[Comp-neuro] postdoctoral positions in theoretical neuroscience

     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@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.