i o n e d  r e s p o
> n s e s - c o n s i s t e n t  w i t h   t h e   i n h i b i t o r y  p r o
> j e c t i o n   f r o m   t h e  c e r e b e l l u m   t o   i n f e r i o r
>  o l i v e .   H e r e ,   w e   s h o w  t h a t   i n h i b i t i o n   o
> f  c l i m b i n g   f i b r e s   s e r v e s  a s   a   t e a c h i n g
> s i g n a l   f o r  e x t i n c t i o n ,   w h e r e  l e a r n i n g   n
> o t   t o   r e s p o n d  i s   s i g n a l l e d   b y  p r e s e n t i n
> g   a   t o n e  w i t h o u t   t h e  u n c o n d i t i o n e d   s t i m
> u l u s .  W e   u s e d   r e v e r s i b l e  i n f u s i o n   o f   s y
> n a p t i c  r e c e p t o r   a n t a g o n i s t s   t o  s h o w   t h a
> t   b l o c k i n g  i n h i b i t o r y   i n p u t   t o   t h e  c l i m
> b i n g   f i b r e s   p r e v e n t s  e x t i n c t i o n   o f   t h e
>  c o n d i t i o n e d   r e s p o n s e ,  w h e r e a s   b l o c k i n g
>  e x c i t a t o r y   i n p u t   i n d u c e s  e x t i n c t i o n .   T
> h e s e  r e s u l t s ,   c o m b i n e d   w i t h  a n a l y s i s   o f
>   c l i m b i n g  f i b r e   a c t i v i t y   i n   a  c o m p u t e r
> s i m u l a t i o n   o f  t h e   c e r e b e l l a r - o l i v a r y  s y
> s t e m ,   s u g g e s t   t h a t  t r a n s i e n t   i n h i b i t i o n
>   o f  c l i m b i n g   f i b r e s   b e l o w  t h e i r   b a c k g r o
> u n d   l e v e l  i s   t h e   s i g n a l   t h a t  d r i v e s   e x t
> i n c t i o n . T h i s   i s   o n e   o f   s e v e r a l  c o m p u t a t
> i o n a l   s t u d i e s   b y  M a u k   a n d   c o l l a b o r a t o r s
>  t h a t   a r e   e n h a n c i n g   o u r  k n o w l e d g e   o f   c e
> r e b e l l a r  p r o c e s s i n g   ( a l s o   s e e  s i m i l a r   p
> a p e r s   b y  R a y m o n d   &   L i s b e r g e r  a p p l i e d   t o
>   t h e   V O R ) .
> 8 .   O l s h a u s e n   B A ,   F i e l d  D J .     N a t u r e .   E m
> e r g e n c e  o f   s i m p l e - c e l l   r e c e p t i v e  f i e l d
> p r o p e r t i e s   b y  l e a r n i n g   a   s p a r s e   c o d e  f o
> r   n a t u r a l   i m a g e s .   1 9 9 6  J u n   1 3 ; 3 8 1 ( 6 5 8 3 )
> : 6 0 7 - 9 A b s t r a c t . T h e   r e c e p t i v e  f i e l d s   o f
> s i m p l e   c e l l s  i n   m a m m a l i a n   p r i m a r y  v i s u a
> l   c o r t e x   c a n   b e  c h a r a c t e r i z e d   a s   b e i n g
>  s p a t i a l l y   l o c a l i z e d ,  o r i e n t e d   a n d   b a n d
> p a s s  ( s e l e c t i v e   t o   s t r u c t u r e  a t   d i f f e r e
> n t   s p a t i a l  s c a l e s ) ,   c o m p a r a b l e   t o  t h e   b
> a s i s   f u n c t i o n s   o f  w a v e l e t   t r a n s f o r m s .   O
> n e  a p p r o a c h   t o  u n d e r s t a n d i n g   s u c h  r e s p o n
> s e   p r o p e r t i e s   o f  v i s u a l   n e u r o n s   h a s   b e e
> n  t o   c o n s i d e r   t h e i r  r e l a t i o n s h i p   t o   t h e
>  s t a t i s t i c a l   s t r u c t u r e   o f  n a t u r a l   i m a g e
> s   i n   t e r m s  o f   e f f i c i e n t   c o d i n g .  A l o n g   t
> h e s e   l i n e s ,   a  n u m b e r   o f   s t u d i e s   h a v e  a t
> t e m p t e d   t o   t r a i n  u n s u p e r v i s e d   l e a r n i n g
>  a l g o r i t h m s   o n   n a t u r a l  i m a g e s   i n   t h e   h o
> p e   o f  d e v e l o p i n g   r e c e p t i v e  f i e l d s   w i t h
