STDP with synaptic fatigue for learning of spike-time-coded patterns in the presence of parallel rate-coding

• Main Authors: Moraitis, Timoleon, Koelmans, Wabe W, Tuma, Tomas, Sebastian, Abu
• Format: Patent
• Language: English
• Published: 28.03.2023
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; INFORMATION STORAGE; PHYSICS; STATIC STORES

A circuit implementing a spiking neural network that includes a learning component that can learn from temporal correlations in the spikes regardless of correlations in the rates. In some embodiments, the learning component comprises a rate-discounting component. In some embodiments, the learning rule computes a rate-normalized covariance (normcov) matrix, detects clusters in this matrix, and sets the synaptic weights according to these clusters. In some embodiments, a synapse with a long-term plasticity rule has an efficacy that is composed by a weight and a fatiguing component. In some embodiments, A Hebbian plasticity component modifies the weight component and a short-term fatigue plasticity component modifies the fatiguing component. The fatigue component increases with increases in the presynaptic spike rate. In some embodiments, the fatigue component increases are implemented in a spike-based manner. In some embodiments, the Hebbian plasticity is a spike-timing-dependent plasticity (STDP), resulting in a fatiguing STDP (FSTDP) synapse.