Analog Integrated Circuits and Signal Processing
An asynchronous spiking chaotic neuron integrated circuit
Neurocomputing
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We propose an adaptive procedure that enables a spiking neuron, whether artificial or biological, to make optimal use of its dynamic range and gain. We discuss an analog electronic circuit implementation of this algorithm using a biologically realistic artificial “silicon” neuron. The adaptation procedure adapts the neuron's firing threshold and the sensitivity (or gain) of its current-frequency relationship to match the DC offset (or mean) and the dynamic range (or variance) of the time-varying somatic input current. The neuron extracts the minimum and maximum levels of the reconstructed somatic current signals from the cell's own spike trains. These are used to regulate the somatic leak conductance in order to shift the somatic current-frequency relation and to adjust a calcium-activated potassium conductance to change the dynamic range of the cell's somatic current-frequency relationship. We report experimental data from a test neuron-built using analog subthreshold CMOS VLSI technology-that shows the expected behavior