A computational framework for cortical learning

Recent physiological findings have revealed that long-term adaptation of the synaptic strengths between cortical pyramidal neurons depends on the temporal order of presynaptic and postsynaptic spikes, which is called spike-timing-dependent plasticity (STDP) or temporally asymmetric Hebbian (TAH) lea...

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Bibliographic Details
Published in:Biological cybernetics 2004-06, Vol.90 (6), p.400-409
Main Author: Suri, Roland E
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Algorithms
Animals
Cerebral Cortex - physiology
Excitatory Postsynaptic Potentials - physiology
Humans
Learning - physiology
Nerve Net - physiology
Neural Networks (Computer)
Neural Pathways - physiology
Neuronal Plasticity - physiology
Neurons
Pyramidal Cells - physiology
Synapses - physiology
Synaptic Transmission - physiology
Time Factors
Voice recognition
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Summary:Recent physiological findings have revealed that long-term adaptation of the synaptic strengths between cortical pyramidal neurons depends on the temporal order of presynaptic and postsynaptic spikes, which is called spike-timing-dependent plasticity (STDP) or temporally asymmetric Hebbian (TAH) learning. Here I prove by analytical means that a physiologically plausible variant of STDP adapts synaptic strengths such that the presynaptic spikes predict the postsynaptic spikes with minimal error. This prediction error model of STDP implies a mechanism for cortical memory: cortical tissue learns temporal spike patterns if these spike patterns are repeatedly elicited in a set of pyramidal neurons. The trained network finishes these patterns if their beginnings are presented, thereby recalling the memory. Implementations of the proposed algorithms may be useful for applications in voice recognition and computer vision.
ISSN:0340-1200
1432-0770
DOI:10.1007/s00422-004-0487-1