The viability of spike-timing-dependent plasticity (STDP) to explain learning processes is controversial, although recent developments of reward-modulated STDP (RM-STDP) models provide a plausible substrate. However, evidence has also emerged to show that rewards themselves can modify the STDP rule. In this modeling study, we use a dynamic STDP rule to show that such modification can lead to ne...

Spike-Timing Dependent Plasticity (STDP) is characterized by a wide range of temporal kernels. However, much of the theoretical work has focused on a specific kernel - the "temporally asymmetric Hebbian" learning rules. Previous studies linked excitatory STDP to positive feedback that can account for the emergence of response selectivity. Inhibitory plasticity was associated with negative feedb...

Penelitian ini bertujuan untuk menganalisis implementasi Sosialisasi Teknik Dasar Promosi (STDP) dalam kampanye kepedulian lingkungan "Berbagi Kebaikan dengan Sebarkan Kebersihan" di Universitas Paramadina. Metode penelitian yang digunakan adalah deskriptif kualitatif menggunakan studi kepustakaan. Hasil menunjukkan bahwa STDP menjadi proses kunci mencapai tujuan kampanye. Proses terdiri dari s...

Machine learning can be effectively applied in control loops to make optimal decisions robustly. There is increasing interest using spiking neural networks (SNNs) as the apparatus for machine engineering because SNNs potentially offer high energy efficiency, and new SNN-enabling neuromorphic hardware being rapidly developed. A defining characteristic of problems that environmental reactions del...

Spike-timing-dependent plasticity (STDP) has been observed in many brain areas such as sensory cortices, where it is hypothesized to structure synaptic connections between neurons. Previous studies have demonstrated how STDP can capture spiking information at short timescales using specific input configurations, such as coincident spiking, spike patterns and oscillatory spike trains. However, t...

Spike timing-dependent plasticity (STDP) modifies synaptic strengths based on timing information available locally at each synapse. Despite this, it induces global structures within a recurrently connected network. We study such structures both through simulations and by analyzing the effects of STDP on pair-wise interactions of neurons. We show how conventional STDP acts as a loop-eliminating ...

Spike-timing dependent plasticity (STDP), a widespread synaptic modification mechanism, is sensitive to correlations between presynaptic spike trains and it generates competition among synapses. However, STDP has an inherent instability because strong synapses are more likely to be strengthened than weak ones, causing them to grow in strength until some biophysical limit is reached. Through sim...

Abstract Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) is a learning method for Spiking Neural Network (SNN) that makes use of an external signal to modulate the synaptic plasticity produced by (STDP). Combining advantages reinforcement and biological plausibility STDP, online on SNN in real-world scenarios can be applied. This paper presents fully digital architecture, implemente...

The spike-timing-dependent plasticity (STDP), a synaptic learning rule for encoding learning and memory, relies on relative timing of neuronal activity on either side of the synapse. GABAergic signaling has been shown to control neuronal excitability and consequently the spike timing, but whether GABAergic circuits rule the STDP remained unknown. Here we show that GABAergic signaling governs th...

Spike-timing-dependent plasticity (STDP) is found in vivo in a variety of systems and species, but the first demonstrations of in vivo STDP were carried out in the optic tectum of Xenopus laevis embryos. Since then, the optic tectum has served as an excellent experimental model for studying STDP in sensory systems, allowing researchers to probe the developmental consequences of this form of syn...