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Model architecture for associative memory in a neural network of spiking neurons

Author

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  • Agnes, Everton J.
  • Erichsen, Rubem
  • Brunnet, Leonardo G.

Abstract

A synaptic connectivity model is assembled on a spiking neuron network aiming to build up a dynamic pattern recognition system. The connection architecture includes gap junctions and both inhibitory and excitatory chemical synapses based on Hebb’s hypothesis. The network evolution resulting from external stimulus is sampled in a properly defined frequency space. Neurons’ responses to different current injections are mapped onto a subspace using Principal Component Analysis. Departing from the base attractor, related to a quiescent state, different external stimuli drive the network to different fixed points through specific trajectories in this subspace.

Suggested Citation

  • Agnes, Everton J. & Erichsen, Rubem & Brunnet, Leonardo G., 2012. "Model architecture for associative memory in a neural network of spiking neurons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 843-848.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:3:p:843-848
    DOI: 10.1016/j.physa.2011.08.036
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    References listed on IDEAS

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    1. Agnes, E.J. & Erichsen, R. & Brunnet, L.G., 2010. "Synchronization regimes in a map-based model neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 651-658.
    2. Stuart Firestein, 2001. "How the olfactory system makes sense of scents," Nature, Nature, vol. 413(6852), pages 211-218, September.
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    Cited by:

    1. Lin, Qianjin & Wang, Jiang & Yang, Shuangming & Yi, Guosheng & Deng, Bin & Wei, Xile & Yu, Haitao, 2017. "The dynamical analysis of modified two-compartment neuron model and FPGA implementation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 199-214.
    2. Mizusaki, Beatriz E.P. & Agnes, Everton J. & Erichsen, Rubem & Brunnet, Leonardo G., 2017. "Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 279-286.

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