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Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning

https://doi.org/10.1162/neco.2006.18.6.1318

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What this work says

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Toward High-Accuracy and Low-Latency Spiking Neural Networks With Two-Stage Optimization

inferred← citeshttps://doi.org/10.1113/jp281510openalex-snapshot

Silences, spikes and bursts: Three‐part knot of the neural code

inferred← citeshttps://doi.org/10.1126/science.aab4113openalex-snapshot

Spiking neurons can discover predictive features by aggregate-label learning

inferred← citeshttps://doi.org/10.1142/s0129065714500026openalex-snapshot

CODE-SPECIFIC LEARNING RULES IMPROVE ACTION SELECTION BY POPULATIONS OF SPIKING NEURONS

inferred← citeshttps://doi.org/10.1142/s0219843622500244openalex-snapshot

Using Hebbian Learning for Training Spiking Neural Networks to Control Fingers of Robotic Hands

inferred← citeshttps://doi.org/10.1145/3229884.3229888openalex-snapshot

Gridbot

inferred← citeshttps://doi.org/10.1145/3313866openalex-snapshot

Energy-efficient FPGA Spiking Neural Accelerators with Supervised and Unsupervised Spike-timing-dependent-Plasticity

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Reinforcement Learning With Modulated Spike Timing–Dependent Synaptic Plasticity

inferred← citeshttps://doi.org/10.1155/2012/713581openalex-snapshot

A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques

inferred← citeshttps://doi.org/10.1155/2015/534198openalex-snapshot

Coincidence Detection Using Spiking Neurons with Application to Face Recognition

inferred← citeshttps://doi.org/10.1155/2015/947098openalex-snapshot

Training Spiking Neural Models Using Artificial Bee Colony

inferred← citeshttps://doi.org/10.1162/0899766054796888openalex-snapshot

What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?

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Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity

inferred← citeshttps://doi.org/10.1162/neco.2007.19.3.639openalex-snapshot

Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution

inferred← citeshttps://doi.org/10.1162/neco.2007.19.6.1468openalex-snapshot

Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity

inferred← citeshttps://doi.org/10.1162/neco.2008.03-08-727openalex-snapshot

Recurrent Infomax Generates Cell Assemblies, Neuronal Avalanches, and Simple Cell-Like Selectivity

inferred← citeshttps://doi.org/10.1162/neco.2008.09-07-605openalex-snapshot

A Gradient Learning Rule for the Tempotron

inferred← citeshttps://doi.org/10.1162/neco.2009.11-08-901openalex-snapshot

Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting

inferred← citeshttps://doi.org/10.1162/neco.2010.05-09-1010openalex-snapshot

Learning Spike-Based Population Codes by Reward and Population Feedback

inferred← citeshttps://doi.org/10.1162/neco_a_00090openalex-snapshot

Does High Firing Irregularity Enhance Learning?

inferred← citeshttps://doi.org/10.1162/neco_a_00140openalex-snapshot

Spike-Timing-Dependent Plasticity and Reliability Optimization: The Role of Neuron Dynamics

inferred← citeshttps://doi.org/10.1162/neco_a_00387openalex-snapshot

A Spiking Neural Model for Stable Reinforcement of Synapses Based on Multiple Distal Rewards

inferred← citeshttps://doi.org/10.1162/neco_a_00450openalex-snapshot

A New Supervised Learning Algorithm for Spiking Neurons

inferred← citeshttps://doi.org/10.1162/neco_a_00477openalex-snapshot

Spike-Based Probabilistic Inference in Analog Graphical Models Using Interspike-Interval Coding

inferred← citeshttps://doi.org/10.1162/neco_a_00503openalex-snapshot

On the Continuous Differentiability of Inter-Spike Intervals of Synaptically Connected Cortical Spiking Neurons in a Neuronal Network

inferred← citeshttps://doi.org/10.1162/neco_a_00758openalex-snapshot

A Reward-Maximizing Spiking Neuron as a Bounded Rational Decision Maker

inferred← citeshttps://doi.org/10.1162/neco_a_00790openalex-snapshot

Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks

inferred← citeshttps://doi.org/10.1162/neco_a_01086openalex-snapshot

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks

inferred← citeshttps://doi.org/10.1162/neco_a_01218openalex-snapshot

Learning with Precise Spike Times: A New Decoding Algorithm for Liquid State Machines