> s i m i l a r  p r o p e r t i e s ,   b u t   n o n e   h a s  s u c c e e
> d e d   i n   p r o d u c i n g   a  f u l l   s e t   t h a t   s p a n s
> t h e  i m a g e   s p a c e   a n d   c o n t a i n s  a l l   t h r e e
> o f   t h e   a b o v e  p r o p e r t i e s .   H e r e   w e  i n v e s t
> i g a t e   t h e   p r o p o s a l  t h a t   a   c o d i n g   s t r a t e
> g y  t h a t   m a x i m i z e s  s p a r s e n e s s   i s   s u f f i c i
> e n t  t o   a c c o u n t   f o r   t h e s e  p r o p e r t i e s .   W e
>   s h o w   t h a t  a   l e a r n i n g   a l g o r i t h m  t h a t   a t
> t e m p t s   t o   f i n d  s p a r s e   l i n e a r   c o d e s   f o r
>  n a t u r a l   s c e n e s   w i l l  d e v e l o p   a   c o m p l e t e
>  f a m i l y   o f   l o c a l i z e d ,  o r i e n t e d ,   b a n d p a s
> s  r e c e p t i v e   f i e l d s ,  s i m i l a r   t o   t h o s e   f o
> u n d  i n   t h e   p r i m a r y   v i s u a l  c o r t e x .   T h e   r
> e s u l t i n g  s p a r s e   i m a g e   c o d e  p r o v i d e s   a   m
> o r e  e f f i c i e n t   r e p r e s e n t a t i o n  f o r   l a t e r
> s t a g e s   o f  p r o c e s s i n g   b e c a u s e   i t  p o s s e s s
> e s   a   h i g h e r  d e g r e e   o f   s t a t i s t i c a l  i n d e p
> e n d e n c e   a m o n g   i t s  o u t p u t s . T h i s   n o w   c l a s
> s i c   s t u d y  s u g g e s t s   h o w   t h e  s t a t i s t i c a l
> s t r u c t u r e   o f  n a t u r a l   i m a g e s   m a y  d e t e r m i
> n e   t h e   r e s p o n s e  p r o p e r t i e s   o f   V 1   c e l l s ,
>  a n d   s e t   t h e   s t a g e   f o r  m a n y   l a t e r   s t u d i
> e s  d i s c u s s i n g   t h e   c o n c e p t  o f     s p a r s e   c o
> d i n g     o f  i m a g e s .
> 9 .   T a y l o r   D M ,   T i l l e r y  S I ,   S c h w a r t z   A B .
>   D i r e c t  c o r t i c a l   c o n t r o l   o f   3 D  n e u r o p r o
> s t h e t i c   d e v i c e s .  S c i e n c e .   2 0 0 2   J u n  7 ; 2 9
> 6 ( 5 5 7 4 ) : 1 8 2 9 - 3 2 .   A b s t r a c t .  T h r e e - d i m e n s
> i o n a l   ( 3 D )  m o v e m e n t   o f  n e u r o p r o s t h e t i c
> d e v i c e s  c a n   b e   c o n t r o l l e d   b y   t h e  a c t i v i
> t y   o f   c o r t i c a l  n e u r o n s   w h e n   a p p r o p r i a t e
>  a l g o r i t h m s   a r e   u s e d   t o  d e c o d e   i n t e n d e d
>   m o v e m e n t  i n   r e a l   t i m e .   P r e v i o u s  s t u d i e
> s   a s s u m e d   t h a t  n e u r o n s   m a i n t a i n   f i x e d  t
> u n i n g   p r o p e r t i e s ,   a n d  t h e   s t u d i e s   u s e d
>  s u b j e c t s   w h o   w e r e  u n a w a r e   o f   t h e   m o v e m
> e n t s  p r e d i c t e d   b y   t h e i r  r e c o r d e d   u n i t s .
>   I n   t h i s  s t u d y ,   s u b j e c t s   h a d  r e a l - t i m e
> v i s u a l  f e e d b a c k   o f   t h e i r  b r a i n - c o n t r o l l
> e d  t r a j e c t o r i e s .   C e l l  t u n i n g   p r o p e r t i e s
>  c h a n g e d   w h e n   u s e d   f o r  b r a i n - c o n t r o l l e d
>  m o v e m e n t s .   B y   u s i n g  c o n t r o l   a l g o r i t h m s
>   t h a t  t r a c k   t h e s e   c h a n g e s ,  s u b j e c t s   m a d
> e   l o n g  s e q u e n c e s   o f   3 D  m o v e m e n t s   u s i n g
> f a r  f e w e r   c o r t i c a l   u n i t s  t h a n   e x p e c t e d .
