文件名称:inspired_by_axonal_delay
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Biological systems are able to recognise temporal sequences of stimuli or compute
in the temporal domain. In this paper we are exploring whether a biophysical
model of a pyramidal neuron can detect and learn systematic time
delays between the spikes from di erent input neurons. In particular, we investigate
whether it is possible to reinforce pairs of synapses separated by a
dendritic propagation time delay corresponding to the arrival time di erence
of two spikes from two di erent input neurons. We examine two subthreshold
learning approaches where the rst relies on the backpropagation of EPSPs (excitatory
postsynaptic potentials) and the second on the backpropagation of a
somatic action potential, whose production is supported by a learning-enabling
background current.
in the temporal domain. In this paper we are exploring whether a biophysical
model of a pyramidal neuron can detect and learn systematic time
delays between the spikes from di erent input neurons. In particular, we investigate
whether it is possible to reinforce pairs of synapses separated by a
dendritic propagation time delay corresponding to the arrival time di erence
of two spikes from two di erent input neurons. We examine two subthreshold
learning approaches where the rst relies on the backpropagation of EPSPs (excitatory
postsynaptic potentials) and the second on the backpropagation of a
somatic action potential, whose production is supported by a learning-enabling
background current.
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下载文件列表
| 文件名 | 大小 | 更新时间 |
|---|---|---|
| inspired_by_axonal_delay.pdf | 556790 | 2018-03-17 |