document: Scalable hybrid computation with spikes We outline a hybrid analog-digital scheme for computing with three important features that enable it to scale to systems of large complexity : First , like digital computation , which uses several one-bit precise logical units to collectively compute a precise answer to a computation , the hybrid scheme uses several moderate-precision analog units to collectively compute a precise answer to a computation . Second , frequent discrete signal restoration of the analog information prevents analog noise and offset from degrading the computation . Third , a state machine enables complex computations to be created using a sequence of elementary computations . A natural choice for implementing this hybrid scheme is one based on spikes because spike-count codes are digital , while spike-time codes are analog . We illustrate how spikes afford easy ways to implement all three components of scalable hybrid computation . First , as an important example of distributed analog computation , we show how spikes can create a distributed modular representation of an analog number by implementing digital carry interactions between spiking analog neurons . Second , we show how signal restoration may be performed by recursive spike-count quantization of spike-time codes . Third , we use spikes from an analog dynamical system to trigger state transitions in a digital dynamical system , which reconfigures the analog dynamical system using a binary control vector ; such feedback interactions between analog and digital dynamical systems create a hybrid state machine -LRB- HSM -RRB- . The HSM extends and expands the concept of a digital finite-state-machine to the hybrid domain . We present experimental data from a two-neuron HSM on a chip that implements error-correcting analog-to-digital conversion with the concurrent use of spike-time and spike-count codes . We also present experimental data from silicon circuits that implement HSM-based pattern recognition using spike-time synchrony . We outline how HSMs may be used to perform learning , vector quantization , spike pattern recognition and generation , and how they may be reconfigured
doc_bio_tags: B I I O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O B I I O O O O O O O O B I I O O O O B O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B I I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O B O B I O O B I O O O O O O O O O
extractive_keyphrases: scalable hybrid computation spikes frequent discrete signal restoration analog noise distributed analog computation digital carry interactions binary control vector feedback interactions silicon circuits pattern recognition learning vector quantization
abstractive_keyphrases: hybrid analog-digital scheme moderate-precision analog units spike-count codes finite-state-machine spike-time codes two neuron hybrid state machine error-correcting analog-to-digital conversion