Every month we host an invited speaker whose experimental or theoretical work falls in the realm of Computational Neuroscience.
Students and postdocs from any department can join a sponsored lunch with the speaker (limited to 9 people + the speaker). We also host a “brain storm” dinner the night of the talk with the rest of the computational neuroscience community.
Please contact Shih-Chieh Lin <sclin@neuro.duke.edu> if you want to arrange and appointment with the speaker.
U. Texas at San Antonio
Friday Frebruary 27 @ 4pm, Bryan 103
Brandeis Univeristy
Wednesday March 3 @ 2pm, Bryan 103
Cold Spring Harbor Laboratory
Thursday April 29 @ 4pm, Bryan 103
Georgia Institute of Technology
The neurally-controlled animat: Using multi-electrode arrays, 2-photon microscopy, and optical recording to study learning in vitro.
Thursday May 22
University of Antwerp, Belgium
Fast cerebellar oscillations: experiments and modeling
Wednesda April 30
Background synaptic activity as a switch for neural networks
Department of Neurobiology and Anatomy
Wake Forest University School of Medicine
Background synaptic activity as a switch for neural networks
Spatiotemporal codes for odor identity and concentration
Sensory Coding and Neural Ensembles Unit, NICHD
February 27 @ 2pm in Bryan Research Bldg 101L3
Spatiotemporal codes for odor identity and concentration
Paul Tiesinga
UNC-Physics
Synchronization as a mechanism for attentional gain modulation
January 23 @ 2pm in Bryan Research Bldg 101L
Synchronization as a mechanism for attentional gain modulation
Visual stimuli are transduced by the retina into spike trains
that are subsequently transmitted via the lateral geniculate
nucleus (LGN) and primary visual cortex (V1) to other visual
cortical areas including V2,V4 and MT. As neural signals representing
visual stimuli propagate through the visual pathway, more neurons
become involved in the representation, each of them responding to
a larger part of the visual field and to different features of the
visual stimulus (motion, color etc). Neural responses also become
increasingly dominated by non-sensory inputs, such as selective
attention and perceptual dominance. Correlations and synchronization
has been observed in these areas. How does the representation of visual
input in the neural spike trains evolve along the visual pathway?
Specifically, what role do synchronization and spike time precision
have in organizing the flow of information in the visual pathway?
I will consider these questions using recent experiments on selective
attention as an example.
A common neuroscience protocol is to present a visual stimulus to
an animal and record the activity of single neurons in the appropriate
brain area. The neural response is often the product of two stimulus
features (color, brightness, attention, etc): firing rate=f(x)g(y).
The neural substrate for this fundamental computation -- gain modulation --
remains elusive. Recent experiments [Steinmetz et al, Nature 404,187
(2000); Fries et al, Science 291, 1560 (2001)] suggest that synchrony in
the gamma frequency range may play an important role in attentional gain
modulation.
January 23, 2004