Large-Scale Spiking Neural Networks using Neuromorphic Hardware Compatible Models

Abstract

Neuromorphic engineering is a fast growing field with great potential in both understanding the function of the brain, and constructing practical artifacts that build upon this understanding. For these novel chips and hardware to be useful, hardware compatible applications and simulation tools are needed. We argue that the neural circuit approach, in which networks of neuronal elements model brain circuitry are constructed, allows the development of practical applications and the exploration of brain function. At this level of abstraction, networks of 10 5 neurons or larger can be efficiently simulated, but still preserve the neuronal and synaptic dynamics that appear to be important for brain function. Because the neural circuit level supports spiking neural networks and the prevalent Addressable Event Representation (AER) communication scheme, it fits well with many existing neuromorphic hardware and simulation tools. To show how this approach can be applied, we present case studies of spiking neural networks in vision and recognition tasks based on one instantiation of a simulation environment. However, there are now many hardware options, simulation environments, and applications in this emerging field. These approaches and other considerations are discussed.