| |
Abstract:
We describe a programmable multi-chip VLSI neuronal system
that can be used for exploring spike-based information processing
models. The system consists of a silicon retina, a PIC
microcontroller, and a transceiver chip whose integrate-and-fire
neurons are connected in a soft winner-take-all architecture. The
circuit on this multi-neuron chip approximates a cortical
microcircuit. The neurons can be configured for different
computational properties by the virtual connections of a selected
set of pixels on the silicon retina. The virtual wiring between
the different chips is effected by an event-driven communication
protocol that uses asynchronous digital pulses, similar to spikes
in a neuronal system. We used the multi-chip spike-based system
to synthesize orientation-tuned neurons using both a feedforward
model and a feedback model. The performance of our analog
hardware spiking model matched the experimental observations and
digital simulations of continuous-valued neurons. The multi-chip
VLSI system has advantages over computer neuronal models in that
it is real-time, and the computational time does not scale with
the size of the neuronal network.
|
