Taro Toyoizumi

Self-tuning of neural circuits through short-term synaptic plasticity (2008)

David Sussillo, Taro Toyoizumi, Wolfgang Maass

Numerous experimental data show that cortical networks of neurons are not silent in the absence of external inputs, but rather maintain a low spontaneous firing activity. This aspect of cortical...

CH-1015 Lausanne (2008)

Jean-pascal Pfister, Taro Toyoizumi, David Barber, Wulfram Gerstner

In timing-based neural codes, neurons have to emit action po-tentials at precise moments in time. We use a supervised learn-ing paradigm to derive a synaptic update rule that optimizes via gradient...

Optimality Model of Unsupervised Spike-Timing Dependent Plasticity: Synaptic Memory and Weight Distribution (2007)

Toyoizumi, Taro, Pfister, Jean-Pascal, Aihara, Kazuyuki, Gerstner, Wulfram

We studied the hypothesis that synaptic dynamics is controlled by three basic principles: (1) synapses adapt their weights so that neurons can effectively transmit information, (2) homeostatic...

Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing (2005)

Pfister, Jean-Pascal, Toyoizumi, Taro, Barber, David, Gerstner, Wulfram

In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes via gradient...

Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model (2005)

Toyoizumi, Taro, Pfister, Jean-Pascal, Aihara, Kazuyuki, Gerstner, Wulfram

We derive an optimal learning rule in the sense of mutual information maximization for a spiking neuron model. Under the assumption of small fluctuations of the input, we find a spike-timing...

Spike-timing dependent plasticity and mutual information maximization for a spiking neuron model (2005)

Taro Toyoizumi, Jean-pascal Pfister, Kazuyuki Aihara, Wulfram Gerstner

We derive an optimal learning rule in the sense of mutual information maximization for a spiking neuron model. Under the assumption of small fluctuations of the input, we find a spike-timing...

Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing in Supervised Leraning (2004)

Pfister, Jean-Pascal, Toyoizumi, Taro, Barber, David, Gerstner, Wulfram

In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes via gradient...

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

Toyoizumi, Taro, Pfister, Jean-Pascal, Aihara, Kazuyuki, Gerstner, Wulfram

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...