TY  - BOOK
AB  - The ongoing motor output of the brain depends on its remarkable ability to rapidly transform and fuse a variety of sensory streams in real-time. The brain processes these data using networks of neurons that communicate by asynchronous spikes, a technology that is dramatically different from conventional electronic systems. We report here a step towards constructing electronic systems with analogous performance to the brain. Our VLSLI spiking neural network combines in real-time three distinct sources of input data; each is place-encoded on an individual neuronal population that expresses soft Winner-Take-All dynamics. These arrays are combined according to a user-specified function that is embedded in the reciprocal connections between the soft Winner-Take-All populations and an intermediate shared population. The overall network is able to perform function approximation (missing data can be inferred from the available streams) and cue integration (when all input streams are present they enhance one another synergistically). The network performs these tasks with about 80% and 90% reliability, respectively. Our results suggest that with further technical improvement, it may be possible to implement more complex probabilistic models such as Bayesian networks in neuromorphic electronic systems.
DA  - 2012
DO  - 10.1109/ISCAS.2012.6271788
LA  - eng
PY  - 2012
SN  - 978-1-4673-0218-0
TI  - Real-time inference in a VLSI spiking neural network
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-24735102
Y2  - 2024-11-22T04:40:34
ER  -