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Pairwise analysis can account for network structures arising from spike-timing dependent plasticity.
PLoS Computational Biology, 2013 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.
Baktash Babadi, L F Abbott
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In vivo spike-timing-dependent plasticity in the optic tectum of Xenopus laevis
Frontiers in Synaptic Neuroscience, 2010 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.
Blake A Richards +2 more
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Stability versus neuronal specialization for STDP: long-tail weight distributions solve the dilemma. [PDF]
PLoS ONE, 2011 Spike-timing-dependent plasticity (STDP) modifies the weight (or strength) of synaptic connections between neurons and is considered to be crucial for generating network structure. It has been observed in physiology that, in addition to spike timing, the
Matthieu Gilson, Tomoki Fukai
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Neuromorphic Learning towards Nano Second Precision [PDF]
, 2013 Temporal coding is one approach to representing information in spiking neural networks. An example of its application is the location of sounds by barn owls that requires especially precise temporal coding. Dependent upon the azimuthal angle, the arrival
Meier, Karlheinz +3 more
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An Adaptive STDP Learning Rule for Neuromorphic Systems [PDF]
Frontiers in Neuroscience, 2021 The promise of neuromorphic computing to develop ultra-low-power intelligent devices lies in its ability to localize information processing and memory storage in synaptic circuits much like the synapses in the brain. Spiking neural networks modeled using high-resolution synapses and armed with local unsupervised learning rules like spike time-dependent
Ashish Gautam, Takashi Kohno
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Frontiers in Synaptic Neuroscience, 2018 One of the most influential synaptic learning rules explored in the past decades is activity dependent spike-timing-dependent plasticity (STDP). In STDP, synapses are either potentiated or depressed based on the order of pre- and postsynaptic neuronal ...
Ludovic D. Langlois +2 more
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Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task. [PDF]
PLoS Computational Biology, 2017 Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks.
Pavel Sanda +2 more
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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective [PDF]
, 2019 On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential.
Abbott +56 more
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Optimal Resonances in Multiplex Neural Networks Driven by an STDP Learning Rule
Frontiers in Physics, 2022 In this paper, we numerically investigate two distinct phenomena, coherence resonance (CR) and self-induced stochastic resonance (SISR), in multiplex neural networks in the presence of spike-timing-dependent plasticity (STDP).
Marius E. Yamakou +5 more
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Design and Implementation of BCM Rule Based on Spike-Timing Dependent Plasticity
, 2012 The Bienenstock-Cooper-Munro (BCM) and Spike Timing-Dependent Plasticity (STDP) rules are two experimentally verified form of synaptic plasticity where the alteration of synaptic weight depends upon the rate and the timing of pre- and post-synaptic ...
Abbott, Derek +3 more
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