ArticlePLoS computational biology2016
Spike-Based Bayesian-Hebbian Learning of Temporal Sequences.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- STDP-inspired temporal transition modeling for adaptive clinical risk prediction from electronic health records.medRxiv : the preprint server for health sciences · 2026Article
- Short-term plasticity influences episodic memory recall: an interplay of synaptic traces in a spiking neural network model.Scientific reports · 2025Article
- The impact of spike timing precision and spike emission reliability on decoding accuracy.Scientific reports · 2024Article
- Sequence learning, prediction, and replay in networks of spiking neurons.PLoS computational biology · 2022Article
- Article
- Neuronal Sequence Models for Bayesian Online Inference.Frontiers in artificial intelligence · 2021Review
- Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Learning spatiotemporal signals using a recurrent spiking network that discretizes time.PLoS computational biology · 2020Article
- Article
- Temporal-Sequential Learning With a Brain-Inspired Spiking Neural Network and Its Application to Musical Memory.Frontiers in computational neuroscience · 2020Article
- Optimizing BCPNN Learning Rule for Memory Access.Frontiers in neuroscience · 2020Article
- Introducing double bouquet cells into a modular cortical associative memory model.Journal of computational neuroscience · 2019Article
- Review
- Probabilistic associative learning suffices for learning the temporal structure of multiple sequences.PloS one · 2019Article
- Principles underlying the input-dependent formation and organization of memories.Network neuroscience (Cambridge, Mass.) · 2019Article
- Bridging structure and function: A model of sequence learning and prediction in primary visual cortex.PLoS computational biology · 2018Article
- A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.PLoS computational biology · 2017Article
- Article
- A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
- Synapse-Centric Mapping of Cortical Models to the SpiNNaker Neuromorphic Architecture.Frontiers in neuroscience · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.