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Biophysics of ComputationInformation Processing in Single Neurons$
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Christof Koch

Print publication date: 1998

Print ISBN-13: 9780195104912

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780195104912.001.0001

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Stochastic Models of Single Cells

Stochastic Models of Single Cells

Chapter:
(p.350) 15 Stochastic Models of Single Cells
Source:
Biophysics of Computation
Author(s):

Christof Koch

Publisher:
Oxford University Press
DOI:10.1093/oso/9780195104912.003.0021

The majority of experiments in neurophysiology are based upon spike trains recorded from individual or multiple nerve cells. If all the action potentials are taken to be identical and only the times at which they are generated are considered, the experimentalist obtains a discrete series of time events {t1,···, tn}, where t¡ corresponds to the occurrence of the i th spike, characterizing the spike train. This spike train is transmitted down the axon to all of the target cells of the neuron, and it is this spike train that contains all of the relevant information that the cell is representing (assuming no dendro-dendritic connections). As alluded to in the preceding chapter, there are two opposing views of neuronal coding, with many interim shades. One view holds that it is the firing rate, averaged over a suitable temporal window (Eqs. 14.1 or 14.2), that is relevant for information processing. The dissenting view, correlation coding, argues that the interactions among spikes, at the single cell as well as between multiple cells, encodes information. A key property of spike trains is their seemingly stochastic or random nature, quite in contrast to switching in digital computers. This randomness is apparent in the highly irregular discharge pattern of a central neuron to a sensory stimulus whose details are rarely reproducible from one trial to the next. The apparent lack of reproducible spike patterns has been one of the principal arguments in favor of the hypothesis that neurons only care about the firing frequency averaged over very long time windows. Such a mean rate code is very robust to “sloppy” hardware but is also relatively inefficient in terms of transmitting the maximal amount of information per spike. Encoding information in the intervals between spikes is obviously much more efficient, in particular if correlated across multiple neurons. Such a scheme does place a premium on postsynaptic neurons that can somehow decode this information. Because little or no information can be encoded into a stream of regularly spaced action potentials, this raises the question of how variable neuronal firing really is.

Keywords:   Absolute refractory period, Brownian motion, Cable equation, Deterministic chaos, Fano factor, Gain normalization, Interspike intervals (ISIs), Leak resistance, Mean rate code, Point processes

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