title: Computing with networks of spiking neurons on a biophysically motivated floating-gate based neuromorphic integrated circuit.
abstract: Results are presented from several spiking network experiments performed on a novel neuromorphic integrated circuit. The networks are discussed in terms of their computational significance, which includes applications such as arbitrary spatiotemporal pattern generation and recognition, winner-take-all competition, stable generation of rhythmic outputs, and volatile memory. Analogies to the behavior of real biological neural systems are also noted. The alternatives for implementing the same computations are discussed and compared from a computational efficiency standpoint, with the conclusion that implementing neural networks on neuromorphic hardware is significantly more power efficient than numerical integration of model equations on traditional digital hardware.
authors.affiliation: Georgia Institute of Technology, Technology Square Research Building, Atlanta, GA 30308, USA. Stephen.Brink@gatech.edu
authors.forename: S
authors.initials: S
authors.lastname: Brink
authors.forename: S
authors.initials: S
authors.lastname: Nease
authors.forename: P
authors.initials: P
authors.lastname: Hasler
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001703:Biophysics; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology
keywords: Floating-gate transistor; Neuromorphic VLSI; Single transistor learning synapse; Spiking winner-take-all; Synfire chain