inferred← citeshttps://doi.org/10.1162/neco_a_01671openalex-snapshot

Desiderata for Normative Models of Synaptic Plasticity

inferred← citeshttps://doi.org/10.1162/neco_a_01752openalex-snapshot

Elucidating the Theoretical Underpinnings of Surrogate Gradient Learning in Spiking Neural Networks

inferred← citeshttps://doi.org/10.1186/1471-2202-9-s1-p72openalex-snapshot

Spike-based reinforcement learning of navigation

inferred← citeshttps://doi.org/10.1186/2190-8567-2-2openalex-snapshot

Gradient estimation in dendritic reinforcement learning

inferred← citeshttps://doi.org/10.1201/b14859-4openalex-snapshot

Neural Coding and Decoding with Spike Times

inferred← citeshttps://doi.org/10.12751/nncn.bc2017.0088openalex-snapshot

Gradient Descent for Spiking Neural Networks

inferred← citeshttps://doi.org/10.1360/n112015-00217openalex-snapshot

Biological plausible goal-directed navigation model based on direct reinforcement learning algorithm

inferred← citeshttps://doi.org/10.1371/annotation/307ea250-3792-4ceb-b905-162d86c96bafopenalex-snapshot

Correction: Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1000180openalex-snapshot

A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1000248openalex-snapshot

Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1000586openalex-snapshot

Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1000894openalex-snapshot

Reinforcement Learning on Slow Features of High-Dimensional Input Streams

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1002092openalex-snapshot

Spatio-Temporal Credit Assignment in Neuronal Population Learning

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1002691openalex-snapshot

Spike-based Decision Learning of Nash Equilibria in Two-Player Games

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1002711openalex-snapshot

Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1003024openalex-snapshot

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1003037openalex-snapshot

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1004307openalex-snapshot

Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1004638openalex-snapshot

Somato-dendritic Synaptic Plasticity and Error-backpropagation in Active Dendrites

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1004954openalex-snapshot

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1005003openalex-snapshot

Prospective Coding by Spiking Neurons

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1007640openalex-snapshot

On the choice of metric in gradient-based theories of brain function

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1009681openalex-snapshot

Hebbian plasticity in parallel synaptic pathways: A circuit mechanism for systems memory consolidation

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1009721openalex-snapshot

Learning as filtering: Implications for spike-based plasticity

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1010221openalex-snapshot

Error-based or target-based? A unified framework for learning in recurrent spiking networks

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1011844openalex-snapshot

Learning what matters: Synaptic plasticity with invariance to second-order input correlations

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1013777openalex-snapshot

Transient boosting of action potential backpropagation for few-shot temporal pattern learning

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1014752openalex-snapshot

Hierarchical feature binding in a spiking neural network model of the primate ventral visual pathway

inferred← citeshttps://doi.org/10.1371/journal.pone.0018539openalex-snapshot

Democratic Population Decisions Result in Robust Policy-Gradient Learning: A Parametric Study with GPU Simulations

inferred← citeshttps://doi.org/10.1371/journal.pone.0087123openalex-snapshot

Coexistence of Reward and Unsupervised Learning During the Operant Conditioning of Neural Firing Rates

inferred← citeshttps://doi.org/10.1371/journal.pone.0123105openalex-snapshot

Human and Machine Learning in Non-Markovian Decision Making

inferred← citeshttps://doi.org/10.1371/journal.pone.0148948openalex-snapshot

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

inferred← citeshttps://doi.org/10.1371/journal.pone.0161335openalex-snapshot

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

inferred← citeshttps://doi.org/10.1371/journal.pone.0229083openalex-snapshot

Pre-Synaptic Pool Modification (PSPM): A supervised learning procedure for recurrent spiking neural networks

inferred← citeshttps://doi.org/10.1371/journal.pone.0247014openalex-snapshot

Target spike patterns enable efficient and biologically plausible learning for complex temporal tasks

inferred← citeshttps://doi.org/10.1523/eneuro.0048-15.2016openalex-snapshot

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity

inferred← citeshttps://doi.org/10.1523/eneuro.0301-17.2018openalex-snapshot

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning

inferred← citeshttps://doi.org/10.1523/jneurosci.4098-12.2013openalex-snapshot

Matching Recall and Storage in Sequence Learning with Spiking Neural Networks

inferred← citeshttps://doi.org/10.1523/jneurosci.4284-09.2010openalex-snapshot

A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control Task

inferred← citeshttps://doi.org/10.1523/jneurosci.6072-08.2009openalex-snapshot

Realignment of Interaural Cortical Maps in Asymmetric Hearing Loss

inferred← citeshttps://doi.org/10.1523/jneurosci.6249-09.2010openalex-snapshot

Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity

inferred← citeshttps://doi.org/10.20537/nd1104009openalex-snapshot

Reinforcement learning of a spiking neural network in the task of control of an agent in a virtual discrete environment

