Mathematical Biology Seminar
Will Nesse
University of
Ottawa Centre for Neural Dynamics
Wednesday, Sept. 2, 2009
3:05pm in LCB 225
The ABC's of the Neural Language: Independent Statistical
Decomposition of Adapting Spike Dynamics
Abstract:
Extracting sensory information from the environment by neurons is
limited by how much information can be captured by neural spike
trains. Sensory systems that can encode more information than others
have a survival advantage. Several recent studies have established
that fine-grained information from the specific temporal structure of
sensory spike trains appears necessary for encoding weak, behaviorally
important stimuli. Still, how this information is captured by a
neuron's biophysical machinery is unresolved. Commonly, spike
generation by neurons is modulated by slow activity-dependent currents
that act as a negative feedback, retarding subsequent spiking. Such
adaptive currents induce correlations and rich temporal relationships
in the spike emission. Using mathematical analysis, and preliminary
experimental observations by my collaborator Gary Marsat, I show that
spike emission governed by realistic adaptation currents leads to a
handy probabilistic decomposition of the spike train into
statistically independent components. Using this decomposition, we
establish novel information-theoretic properties of these naturalistic
spike trains. Further, I argue that while realistic correlated spike
trains posses more redundant information overall, that the information
is in a more accessible form for subsequent stages of sensory
processing.
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Mathematical Biology Program
Department of Mathematics
University of Utah
155 South 1400 East Room 233
Salt Lake City, UT 84112
rasmusse@math.utah.edu
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