Spike Based Learning Application For Neuromorphic Engineering

Spike-based learning application for neuromorphic engineering

Spiking Neural Networks (SNN) closely imitate biological networks. Information processing occurs in both spatial and temporal manner, making SNN extremely interesting for the pertinent mimicking of the biological brain. Biological brains code and transmit the sensory information in the form of spikes that capture the spatial and temporal information of the environment with amazing precision. This information is processed in an asynchronous way by the neural layer performing recognition of complex spatio-temporal patterns with sub-milliseconds delay and at with a power budget in the order of 20W. The efficient spike coding mechanism and the asynchronous and sparse processing and communication of spikes seems to be key in the energy efficiency and high-speed computation capabilities of biological brains. SNN low-power and event-based computation make them more attractive when compared to other artificial neural networks (ANN).

Spiking Neural Network Learning, Benchmarking, Programming and Executing

Neuromorphic Engineering Systems and Applications

Neuromorphic engineering has just reached its 25th year as a discipline. In the first two decades neuromorphic engineers focused on building models of sensors, such as silicon cochleas and retinas, and building blocks such as silicon neurons and synapses. These designs have honed our skills in implementing sensors and neural networks in VLSI using analog and mixed mode circuits. Over the last decade the address event representation has been used to interface devices and computers from different designers and even different groups. This facility has been essential for our ability to combine sensors, neural networks, and actuators into neuromorphic systems. More recently, several big projects have emerged to build very large scale neuromorphic systems. The Telluride Neuromorphic Engineering Workshop (since 1994) and the CapoCaccia Cognitive Neuromorphic Engineering Workshop (since 2009) have been instrumental not only in creating a strongly connected research community, but also in introducing different groups to each other’s hardware. Many neuromorphic systems are first created at one of these workshops. With this special research topic, we showcase the state-of-the-art in neuromorphic systems.