[Comp-neuro] Postdoctoral positions: Memory, motor, & learning dynamics
msgoldman at ucdavis.edu
Fri Jul 25 19:42:43 CEST 2014
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
/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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