Emergence of Gabor-like Receptive Fields in a Recurrent Network of Mixed-Signal Silicon Neurons

Abstract

Mixed signal analog/digital neuromorphic circuits offer an ideal computational substrate for testing and validating hypotheses about models of sensory processing, as they are affected by low resolution, variability, and other limitations that affect in a similar way real neural circuits. In addition, their real-time response properties allow to test these models in closed-loop sensory-processing hardware setups and to get an immediate feedback on the effect of different parameter settings. Within this context we developed a recurrent neural network architecture based on a model of the retinocortical visual pathway to obtain neurons highly tuned to oriented visual stimuli along a specific direction and with a specific spatial frequency, with Gabor-like receptive fields. The computation performed by the retina is emulated by a Dynamic Vision Sensor (DVS) while the following feed-forward and recurrent processing stages are implemented by a Dynamic Neuromorphic Asynchronous Processor (DYNAP) chip that comprises adaptive integrate-and fire neurons and dynamic synapses. We show how the network implemented on this device gives rise to neurons tuned to specific orientations and spatial frequencies, independent of the temporal frequency of the visual stimulus. Compared to alternative feed-forward schemes, the model proposed produces highly structured receptive fields with a limited number of synaptic connections, thus optimizing hardware resources. We validate the model and approach proposed with experimental results using both synthetic and natural images.