Postdoctoral position in theoretical neuroscience
Autor: Nivaldo A P de Vasconcelos | Publicado: 2012-10-17T14:33:00-07:00


A postdoctoral position is available in
            the laboratory of Dr.
            Mark Goldman at the University
            of California at Davis.&nbsp; 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. &nbsp;Immediate funding is
            available for a range of
            projects related to working memory, neural integration,
            motor learning, and
            decision-making as described below.&nbsp; The
            postdoctoral candidate also would have flexibility to work
            on a range of issues
            of his or her choosing.&nbsp; Candidates
            are
            expected to have strong training in an analytically rigorous
            discipline such as
            theoretical neuroscience, physics, mathematics, computer
            science, or
            engineering.&nbsp; 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.&nbsp; &nbsp;&nbsp;

&nbsp;&nbsp;&nbsp;&nbsp; Candidates
should
            send a CV, brief statement of previous research and future
            research
            interests, and email addresses and phone numbers of three
            references to:&nbsp; Mark
            Goldman, msgoldman@ucdavis.edu.&nbsp; I will also be at the
            upcoming SFN meeting.



Recent topics of particular interest to
            the laboratory are:



1) Dynamics of memory
              and motor-related neural activity:&nbsp; 

&nbsp;&nbsp;&nbsp;&nbsp; Challenging the
              attractor picture of working
              memory.&nbsp; In the
            traditional attractor
            picture of working memory, memory storage results from
            positive feedback
            processes that lead to the formation of self-sustained
            attractors.&nbsp; 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.&nbsp;
            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.&nbsp; 

&nbsp;&nbsp;&nbsp;&nbsp; Multi-scale modeling of
              neural integration.&nbsp; The
            oculomotor neural integrator is a model system
            for understanding the mathematical integration of inputs and
            the maintenance of
            persistent neural activity.&nbsp;
            We seek to
            determine the respective roles of cellular and circuit
            mechanisms of memory
            storage in this system.&nbsp; 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. &nbsp;

&nbsp;&nbsp;&nbsp;&nbsp; Role of the granule
              cell layer in cerebellar
              motor learning.&nbsp; 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.&nbsp; In
            collaboration with the whole-circuit optical imaging
            experiments of Emre Aksay’s
            laboratory, we are modeling the neural dynamics and coding
            of cerebellar
            granule neurons.&nbsp; Particular
            focus is
            upon understanding the circuit basis for the transformations
            underlying
            plasticity in the gaze-holding system and oculomotor neural
            integrator.&nbsp; 



2) Collective
              intelligence and decision-making in ant colonies: &nbsp;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.&nbsp; Desert
          ants have strong
          ecological pressure to make wise choices as to when to leave
          the nest to forage
          for food.&nbsp; We are
          modeling how the decision-making
          processes of individual ants result in adaptive whole-colony
          behavior.