Abstract
A detailed compartmental model of a cortical pyramidal cell is used to determine the effect of the spatial distribution of synapses across a dendritic tree on the pattern recognition capability of the neuron. By setting synaptic strengths according to the clipped Hebbian learning rule used in the associative net neural network model, the cell is able to recognise input patterns, but with a one to two order of magnitude decrease in performance compared to the computing units in the network model. Performance of the cell is optimised by particular forms of input signal, but is not altered by different pattern recognition criteria.
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© 1997 Springer-Verlag Berlin Heidelberg
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Graham, B., Willshaw, D. (1997). A model of clipped hebbian learning in a neocortical pyramidal cell. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020148
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DOI: https://doi.org/10.1007/BFb0020148
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