We present a cellular pulse coupled neural network with adaptive weights and its analog VLSI implementation.
The neural network operates on a scalar image feature, such as grey scale or the output of a spatial filter. It
detects segments and marks them with synchronous pulses of the corresponding neurons. The network consists
of integrate-and-fire neurons, which are coupled to their nearest neighbors via adaptive synaptic weights.
Adaptation follows either one of two empirical rules. Both rules lead to spike grouping in wave like patterns.
This synchronous activity binds groups of neurons and labels the corresponding image segments. Applications
of the network also include feature preserving noise removal, image smoothing, and detection of bright and dark
spots. The adaptation rules are insensitive for parameter deviations, mismatch and non-ideal approximation of
the implied functions. That makes an analog VLSI implementation feasible. Simulations showed no significant
differences in the synchronization properties between networks using the ideal adaptation rules and networks
resembling implementation properties such as randomly distributed parameters and roughly implemented adaptation
functions. A prototype is currently being designed and fabricated using an Infineon 130nm technology. It
comprises a 128 × 128 neuron array, analog image memory, and an address event representation pulse output.