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Spiking Neuron Based Cognitive Memory Model

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Neuromorphic Cognitive Systems

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 126))

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Abstract

Jensen et al. (Learn. Mem. 3(2–3), 245–246, 1996 [1]) proposed an auto-associative memory model using an integrated short-term memory (STM) and long-term memory (LTM) spiking neural network. Their model requires that distinct pyramidal cells encoding different STM patterns are fired in different high-frequency gamma subcycles within each low-frequency theta oscillation. Auto-associative LTM is formed by modifying the recurrent synaptic efficacy between pyramidal cells. In order to store auto-associative LTM correctly, the recurrent synaptic efficacy must be bounded. The synaptic efficacy must be upper bounded to prevent re-firing of pyramidal cells in subsequent gamma subcycles. If cells encoding one memory item were to re-fire synchronously with other cells encoding another item in subsequent gamma subcycle, LTM stored via modifiable recurrent synapses would be corrupted. The synaptic efficacy must also be lower bounded so that memory pattern completion can be performed correctly. This chapter uses the original model by Jensen et al. as the basis to illustrate the following points. Firstly, the importance of coordinated long-term memory (LTM) synaptic modification. Secondly, the use of a generic mathematical formulation (spiking response model) that can theoretically extend the results to other spiking network utilizing threshold-fire spiking neuron model. Thirdly, the interaction of long-term and short-term memory networks that possibly explains the asymmetric distribution of spike density in theta cycle through the merger of STM patterns with interaction of LTM network.

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Author information

Authors and Affiliations

  1. Institute for Infocomm Research, Singapore, Singapore

    Qiang Yu

  2. College of Computer Science, Sichuan University, Chengdu, China

    Huajin Tang

  3. AGI Technologies, Singapore, Singapore

    Jun Hu

  4. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Kay Chen Tan

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  1. Qiang Yu
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  2. Huajin Tang
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Correspondence to Qiang Yu .

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Yu, Q., Tang, H., Hu, J., Tan, K. (2017). Spiking Neuron Based Cognitive Memory Model. In: Neuromorphic Cognitive Systems. Intelligent Systems Reference Library, vol 126. Springer, Cham. https://doi.org/10.1007/978-3-319-55310-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-55310-8_8

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