[Comp-neuro] Postdoctoral positions: Memory, motor, & learning dynamics

[Mark Goldman]

Two theoretical postdoctoral positions and 1-2 collaborative 
experiment-theory positions are available in the laboratory of Dr. Mark 
Goldman at the University of California at Davis.The lab works on a 
broad range of problems in computational neuroscience ranging from 
neural coding to dynamics and plasticity of single neurons and networks. 
Immediate funding is available for a range of projects related to 
working memory, neural integration, motor learning, and synaptic 
plasticity as described below.The postdoctoral candidate also would have 
flexibility to work on a range of issues of his or her 
choosing.Candidates are expected to have strong training in an 
analytically rigorous discipline such as theoretical neuroscience, 
physics, mathematics, computer science, or engineering.The postdoctoral 
candidate will have ample opportunity to interact within the vibrant 
computational and systems neuroscience communities at UC Davis and the 
greater San Francisco Bay Area.

Candidates should send a CV, brief statement of previous research and 
future research interests, and contact information for three references 
to: Mark Goldman, msgoldman at ucdavis.edu.

Specific positions with funding include:

1) *Theoretical positions:Dynamics of memory and motor-related neural 
activity*

/Challenging the attractor picture of working memory/.In the traditional 
attractor picture of working memory, memory storage results from 
positive feedback processes that lead to the formation of self-sustained 
attractors.In one project, we are exploring how functionally 
feedforward, rather than feedback, network architectures can generate 
flexible codes for storing memories and producing a broad range of 
input-output transformations.In a second project, we are utilizing 
methods from engineering control theory to show how balanced cortical 
networks can utilize negative feedback to stabilize persistent patterns 
of neural activity.

/Multi-scale modeling of neural integration./The oculomotor neural 
integrator, which transforms eye-velocity encoding motor commands into 
eye-position encoding commands, is a model system for understanding the 
mathematical integration of inputs and the maintenance of memory-storing 
activity.We seek to determine the respective roles of cellular and 
circuit mechanisms of memory storage in this system.Multi-scale models, 
from ion channels to behavior, will be generated based upon 
electrophysiological and optical imaging recordings and optogenetic 
manipulations performed in our experimental collaborators' laboratories.

/Context-dependent memory storage./Recent experiments suggest that the 
oculomotor neural integrator functions in a context-dependent manner, 
producing spatially distinct activity patterns depending upon whether 
eye movements are being made in the context of rapid changes in gaze 
(saccades) versus smooth tracking of moving objects.We seek to determine 
the circuit mechanisms underlying this context-dependent activity and to 
propose general frameworks, applicable to both oculomotor and cortical 
memory systems, for how multiple context-dependent inputs can be 
separately stored in a single network.

/Role of the granule cell layer in cerebellar motor learning./The eye 
movement system provides a highly tractable setting for studying motor 
learning because it is well-characterized experimentally and has fewer 
degrees of freedom than more complicated movement systems.In 
collaboration with whole-circuit optical imaging and optogenetic 
perturbation experiments in the Aksay laboratory at Cornell Medical 
University and genetic manipulations, electrophysiological recording, 
and optogenetic perturbations in Jennifer Raymond's laboratory at 
Stanford University, we are modeling the neural dynamics and coding of 
cerebellar granule neurons and their relation to Purkinje cell firing 
and the plasticity of eye movement behaviors.

2) *Experimental positions:*

*Development, dynamics, and plasticity of neural networks: *In 
collaboration with Kim McAllister (kmcallister at ucdavis.edu 
<mailto:kmcallister at ucdavis.edu>), we are seeking to understand the 
learning rules underlying development and learning in neural networks.  
We will use cutting-edge technology in patterned substrates, 
optogenetics, and uncaging in a novel long-term imaging assay for 
synapse dynamics that allows recording and single synapse or single cell 
manipulation of neuronal and network activity.  These cultured networks 
will be used to directly test central tenets of Hebbian, spike-timing 
dependent, and homeostatic learning over development.  The applicant 
will perform these imaging experiments as well as apply theoretical 
models to better understand and predict our results.

*Dynamics of memory and motor-related neural activity:*There are also 
possibilities for joint experimental-theoretical work with Emre Aksay's 
laboratory at Weill Medical College of Cornell University (in New York 
City) on either oculomotor memory storage or cerebellar motor learning, 
using /in vivo/ two-photon optical recording and stimulation to dissect 
circuit function and plasticity.Interested candidates should contact Dr. 
Aksay at ema2004 at med.cornell.edu <mailto:ema2004 at med.cornell.edu>.

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