Analogue Circuits of a Learning Spiking Neuron Model

Biological neurons communicate via sequences of calibrated pulses or spikes. The behavior of spiking neurons is the following: input spikes from pre-synaptic neurons are weighted and summed up yielding a value called membrane potential. The membrane potential is time dependent and decays when no spikes are received by the neuron. If however spikes excite the membrane potential sufficiently so that it exceeds a certain threshold, a spike is emitted and transmitted through its axon via synapses to other neurons. After the emission of a spike, the neuron is unable to spike again for a certain period called refractory period. Recently, a new theoretical formulation has been proposed by Gerstner [1]. The computational power of neural networks based on temporal coding by spikes, rather than on the traditional interpretation of analogue variables, has been investigated by Maass [2]. It is shown that simple operations on phase-differences between spike-trains provide a powerful computational tool.