Advertisement

A Spiking Neural Network Model for Associative Memory Using Temporal Codes

  • Jun HuEmail author
  • Huajin Tang
  • Kay Chen Tan
  • Sen Bong Gee
Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 1)

Abstract

Associative memory is defined as the ability to map input patterns to output patterns. Understanding how human brain performs association between unrelated patterns and stores this knowledge is one of the most important goals in computational intelligence. Although this problem has been widely studied using conventional neural networks, increasing biological findings suggest that spiking neural network can be an alternative. The proposed model encodes different memories using different subsets of encoding neurons with temporal codes. A spike-timing based learning algorithm and spike-timing-dependent plasticity (STDP) are used to form associative memory. Simulation results show that hetero-associative memory and auto-associative memory are achievable by the synaptic modification of connections between input layer and hidden layers, and recurrent connections of hidden layers, respectively.

Keywords

Spiking Neural Networks (SNNs) associative memory Spike-Timing-Dependent Plasticity (STDP) temporal codes 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Durstewitz, D., Seamans, J.K., Sejnowski, T.J.: Neurocomputational models of working memory. Nature Neuroscience 3, 1184–1191 (2000)CrossRefGoogle Scholar
  2. 2.
    Mongillo, G., Barak, O., Tsodyks, M.: Synaptic theory of working memory. Science 319(5869), 1543–1546 (2008)CrossRefGoogle Scholar
  3. 3.
    Cutsuridis, V., Cobb, S., Graham, B.P.: Encoding and retrieval in a model of the hippocampal CA1 microcircuit. Hippocampus 20(3), 423–446 (2010)Google Scholar
  4. 4.
    Jensen, O., Idiart, M.A., Lisman, J.E.: Physiologically realistic formation of autoassociative memory in networks with theta/gamma oscillations: Role of fast NMDA channels. Learn & Memory 3, 243–256 (1996)CrossRefGoogle Scholar
  5. 5.
    Jensen, O.: Information transfer between rhythmically coupled networks: reading the hippocampal phase code. Neural Computation 13, 2743–2761 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Sommer, F.T., Wennekers, T.: Associative memory in networks of spiking neurons. Neural Networks 14(6-7), 825–834 (2001)CrossRefGoogle Scholar
  7. 7.
    Eichenbaum, H.: A cortical-hippocampal system for declarative memory. Nature Reviews Neuroscience 1, 41–50 (2000)CrossRefGoogle Scholar
  8. 8.
    Frankland, P.W., Bontempi, B.: The organization of recent and remote memories. Nature Reviews Neuroscience 6, 119–130 (2005)CrossRefGoogle Scholar
  9. 9.
    Wiltgen, B.J., Brown, R.A., Talton, L.E., Silva, A.J.: New circuits for old memories: the role of the neocortex in consolidation. Neuron 44, 101–108 (2004)CrossRefGoogle Scholar
  10. 10.
    Lin, L., Osan, R., Shoham, S., Jin, W., Zuo, W., Tsien, J.Z.: Identification of network-level coding units for real-time representation of episodic experiences in the hippocampus. PNAS 102(17), 6125–6130 (2005)CrossRefGoogle Scholar
  11. 11.
    Shadlen, M.N., Newsome, W.T.: Noise, neural codes and cortical organization. Current Opinion in Neurobiology 4, 569–579 (1994)CrossRefGoogle Scholar
  12. 12.
    Litvak, V., Sompolinsky, H., Segev, I., Abeles, M.: On the transmission of rate code in long feed-forward networks with excitatory-inhibitory balance. Journal of Neuroscience 23, 3006–3015 (2003)Google Scholar
  13. 13.
    Perrett, D.I., Rolls, E.T., Caan, W.: Visual neurones responsive to faces in the monkey temporal cortex. Experimental Brain Research 47(3), 329–342 (1982)CrossRefGoogle Scholar
  14. 14.
    Thorpe, S.J., Imbert, M.: Biological constraints on connectionist modelling. In: Connectionism in Perspective, pp.63–92. Elsevier (1989)Google Scholar
  15. 15.
    Carr, C.E.: Processing of temporal information in the brain. Annual Review of Neuroscience 16, 223–243 (1993)CrossRefGoogle Scholar
  16. 16.
    Gütig, R., Sompolinsky, H.: The tempotron: A neuron that learns spike timing-based decisions. Nature Neuroscience 9(3), 420–428 (2006)CrossRefGoogle Scholar
  17. 17.
    Bi, G.Q., Poo, M.M.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, & postsynaptic cell type. Journal of Neuroscience 18(24), 10464–10472 (1998)Google Scholar
  18. 18.
    Sjöström, P.G., Nelson, S.B.: Spike timing, calcium signals and synaptic plasticity. Current Opinion in Neurobiology 12(3), 305–314 (2002)CrossRefGoogle Scholar
  19. 19.
    Bear, M.F., Malenka, R.C.: Synaptic plasticity: LTP and LTD. Current Opinion in Neurobiology 4(3), 389–399 (1994)CrossRefGoogle Scholar
  20. 20.
    Malenka, R.C., Bear, M.F.: LTP and LTD: An embarrassment of riches. Neuron 44(1), 5–21 (2004)CrossRefGoogle Scholar
  21. 21.
    Schreiber, S., Fellous, J., Whitmer, D., Tiesinga, P., Sejnowski, T.: A new correlation-based measure of spike timing reliability. Neurocomputing 52-54, 925–931 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jun Hu
    • 1
    Email author
  • Huajin Tang
    • 2
  • Kay Chen Tan
    • 1
  • Sen Bong Gee
    • 1
  1. 1.Department of Electrical and Computing EngineeringNational University of SingaporeSingaporeSingapore
  2. 2.Institute for Infocomm Research Agency for ScienceTechnology and Research (A*STAR)SingaporeSingapore

Personalised recommendations