Sensitivity Analysis for additive STDP rule
Spike Timing Dependent Plasticity (STDP) is a Hebbian like synaptic learning rule. The basis of STDP has strong experimental evidences and it depends on precise input and output spike timings. In this paper we show that under biologically plausible spiking regime, slight variability in the spike timing leads to drastically different evolution of synaptic weights when its dynamics are governed by the additive STDP rule.
Code (0)
등록된 구현이 없습니다.
Tasks
SensitivitySimilar Papers 제목 키워드 기반
Fatiguing STDP: Learning from Spike-Timing Codes in the Presence of Rate Codes
Spiking neural networks (SNNs) could play a key role in unsupervised machine learning applications, by virtue of strengths related to learning from the fine temporal structure of event-based signals. However, some spike-…
A Neuromorphic VLSI Design for Spike Timing and Rate Based Synaptic Plasticity
Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is capable of reproducing the outcomes from a variety …
TripletOn the Spontaneous Dynamics of Synaptic Weights in Stochastic Models with Pair-Based STDP
We investigate spike-timing dependent plasticity (STPD) in the case of a synapse connecting two neural cells. We develop a theoretical analysis of several STDP rules using Markovian theory. In this context there are two …
Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks
Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware has the potential to significantly reduce the energy consumption of artificial neural network training. SNNs trained with Spike Timing-Dependent …
Triplet Spike Time Dependent Plasticity: A floating-gate Implementation
Synapse plays an important role of learning in a neural network; the learning rules which modify the synaptic strength based on the timing difference between the pre- and post-synaptic spike occurrence is termed as Spike…
Triplet