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23 papers · ranked by Valyu relevance

01
PPubMed26 Feb 2023
Rapid learning of predictive maps with STDP and theta phase precession
Tom M George, William de Cothi, Kimberly L Stachenfeld, Caswell Barry + 2 more
'Caswell Barry' 'Michael J Frank' 'Michael J Frank'] The predictive map hypothesis is a promising candidate principle for hippocampal function. A favoured formalisation of this hypothesis, called the successor representation, proposes that each place cell encodes the expected state occupancy of its target location in…
87relevance
02
AarXiv22 Aug 2025
Spike Agreement Dependent Plasticity: A scalable Bio-Inspired learning paradigm for Spiking Neural Networks
Bej, Saptarshi, E, Muhammed Sahad + 8 more
We introduce Spike Agreement Dependent Plasticity (SADP), a biologically inspired synaptic learning rule for Spiking Neural Networks (SNNs) that relies on the agreement between pre- and post-synaptic spike trains rather than precise spike-pair timing. SADP generalizes classical Spike-Timing-Dependent Plasticity (STDP)…
86relevance
03
PPubMed16 Jul 2012
STDP encodes oscillation frequencies in the connections of recurrent networks of spiking neurons
Robert R Kerr, Anthony N Burkitt, Doreen A Thomas, David B Grayden
Spike-timing-dependent plasticity (STDP) is a learning rule that updates synaptic strengths based on the relative timing of pre- and post-synaptic spikes. Unlike rate-based Hebbian learning, STDP can potentially encode fast temporal correlations in neuronal activity, such as oscillations, in the functional structure of…
83relevance
04
PPubMed15 Oct 2018
On Practical Issues for Stochastic STDP Hardware With 1-bit Synaptic Weights
Amirreza Yousefzadeh, Evangelos Stromatias, Miguel Soto, Teresa Serrano-Gotarredona + 1 more
'Teresa Serrano-Gotarredona' 'Bernabé Linares-Barranco'] In computational neuroscience, synaptic plasticity learning rules are typically studied using the full 64-bit floating point precision computers provide. However, for dedicated hardware implementations, the precision used not only penalizes directly the required…
82relevance
05
BbioRxiv20 Nov 2021
Evolutionary and spike-timing-dependent reinforcement learning train spiking neuronal network motor control
Daniel Haşegan, Matt Deible, Christopher Earl, David D’Onofrio + 3 more
Despite being biologically unrealistic, artificial neural networks (ANNs) have been successfully trained to perform a wide range of sensory-motor behaviors. In contrast, the performance of more biologically realistic spiking neuronal network (SNN) models trained to perform similar behaviors remains relatively…
82relevance
06
PPubMed19 Jan 2016
Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules
Nicolas Frémaux, Wulfram Gerstner
Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulators on synaptic plasticity is useful not just for action learning in classical…
79relevance
07
AarXiv3 Nov 2017
ASP: Learning to Forget With Adaptive Synaptic Plasticity in Spiking Neural Networks
Priyadarshini Panda, Jason M. Allred, Shriram Ramanathan, Kaushik Roy
'Kaushik Roy'] Abstract—A fundamental feature of learning in animals is the "ability to forget" that allows an organism to perceive, model and make decisions from disparate streams of information and adapt to changing environments. Against this backdrop, we present a novel unsupervised learning mechanism ASP (Adaptive…
79relevance
08
PPubMed29 Sep 2017
Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task Multi-layer network utilizing rewarded STDP to learn a foraging task
Pavel Sanda, Steven Skorheim, Maxim Bazhenov, Abigail Morrison
Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both…
78relevance
09
PPubMed7 Feb 2013
Delay Selection by Spike-Timing-Dependent Plasticity in Recurrent Networks of Spiking Neurons Receiving Oscillatory Inputs Delay Selection by STDP with Oscillatory Inputs
Robert R. Kerr, Anthony N. Burkitt, Doreen A. Thomas, Matthieu Gilson + 2 more
'Matthieu Gilson' 'David B. Grayden' 'Abigail Morrison'] Learning rules, such as spike-timing-dependent plasticity (STDP), change the structure of networks of neurons based on the firing activity. A network level understanding of these mechanisms can help infer how the brain learns patterns and processes information.…
77relevance
10
BbioRxiv15 Apr 2020
Synaptic learning rules for sequence learning
Eric T. Reifenstein, Richard Kempter
Remembering the temporal order of a sequence of events is a task easily performed by humans in everyday life, but the underlying neuronal mechanisms are unclear. This problem is particularly intriguing as human behavior often proceeds on a time scale of seconds, which is in stark contrast to the much faster millisecond…
77relevance
11
PPubMed18 Jul 2011
Dependence of spatial filtering by spike timing dependent synaptic plasticity on learning window
Kazuhisa Fujita
