Name
Abstract | ||
|---|---|---|
A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as input-driven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing software-based event-driven approaches. As an alternative we have designed an efficient time-based computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/TNN.2006.875980 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
reconfigurable hardware,spiking neuron,real time system,biologically realistic model,spiking neural network hardware.,neuron model,real-time computing platform,computing platform,spike response model,efficient time-based computing architecture,hardware component,temporal dynamic,pipeline pro- cessing,index terms—field-programmable gate arrays,synaptic integration process,network model,real time,neural nets,real time computing,spiking neural network,software component,field programmable gate arrays,hardware,indexing terms,field programmable gate array,real time systems,time constant,energy management,concurrent computing,computer networks,computational modeling,real time control,computer architecture | Biological neuron model,Computer science,Parallel algorithm,Real-time computing,Software,Artificial intelligence,Concurrent computing,Component-based software engineering,Machine learning,Network model,Reconfigurable computing,Scalability | Journal |
Volume | Issue | ISSN |
17 | 4 | 1045-9227 |
Citations | PageRank | References |
49 | 2.63 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eduardo Ros | 1 | 1100 | 86.00 |
| E. M. Ortigosa | 2 | 194 | 11.60 |
| Rodrigo Agís | 3 | 154 | 13.25 |
| Richard R. Carrillo | 4 | 221 | 16.45 |
| Michael Arnold | 5 | 49 | 2.63 |