Dynamic range and sensitivity adaptation in a silicon spiking neuron

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