Abstract
We analyse in detail the statistical properties of a “canonical” integrate-and-fire neuron with a linear integrator as often used in VLSI implementations [1]. We show that a network of such elements can maintain both stable spontaneous activity and selective (stimulus specific) activity, contrary to current opinion. The spike statistics appears to be qualitatively the same as in networks of conventional (exponential) integrate-and-fire neurons that in turn, exhibit a wide variety of characteristics observed in cortical recordings[2].
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© 1997 Springer-Verlag Berlin Heidelberg
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Mattia, M., Fusi, S. (1997). Modeling networks with linear (VLSI) integrate-and-fire neurons. 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/BFb0020134
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DOI: https://doi.org/10.1007/BFb0020134
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