[Comp-neuro] postdoctoral positions in theoretical neuroscience

[Mark Goldman]

Up to 2 postdoctoral 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 decision-making 
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 in 
the greater San Francisco Bay Area.

Candidates should send a CV, brief statement of previous research and 
future research interests, and email addresses and phone numbers of 
three references to:Mark Goldman, msgoldman at ucdavis.edu.

Recent topics of particular interest to the laboratory are:

1) *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 is a model system for understanding the mathematical 
integration of inputs and the maintenance of persistent neural 
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 from the 
laboratories of David Tank at Princeton University and Emre Aksay at 
Weill Medical College of Cornell University.

/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 experiments in the 
Aksay laboratory and genetic manipulations and electrophysiological 
recordings 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) *Collective intelligence and decision-making in ant colonies*: In 
collaboration with Deborah Gordon's laboratory at Stanford University, 
we are using the foraging behavior of desert ants as a model system to 
quantitatively understand social decision-making.Desert ants have strong 
ecological pressure to make wise choices as to when to leave the nest to 
forage for food.We are modeling how the decision-making processes of 
individual ants result in adaptive whole-colony behavior.

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