Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity

Experimental studies have observed synaptic potentiation when a presynaptic neuron fires shortly before a postsynaptic neuron and synaptic depression when the presynaptic neuron fires shortly after. The dependence of synaptic modulation on the precise timing of the two action potentials is known as...

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Bibliographic Details
Published in:Neural computation 2007-02, Vol.19 (2), p.371-403
Main Authors: Bohte, Sander M., Mozer, Michael C.
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Animals
Applied sciences
Artificial intelligence
Biological and medical sciences
Biophysics
Computer science
control theory
systems
Computer Simulation
Connectionism. Neural networks
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General aspects
Learning and adaptive systems
Letters
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Neurological
Neuronal Plasticity - physiology
Neurons
Neurons - physiology
Neurosciences
Stochastic Processes
Synapses - physiology
Synaptic Transmission - physiology
Time Factors
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Summary:Experimental studies have observed synaptic potentiation when a presynaptic neuron fires shortly before a postsynaptic neuron and synaptic depression when the presynaptic neuron fires shortly after. The dependence of synaptic modulation on the precise timing of the two action potentials is known as spike-timing dependent plasticity (STDP). We derive STDP from a simple computational principle: synapses adapt so as to minimize the postsynaptic neuron's response variability to a given presynaptic input, causing the neuron's output to become more reliable in the face of noise. Using an objective function that minimizes response variability and the biophysically realistic spike-response model of Gerstner (2001), we simulate neurophysiological experiments and obtain the characteristic STDP curve along with other phenomena, including the reduction in synaptic plasticity as synaptic efficacy increases. We compare our account to other efforts to derive STDP from computational principles and argue that our account provides the most comprehensive coverage of the phenomena. Thus, reliability of neural response in the face of noise may be a key goal of unsupervised cortical adaptation.
ISSN:0899-7667
1530-888X
DOI:10.1162/neco.2007.19.2.371