[Comp-neuro] postdoctoral positions in theoretical neuroscience
msgoldman at ucdavis.edu
Sat Sep 28 10:22:45 CEST 2013
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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