Biophysical Models of Neural Computation: Max and Tuning Circuits

Pooling under a softmax operation and Gaussian-like tuning in the form of a normalized dot-product were proposed as the key operations in a recent model of object recognition in the ventral stream of visual cortex. We investigate how these two operations might be implemented by plausible circuits of a few hundred neurons in cortex. We consider two different sets of circuits whose different properties may correspond to the conditions in visual and barrel cortices, respectively. They constitute a plausibility proof that stringent timing and accuracy constraints imposed by the neuroscience of object recognition can be satisfied with standard spiking and synaptic mechanisms. We provide simulations illustrating the performance of the circuits, and discuss the relevance of our work to neurophysiology as well as what bearing it may have on the search for maximum and tuning circuits in cortex.

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