Neural Comput - A systematic method for configuring VLSI networks of spiking neurons.

Tópicos

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Resumo

An increasing number of research groups are developing custom hybrid analog/digital very large scale integration (VLSI) chips and systems that implement hundreds to thousands of spiking neurons with biophysically realistic dynamics, with the intention of emulating brainlike real-world behavior in hardware and robotic systems rather than simply simulating their performance on general-purpose digital computers. Although the electronic engineering aspects of these emulation systems is proceeding well, progress toward the actual emulation of brainlike tasks is restricted by the lack of suitable high-level configuration methods of the kind that have already been developed over many decades for simulations on general-purpose computers. The key difficulty is that the dynamics of the CMOS electronic analogs are determined by transistor biases that do not map simply to the parameter types and values used in typical abstract mathematical models of neurons and their networks. Here we provide a general method for resolving this difficulty. We describe a parameter mapping technique that permits an automatic configuration of VLSI neural networks so that their electronic emulation conforms to a higher-level neuronal simulation. We show that the neurons configured by our method exhibit spike timing statistics and temporal dynamics that are the same as those observed in the software simulated neurons and, in particular, that the key parameters of recurrent VLSI neural networks (e.g., implementing soft winner-take-all) can be precisely tuned. The proposed method permits a seamless integration between software simulations with hardware emulations and intertranslatability between the parameters of abstract neuronal models and their emulation counterparts. Most important, our method offers a route toward a high-level task configuration language for neuromorphic VLSI systems.

Resumo Limpo

increas number research group develop custom hybrid analogdigit larg scale integr vlsi chip system implement hundr thousand spike neuron biophys realist dynam intent emul brainlik realworld behavior hardwar robot system rather simpli simul perform generalpurpos digit comput although electron engin aspect emul system proceed well progress toward actual emul brainlik task restrict lack suitabl highlevel configur method kind alreadi develop mani decad simul generalpurpos comput key difficulti dynam cmos electron analog determin transistor bias map simpli paramet type valu use typic abstract mathemat model neuron network provid general method resolv difficulti describ paramet map techniqu permit automat configur vlsi neural network electron emul conform higherlevel neuron simul show neuron configur method exhibit spike time statist tempor dynam observ softwar simul neuron particular key paramet recurr vlsi neural network eg implement soft winnertakeal can precis tune propos method permit seamless integr softwar simul hardwar emul intertranslat paramet abstract neuron model emul counterpart import method offer rout toward highlevel task configur languag neuromorph vlsi system

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