title: A learning-enabled neuron array IC based upon transistor channel models of biological phenomena.
abstract: We present a single-chip array of 100 biologically-based electronic neuron models interconnected to each other and the outside environment through 30,000 synapses. The chip was fabricated in a standard 350 nm CMOS IC process. Our approach used dense circuit models of synaptic behavior, including biological computation and learning, as well as transistor channel models. We use Address-Event Representation (AER) spike communication for inputs and outputs to this IC. We present the IC architecture and infrastructure, including IC chip, configuration tools, and testing platform. We present measurement of small network of neurons, measurement of STDP neuron dynamics, and measurement from a compiled spiking neuron WTA topology, all compiled into this IC.
authors.affiliation: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-250, USA.
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
authors.forename: S
authors.initials: S
authors.lastname: Ramakrishnan
authors.forename: R
authors.initials: R
authors.lastname: Wunderlich
authors.forename: A
authors.initials: A
authors.lastname: Basu
authors.forename: B
authors.initials: B
authors.lastname: Degnan
mesh_terms: D001185:Artificial Intelligence; D016571:Neural Networks, Computer; D009474:Neurons; D013569:Synapses; D014173:Transistors, Electronic