A neuronal learning rule for sub-millisecond temporal coding
Wulfram Gerstner,Wulfram Gerstner,Richard Kempter,J. Leo van Hemmen,Hermann Wagner,Hermann Wagner +5 more
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TLDR
A modelling study based on computer simulations of a neuron in the laminar nucleus of the barn owl shows that the necessary degree of coherence in the signal arrival times can be attained during ontogenetic development by virtue of an unsupervised hebbian learning rule.Abstract:
A paradox that exists in auditory and electrosensory neural systems is that they encode behaviorally relevant signals in the range of a few microseconds with neurons that are at least one order of magnitude slower. The importance of temporal coding in neural information processing is not clear yet. A central question is whether neuronal firing can be more precise than the time constants of the neuronal processes involved. Here we address this problem using the auditory system of the barn owl as an example. We present a modelling study based on computer simulations of a neuron in the laminar nucleus. Three observations explain the paradox. First, spiking of an 'integrate-and-fire' neuron driven by excitatory postsynaptic potentials with a width at half-maximum height of 250 micros, has an accuracy of 25 micros if the presynaptic signals arrive coherently. Second, the necessary degree of coherence in the signal arrival times can be attained during ontogenetic development by virtue of an unsupervised hebbian learning rule. Learning selects connections with matching delays from a broad distribution of axons with random delays. Third, the learning rule also selects the correct delays from two independent groups of inputs, for example, from the left and right ear.read more
Citations
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Book ChapterDOI
How Is Time Represented in the Brain
TL;DR: Algorithms which deal with sensory pattern sequences that vary over time scales from a few to a few hundred milliseconds are presented, of particular importance are algorithms that are compatible with naturally occurring signal variations such as achange of the stimulus intensity or a change of the duration of all signal components.
Journal ArticleDOI
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Dissertation
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TL;DR: A phenomenological model of Hebbian plasticity at the location of the synapse is used and it is found that the time course of the voltage in the neighborhood of a stimulatedsynapse is a reliable predictor of whether a stimulated synapse undergoes potentiation, depression, or no change.
References
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The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs
William R. Softky,Christof Koch +1 more
TL;DR: It is argued that neurons that act as temporal integrators over many synaptic inputs must fire very regularly and only in the presence of either fast and strong dendritic nonlinearities or strong synchronization among individual synaptic events will the degree of predicted variability approach that of real cortical neurons.
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