Dynamical properties of neural network model for working memory with Hodgkin–Huxley neurons

✍ Scribed by Toshiaki Omori; Tsuyoshi Horiguchi

• Publisher: Elsevier Science
• Year: 2004
• Tongue: English
• Weight: 423 KB
• Volume: 334
• Category: Article
• ISSN: 0378-4371
• DOI: 10.1016/j.physa.2003.11.022

✦ Synopsis

We propose a neural network model of working memory with one-compartmental neurons and investigate its dynamical properties. We assume that the model consists of excitatory neurons and inhibitory neurons; all the neurons are connected to each other. The excitatory neurons are distinguished as several groups of selective neurons and one group of non-selective neurons. The selective neurons are assumed to form subpopulations in which each selective neuron belongs to only one of subpopulations. The non-selective neurons are assumed not to form any subpopulation. Synaptic strengths between neurons within a subpopulation are assumed to be potentiated. By the numerical simulations, persistent ÿring of neurons in a subpopulation emerges; the persistent ÿring corresponds to the retention of memory as one of the functions of working memory. We ÿnd that the strength of external input and the strength of N -methyl-D-aspartate synapse are important factors for dynamical behaviors of the network; for example, if we enhance the strength of the external input to a subpopulation while the persistent ÿring is occurring in other subpopulation, the persistent ÿring occurs in the subpopulation or is sustained against the external input. These results reveal that the neural network as for the function of the working memory is controlled by the neuromodulation and the external stimuli within the proposed model. We also ÿnd that the persistent time of ÿring of the selective neurons shows a kind of phase transition as a function of the degree of potentiation of synapses.