Spike timing dependent synaptic plasticity (STDP) plays an important role in temporal information processing. Whereas our result of the previous study showed that an interconnected network with STDP plays a role in spatial information processing [1]. The role is spatial filtering. In the present study, we studied the…
76relevance
12
AarXiv30 Jun 2020
Spiking Associative Memory for Spatio-Temporal Patterns.
Simon Davidson, Steve Furber, Oliver Rhodes
Recent successes in the application of neural networks have been in Artificial Neural Networks (ANNs) with deep structure and trained with back-propagation, inspired by neuroscience but very much an engineering abstraction. Traditional approaches more firmly rooted in the biology - with information flow encoded as…
75relevance
13
AarXiv6 Dec 2024
Computational models of learning and synaptic plasticity
Danil Tyulmankov
- Summarize the key models of synaptic plasticity and types of signals that can be used to update synaptic weights. - Provide an outline for organizing several major classes of learning and memory paradigms. - Link learning outcomes at a behavioral level to synaptic plasticity mechanisms that can implement them.
74relevance
14
AarXiv24 Jul 2024
Continual Learning with Hebbian Plasticity in Sparse and Predictive Coding Networks: A Survey and Perspective
Ali Safa
Coding Networks: A Survey and Perspective Authors: ['Ali Safa'] Abstract. Recently, the use bio-plausible learning techniques such as Hebbian and Spike-Timing-Dependent Plasticity (STDP) have drawn significant attention for the design of compute-efficient AI systems that can continuously learn on-line at the edge. A…
74relevance
15
BbioRxiv22 Feb 2018
Emergence of oscillations via spike timing dependent plasticity
Sarit Soloduchin, Maoz Shamir
Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes. In certain cases changes in oscillatory activity has been associated with pathological states. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are…
73relevance
16
BbioRxiv25 Nov 2021
Interaction between theta-phase and spike-timing dependent plasticity simulates theta induced memory effects
Danying Wang, George Parish, Kimron L. Shapiro, Simon Hanslmayr
Rodent studies suggest that spike timing relative to hippocampal theta activity determines whether potentiation or depression of synapses arise. Such changes also depend on spike timing between pre- and post-synaptic neurons, known as spike-timing-dependent plasticity (STDP). STDP, together with theta-phase-dependent…
72relevance
17
BbioRxiv13 Jul 2016
Beyond spike-timing-dependent plasticity: a computational study of plasticity gradients across basal dendrites
Jacopo Bono, Claudia Clopath
Synaptic plasticity is thought to be the principal mechanism underlying learning in the brain. The majority of plastic networks in computational neuroscience combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule. However, a point neuron does not capture the complexity of dendrites.…
71relevance
18
BbioRxiv10 Nov 2017
Recurrent network model for learning goal-directed sequences through reverse replay
Tatsuya Haga, Tomoki Fukai
Reverse replay of hippocampal place cells occurs frequently at rewarded locations, suggesting its contribution to goal-directed pathway learning. Symmetric spike-timing dependent plasticity (STDP) in CA3 likely potentiates recurrent synapses for both forward (start to goal) and reverse (goal to start) replays during…
70relevance
19
PPubMed22 Jan 2013
How to train a neuron
Rui P Costa, Alanna J Watt, P Jesper Sjöström
A cellular learning rule known as spike-timing-dependent plasticity can form, reshape and erase the response preferences of visual cortex neurons.
69relevance
20
CchemRxiv10 Jul 2025
LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields
Authors not listed
Large Language Models (LLMs) based on transformer architectures excel at internet-scale tasks. However, real-world scientific scenarios—such as synthetic chemistry laboratories and autonomous experimental setups—typically involve incremental data generation in batches as new chemical reactions are conducted, unlike…
58relevance
21
CchemRxiv21 Oct 2025
Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
56relevance
22
AarXiv2019
A Complementary Learning Systems Approach to Temporal Difference Learning
Sam Blakeman, Denis Mareschal
Complementary Learning Systems (CLS) theory suggests that the brain uses a 'neocortical' and a 'hippocampal' learning system to achieve complex behavior. These two systems are complementary in that the 'neocortical' system relies on slow learning of distributed representations while the 'hippocampal' system relies on…
54relevance
23
CchemRxiv20 Oct 2021
Improving De Novo Molecular Design with Curriculum Learning
Jeff Guo, Vendy Fialková, Juan Diego Arango, Christian Margreitter + 4 more
Reinforcement learning (RL) is a powerful paradigm that has gained popularity across multiple domains. However, applying RL may come at a cost of multiple interactions between the agent and the environment. This cost can be especially pronounced when the single feedback from the environment is slow or computationally…
52relevance

