Recurrent models of orientation selectivity enable robust early-vision processing in mixed-signal neuromorphic hardware.

Mixed signal analog/digital neuromorphic circuits represent an ideal medium for reproducing bio-physically realistic dynamics of biological neural systems in real-time. However, similar to their biological counterparts, these circuits have limited resolution and are affected by a high degree of variability. By developing a recurrent spiking neural network model of the retinocortical visual pathway, we show how such noisy and heterogeneous computing substrate can produce linear receptive fields tuned to visual stimuli with specific orientations and spatial frequencies. Compared to strictly feed-forward schemes, the model generates highly structured Gabor-like receptive fields of any phase symmetry, making optimal use of the hardware resources available in terms of synaptic connections and neuron numbers. Experimental results validate the approach, demonstrating how principles of neural computation can lead to robust sensory processing electronic systems, even when they are affected by high degree of heterogeneity, e.g., due to the use of analog circuits or memristive devices.