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**CMOL implementation of spiking neurons and spike-timing dependent plasticity.**
*(English)*
Zbl 1221.68187

Summary: Successful implementation of spiking neural networks onto CMOS-Molecular (CMOL) architecture has already been proposed, but the ability of dynamic learning has not yet been addressed. Here, we propose a spiking neural topology with spike-timing-dependent learning ability and provide its basic building blocks that are easily mapped onto CMOL architecture. The learning method modifies state of synaptic switches, using spatially and temporally local information which is available at the synapse when state modification is performed.

The performance of the proposed topology is analyzed with regards to pre- and post-synaptic spike timing, and simulation results are provided for a synapse with spike-timing-dependent plasticity properties. Furthermore, its performance as spike-timing correlation learning and synchrony detection in a small feed-forward network is demonstrated as a case example.

The performance of the proposed topology is analyzed with regards to pre- and post-synaptic spike timing, and simulation results are provided for a synapse with spike-timing-dependent plasticity properties. Furthermore, its performance as spike-timing correlation learning and synchrony detection in a small feed-forward network is demonstrated as a case example.

### MSC:

68T05 | Learning and adaptive systems in artificial intelligence |

### Software:

CrossNets
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\textit{A. Afifi} et al., Int. J. Circuit Theory Appl. 39, No. 4, 357--372 (2011; Zbl 1221.68187)

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