Results for STDP learning

Rapid learning of predictive maps with STDP and theta phase precession

Spike Agreement Dependent Plasticity: A scalable Bio-Inspired learning paradigm for Spiking Neural Networks

STDP encodes oscillation frequencies in the connections of recurrent networks of spiking neurons

On Practical Issues for Stochastic STDP Hardware With 1-bit Synaptic Weights

Evolutionary and spike-timing-dependent reinforcement learning train spiking neuronal network motor control

Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules

ASP: Learning to Forget With Adaptive Synaptic Plasticity in Spiking Neural Networks

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task Multi-layer network utilizing rewarded STDP to learn a foraging task

Delay Selection by Spike-Timing-Dependent Plasticity in Recurrent Networks of Spiking Neurons Receiving Oscillatory Inputs Delay Selection by STDP with Oscillatory Inputs

Synaptic learning rules for sequence learning

Dependence of spatial filtering by spike timing dependent synaptic plasticity on learning window

Spiking Associative Memory for Spatio-Temporal Patterns.

Computational models of learning and synaptic plasticity

Continual Learning with Hebbian Plasticity in Sparse and Predictive Coding Networks: A Survey and Perspective

Emergence of oscillations via spike timing dependent plasticity

Interaction between theta-phase and spike-timing dependent plasticity simulates theta induced memory effects

Beyond spike-timing-dependent plasticity: a computational study of plasticity gradients across basal dendrites

Recurrent network model for learning goal-directed sequences through reverse replay

How to train a neuron

LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

A Complementary Learning Systems Approach to Temporal Difference Learning

Improving De Novo Molecular Design with Curriculum Learning

Rapid learning of predictive maps with STDP and theta phase precession

Spike Agreement Dependent Plasticity: A scalable Bio-Inspired learning paradigm for Spiking Neural Networks

STDP encodes oscillation frequencies in the connections of recurrent networks of spiking neurons

On Practical Issues for Stochastic STDP Hardware With 1-bit Synaptic Weights

Evolutionary and spike-timing-dependent reinforcement learning train spiking neuronal network motor control

Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules

ASP: Learning to Forget With Adaptive Synaptic Plasticity in Spiking Neural Networks

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task Multi-layer network utilizing rewarded STDP to learn a foraging task

Delay Selection by Spike-Timing-Dependent Plasticity in Recurrent Networks of Spiking Neurons Receiving Oscillatory Inputs Delay Selection by STDP with Oscillatory Inputs

Synaptic learning rules for sequence learning

Dependence of spatial filtering by spike timing dependent synaptic plasticity on learning window

Spiking Associative Memory for Spatio-Temporal Patterns.

Computational models of learning and synaptic plasticity

Continual Learning with Hebbian Plasticity in Sparse and Predictive Coding Networks: A Survey and Perspective

Emergence of oscillations via spike timing dependent plasticity

Interaction between theta-phase and spike-timing dependent plasticity simulates theta induced memory effects

Beyond spike-timing-dependent plasticity: a computational study of plasticity gradients across basal dendrites

Recurrent network model for learning goal-directed sequences through reverse replay

How to train a neuron

LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

A Complementary Learning Systems Approach to Temporal Difference Learning

Improving De Novo Molecular Design with Curriculum Learning