Abstract
Latency reduction in postsynaptic spikes is a well-known effect of spiking time-dependent plasticity. We expand this notion for long postsynaptic spike trains on single neurons, showing that, for a fixed input spike train, STDP reduces the number of postsynaptic spikes and concentrates the remaining ones. Then, we study the consequences of this phenomena in terms of coding, finding that this mechanism improves the neural code by increasing the signal-to-noise ratio and lowering the metabolic costs of frequent stimuli. Finally, we illustrate that the reduction in postsynaptic latencies can lead to the emergence of predictions.
Highlights
Living organisms need to make accurate predictions in order to survive (Bubic et al 2010; Hohwy 2013), posing the question of how do brains learn to make those predictions
We provide an interpretation of this spike concentration in terms of neural code performance, showing that it leads to lower number of spikes and synchronization
LIF neurons do not exhibit the rich range of dynamics that real neurons possess(Izhikevich 2004), ion channel kinetics are more complicated than simple Dirac deltas (Chapeau-Blondeau and Chambet 1995) and the spiking time-dependent plasticity (STDP) model used here cannot account for the evolution of synaptic weights when the frequency of postsynaptic or presynaptic spikes are high (Pfister and Gerstner 2006)
Summary
Living organisms need to make accurate predictions in order to survive (Bubic et al 2010; Hohwy 2013), posing the question of how do brains learn to make those predictions. We present such a mechanism by focusing on postsynaptic latency reduction This is a well-known effect of spiking time-dependent plasticity (STDP) first mentioned by Song et al (2000) for a single postsynaptic neuron driven by a specific excitatory input pattern. This effect was explored in detail in a simulation study by Guyonneau et al (2005) who showed that the latency reduction in the target neuron’s firing time is robust to fluctuations in presyanptic input in the form of jitter and Poissonian background noise.
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