Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission

@article{citeulike:688502, abstract = {Maximization of information transmission by a spiking-neuron model predicts changes of synaptic connections that depend on timing of pre- and postsynaptic spikes and on the postsynaptic membrane potential. Under the assumption of Poisson firing statistics, the synaptic update rule exhibits all of the features of the Bienenstock-Cooper-Munro rule, in particular, regimes of synaptic potentiation and depression separated by a sliding threshold. Moreover, the learning rule is also applicable to the more realistic case of neuron models with refractoriness, and is sensitive to correlations between input spikes, even in the absence of presynaptic rate modulation. The learning rule is found by maximizing the mutual information between presynaptic and postsynaptic spike trains under the constraint that the postsynaptic firing rate stays close to some target firing rate. An interpretation of the synaptic update rule in terms of homeostatic synaptic processes and spike-timing-dependent plasticity is discussed. 10.1073/pnas.0500495102}, author = {Toyoizumi, Taro and Pfister, Jean-Pascal and Aihara, Kazuyuki and Gerstner, Wulfram }, citeulike-article-id = {688502}, doi = {http://dx.doi.org/10.1073/pnas.0500495102}, journal = {PNAS}, keywords = {bayesian, stdp}, month = {April}, number = {14}, pages = {5239--5244}, posted-at = {2008-04-29 19:08:43}, priority = {2}, title = {Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission}, url = {http://dx.doi.org/10.1073/pnas.0500495102}, volume = {102}, year = {2005} }

TY - JOUR ID - citeulike:688502 L3 - citeulike-article-id:688502 TI - Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission JF - PNAS VL - 102 IS - 14 SP - 5239 EP - 5244 N2 - Maximization of information transmission by a spiking-neuron model predicts changes of synaptic connections that depend on timing of pre- and postsynaptic spikes and on the postsynaptic membrane potential. Under the assumption of Poisson firing statistics, the synaptic update rule exhibits all of the features of the Bienenstock-Cooper-Munro rule, in particular, regimes of synaptic potentiation and depression separated by a sliding threshold. Moreover, the learning rule is also applicable to the more realistic case of neuron models with refractoriness, and is sensitive to correlations between input spikes, even in the absence of presynaptic rate modulation. The learning rule is found by maximizing the mutual information between presynaptic and postsynaptic spike trains under the constraint that the postsynaptic firing rate stays close to some target firing rate. An interpretation of the synaptic update rule in terms of homeostatic synaptic processes and spike-timing-dependent plasticity is discussed. 10.1073/pnas.0500495102 KW - bayesian KW - stdp AU - Toyoizumi, Taro AU - Pfister, Jean-Pascal AU - Aihara, Kazuyuki AU - Gerstner, Wulfram PY - 2005/04/05/ UR - http://dx.doi.org/10.1073/pnas.0500495102 ER -