>   D a i l y  p r a c t i c e   i m p r o v e d  m o v e m e n t   a c c u r
> a c y   a n d  t h e   d i r e c t i o n a l   t u n i n g  o f   t h e s e
>   u n i t s . T h i s   r e p r e s e n t s   s o m e   o f  t h e   s e m i
> n a l   w o r k  d e c o d i n g   c o r t i c a l  a c t i v i t y   t o
> c o n t r o l  n e u r a l   p r o s t h e t i c s .
> 1 0 .   V a n   V r e e s w i j k   C ,  A b b o t t   L F ,   E r m e n t
> r o u t  G B .   W h e n   i n h i b i t i o n   n o t  e x c i t a t i o n
>   s y n c h r o n i z e s  n e u r a l   f i r i n g .   J   C o m p u t  N
> e u r o s c i .   1 9 9 4  D e c ; 1 ( 4 ) : 3 1 3 - 2 1 . A b s t r a c t .
>   E x c i t a t o r y   a n d  i n h i b i t o r y   s y n a p t i c  c o u
> p l i n g   c a n   h a v e  c o u n t e r - i n t u i t i v e  e f f e c t
> s   o n   t h e  s y n c h r o n i z a t i o n   o f  n e u r o n a l   f i
> r i n g .   W h i l e  i t   m i g h t   a p p e a r   t h a t  e x c i t a
> t o r y   c o u p l i n g  w o u l d   l e a d   t o  s y n c h r o n i z a
> t i o n ,   w e   s h o w  t h a t   f r e q u e n t l y  i n h i b i t i o
> n   r a t h e r   t h a n  e x c i t a t i o n   s y n c h r o n i z e s  f
> i r i n g .   W e   s t u d y   t w o  i d e n t i c a l   n e u r o n s  d
> e s c r i b e d   b y  i n t e g r a t e - a n d - f i r e  m o d e l s ,
> g e n e r a l  p h a s e - c o u p l e d   m o d e l s   o r  t h e   H o d
> g k i n - H u x l e y   m o d e l  w i t h   m u t u a l ,  n o n - i n s t
> a n t a n e o u s  e x c i t a t o r y   o r   i n h i b i t o r y  s y n a
> p s e s   b e t w e e n   t h e m .  W e   f i n d   t h a t   i f   t h e
> r i s e  t i m e   o f   t h e   s y n a p s e   i s  l o n g e r   t h a n
>   t h e   d u r a t i o n  o f   a n   a c t i o n   p o t e n t i a l ,  i
> n h i b i t i o n   n o t  e x c i t a t i o n   l e a d s   t o  s y n c h
> r o n i z e d   f i r i n g .
>  I f   I   w e r e   t o   u p d a t e ,   I  t h i n k   I   w o u l d   a
> d d   p a p e r s  f r o m :
> 1 )   N e u r o e c o n o m i c s   &  R e i n f o r c e m e n t   L e a r
> n i n g - -  i n   a d d i t i o n   t o   t h e  s e m i n a l   w o r k
> b y   D a y a n   &  S c h u l t z   ( a l r e a d y   i n  a t t a c h e d
> ) ,   p e r h a p s  L o e w e n s t e i n / S e u n g   p a p e r  o n   m
> a t c h i n g   b e h a v i o r   a s  a   g e n e r i c   c o n s e q u e n
> c e   o f  c o r r e l a t i o n a l   l e a r n i n g  r u l e s .
> 2 )   B a y e s i a n   n e t w o r k s   - -  m a y b e   M a ,   B e c k
> ,   L a t h a m ,  P o u g e t   o r   o t h e r s   o n   i d e a  t h a t
>   t h e   b r a i n   m a y  e n c o d e   &   c o m p u t e   w i t h  p r
> o b a b i l i t i e s
> - - END OF FILE
> --
> --
> Dr Jim Stone,
> Psychology Department, Sheffield University, Sheffield, S10 2TP, UK.
> Tel: 0114 2226522. http://jim-stone.staff.shef.ac.uk/
> _______________________________________________
> Comp-neuro mailing list
> Comp-neuro at neuroinf.org
> http://www.neuroinf.org/mailman/listinfo/comp-neuro
>
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