Border ownership in a nano-neuromorphic circuit using nonlinear dendritic computations

We present an electronic cortical neuron with nonlinear dendritic computations that performs border-ownership assignment. The circuit is simulated using a carbon nanotube field-effect transistor SPICE model. We demonstrate that our neuron can distinguish convex and concave contours, selectively respond to a figure based on the contour and/or disparity cues, and transform this neural information into a border-ownership signal. We demonstrate that a dendritic spike is key to contour detection and increases the robustness of border-ownership neuron firing. We construct an example hierarchical neural network that includes feedforward excitation and lateral inhibition to enhance border-ownership assignment.