Title
Temporal correlation based learning in neuron models.
Abstract
We study a learning rule based upon the temporal correlation (weighted by a learning kernel) between incoming spikes and the internal state of the postsynaptic neuron, building upon previous studies of spike timing dependent synaptic plasticity (Kempter, R., Gerstner, W., van Hemmen, J.L., Wagner, H., 1998. Extracting Oscillations: Neuronal coincidence detection with noisy periodic spike input. Neural computation 10, 1987-2017; Kempter, R., Gerstner, W., van Hemmen, J.L., 1999. Hebbian learning and spiking neurons. Physical Reviewm E59, 4498-4514; van Hemmen, J.L., 2001. Theory of synaptic plasticity. In: Moss, F., Gielen, S. (Eds.), Handbook of biological physics. vol. 4, Neuro Informatics, neural modelling, Elsevier, Amsterdam, pp. 771-823. Our learning rule for the synaptic weight w(ij) is [formula: see text] where the t(j,mu) are the arrival times of spikes from the presynaptic neuron j and the function u(t) describes the state of the postsynaptic neuron i. Thus, the spike-triggered average contained in the inner integral is weighted by a kernel Gamma(s), the learning window, positive for negative, negative for positive values of the time difference s between post- and presynaptic activity. An antisymmetry assumption for the learning window enables us to derive analytical expressions for a general class of neuron models and to study the changes in input-output relationships following from synaptic weight changes. This is a genuinely non-linear effect (Song, S., Miller, K., Abbott, L., 2000. Competitive Hebbian learning through spike-timing dependent synaptic plasticity. Nature Neuroscience 3, 919-926).
Year
DOI
Venue
2006
10.1016/j.thbio.2006.03.001
Theory in Biosciences
Keywords
Field
DocType
Neural learning,spike timing dependent synaptic plasticity,Temporal correlation,Spiking neuron model
Combinatorics,Biological neuron model,Neurotransmission,Biology,Postsynaptic potential,Hebbian theory,Learning rule,Synaptic plasticity,Coincidence detection in neurobiology,Genetics,Synaptic weight
Journal
Volume
Issue
ISSN
125
1
1431-7613
Citations 
PageRank 
References 
1
0.39
3
Authors
1
Name
Order
Citations
PageRank
Jürgen Jost19512.39