Biological neural networks are large systems of complex elements interacting through a complex array of connections.
How do we describe and interpret the activity of a large
population of neurons and how do we model neural circuits when:
o individual neurons are such complex elements and
o our knowledge of the synaptic connections is so incomplete?
A review paper by L.F. Abbott
Center for Complex Systems
Brandeis University
Waltham, MA 02254
Published in Quart. Rev. Biophys. 27:291-331 (1994)
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Information theory quantifies how much information a neural response carries about the stimulus. This can be compared to the information transferred in particular models of the stimulus-response function and to maximum possible information transfer. Such comparisons are crucial because they validate assumptions present in any neurophysiological analysis.
The authors review information-theory basics before demonstrating its use in neural coding, validating simple stimulus-response models of neural coding of dynamic stimuli.
By Alexander Borst & Frederic E. Theunissen
Nature Neuroscience 2, 947 – 957 (1999)
doi:10.1038/14731
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The nervous system represents time-dependent signals in sequences of discrete action potentials or spikes, all spikes are identical so that information is carried only in the spike arrival times.
A scientific paper by Steven P. Strong, Roland Koberle, Rob R. de Ruyter van Steveninck, and William Bialek
-NEC Research Institute, Princeton, New Jersey
-Department of Physics, Princeton University, Princeton, New Jersey
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Shannon mutual information provides a measure of how much information is, on average, contained in a set of neural activities about a set of stimuli. It has been extensively used to study neural coding in different brain areas. To apply a similar approach to investigate single stimulus encoding, the authors need to introduce a quantity specific for a single stimulus.
A scientific paper by Michele Bezzi
Accenture Technology Labs, Sophia Antipolis, France
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