COMPUTATIONAL NEUROSCIENCE COLLOQUIUM
Modeling Neocortical Networks with Short-term Synaptic Plasticity
Misha Tsodyks
Weizmann Institute
Wednesday, February 14, 2.30pm
Maxwell Dworkin 115
ABSTRACT
Recurrent connections in neocortical circuits exhibit pronounced
activity-dependent short-term plasticity--synaptic
depression/facilitation--but the role it plays in information processing
is still not clear. Recent experiments indicate that synaptic properties
may systematically change when moving along the information stream from
the sensory areas towards the prefrontal cortex. In particular, it was
found that connections between pyramidal neurons in the prefrontal
cortex are more diverse, with substantial fraction of them exhibiting
strong facilitation, as opposed to corresponding connections in the
sensory areas that are predominantly depressing. To uncover the
potential functional implications of these diverse properties, we
analyzed two different recurrent networks. In the first study, we
describe the information processing via temporal neuronal
synchronization (which we call 'Population Spikes') in network with
synaptic depression. In particular, we constructed a neural network
model of primary auditory cortex that responds to complex sounds with
specific spatio-temporal pattern of population spikes at different
iso-frequency columns. In the second study, we analyzed the attractor
neural network model of persistent activity with synaptic facilitation.
Depending on the strength of synaptic facilitation, the network exhibits
qualitatively different regimes of convergence to persistent activity
sate, from fast irreversible transition with population spike, to slow
reversible transition. We then considered the attractor neural network
of interacting sub-populations with different synaptic properties, and
observed an emerging new selectivity of the resulting persistent state
to the temporal profile of the triggering input.
Host: Leslie Valiant
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