A multichip aVLSI system emulating orientation selectivity of primary visual cortical cells

In this paper, we designed and fabricated a multichip neuromorphic analog very large scale integrated (aVLSI) system, which emulates the orientation selective response of the simple cell in the primary visual cortex. The system consists of a silicon retina and an orientation chip. An image, which is filtered by a concentric center-surround (CS) antagonistic receptive field of the silicon retina, is transferred to the orientation chip. The image transfer from the silicon retina to the orientation chip is carried out with analog signals. The orientation chip selectively aggregates multiple pixels of the silicon retina, mimicking the feedforward model proposed by Hubel and Wiesel. The chip provides the orientation-selective (OS) outputs which are tuned to 0 degrees, 60 degrees, and 120 degrees. The feed-forward aggregation reduces the fixed pattern noise that is due to the mismatch of the transistors in the orientation chip. The spatial properties of the orientation selective response were examined in terms of the adjustable parameters of the chip, i.e., the number of aggregated pixels and size of the receptive field of the silicon retina. The multichip aVLSI architecture used in the present study can be applied to implement higher order cells such as the complex cell of the primary visual cortex.

Orientation-selective aVLSI spiking neurons

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.