Handbook of surface and interface science by K Wandelt

By K Wandelt

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Specifically, simple synaptic models modulate incoming signals through externally programmable weighting schemes, thus making the learning circuitry external to the neuron. These synaptic voltages can be modulated using either externally programmable digital-to-analog voltages or externally controllable voltages for an operational transconductance amplifier. These circuits are typically configured off-chip and appropriate when fixed neural spiking rhythms are used, such as to emulate a central pattern generator (see Chapter 9 for details on these implementations).

88 (2), 769–840. V. and Markram, H. (1997) The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc. Natl. Acad. Sci. USA, 94 (2), 719–723. , and Markram, H. (2000) Synchrony generation in recurrent networks with frequency-dependent synapses. J. , 20 (1), RC50. F. (2000) Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. , 3 (9), 919–926. R. V. (2002) A model of spike-timing dependent plasticity: one or two coincidence detectors?

3e). Indiveri et al. [21, 22] also use source followers to increase their linear integration range. More recently, Folowosele et al. [23] reported on a different kind of integrate-andfire neuron based on the Mihalas-Niebur model [24] and implemented using an adaptive threshold and a switched capacitor-based architecture to implement the model’s parameters. Using the switched capacitor methodology instead of depending on the subthreshold transistor characteristics (as is typically the case in the neuromorphic engineering community) allows for better controllability, matching, and reproducibility of the neuron’s behavior.

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