inferred← citeshttps://doi.org/10.21203/rs.3.rs-701752/v1openalex-snapshot

The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware

inferred← citeshttps://doi.org/10.3103/s1060992x10040077openalex-snapshot

Generalized stochastic spiking neuron model and extended spike response model in spatial-temporal pulse pattern detection task

inferred← citeshttps://doi.org/10.32657/10356/179879openalex-snapshot

Learning improvement in spiking neural networks: enhancement techniques, design and performance analysis

inferred← citeshttps://doi.org/10.3389/fnbot.2019.00081openalex-snapshot

Embodied Synaptic Plasticity With Online Reinforcement Learning

inferred← citeshttps://doi.org/10.3389/fncir.2015.00085openalex-snapshot

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

inferred← citeshttps://doi.org/10.3389/fncir.2018.00053openalex-snapshot

Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules

inferred← citeshttps://doi.org/10.3389/fncom.2010.00023openalex-snapshot

STDP in recurrent neuronal networks

inferred← citeshttps://doi.org/10.3389/fncom.2010.00143openalex-snapshot

STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission

inferred← citeshttps://doi.org/10.3389/fncom.2010.00156openalex-snapshot

Anti-Hebbian Spike-Timing-Dependent Plasticity and Adaptive Sensory Processing

inferred← citeshttps://doi.org/10.3389/fncom.2014.00038openalex-snapshot

Stochastic variational learning in recurrent spiking networks

inferred← citeshttps://doi.org/10.3389/fncom.2017.00033openalex-snapshot

A Model of Fast Hebbian Spike Latency Normalization

inferred← citeshttps://doi.org/10.3389/fncom.2017.00104openalex-snapshot

Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns

inferred← citeshttps://doi.org/10.3389/fncom.2021.617862openalex-snapshot

Supervised Learning With First-to-Spike Decoding in Multilayer Spiking Neural Networks

inferred← citeshttps://doi.org/10.3389/fnins.2015.00227openalex-snapshot

Single pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25 ms to 125 μs

inferred← citeshttps://doi.org/10.3389/fnins.2015.00357openalex-snapshot

Implementation of a spike-based perceptron learning rule using TiO2−x memristors

inferred← citeshttps://doi.org/10.3389/fnins.2018.00583openalex-snapshot

Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning

inferred← citeshttps://doi.org/10.3389/fnins.2019.00883openalex-snapshot

Reinforcement Learning With Low-Complexity Liquid State Machines

inferred← citeshttps://doi.org/10.3389/fnins.2020.00424openalex-snapshot

Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)

inferred← citeshttps://doi.org/10.3389/fnins.2023.1266003openalex-snapshot

First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures

inferred← citeshttps://doi.org/10.3389/fnins.2023.1270090openalex-snapshot

SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks

inferred← citeshttps://doi.org/10.3389/fnins.2024.1402646openalex-snapshot

Synchronized stepwise control of firing and learning thresholds in a spiking randomly connected neural network toward hardware implementation

inferred← citeshttps://doi.org/10.3389/fnsyn.2010.00033openalex-snapshot

Rate and pulse based plasticity governed by local synaptic state variables

inferred← citeshttps://doi.org/10.3389/fnsyn.2023.1113957openalex-snapshot

GluN2B-NMDAR subunit contribution on synaptic plasticity: A phenomenological model for CA3-CA1 synapses

inferred← citeshttps://doi.org/10.3390/jcm11133868openalex-snapshot

The Full Informational Spectral Analysis for Auditory Steady-State Responses in Human Brain Using the Combination of Canonical Correlation Analysis and Holo-Hilbert Spectral Analysis

inferred← citeshttps://doi.org/10.3390/s19020395openalex-snapshot

Chain Modeling of Molecular Communications for Body Area Network

inferred← citeshttps://doi.org/10.3902/jnns.30.84openalex-snapshot

Baysian Synaptic Plasticity for Data Efficient Learning in the Brain

inferred← citeshttps://doi.org/10.3989/loquens.2021.077openalex-snapshot

Age and gender effects in European Portuguese spontaneous speech

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