ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2015
A Lognormal Recurrent Network Model for Burst Generation during Hippocampal Sharp Waves.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 80 citations in OpenAlex.
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- Microcircuit Synchronization and Heavy-Tailed Synaptic Weight Distribution Augment preBötzinger Complex Bursting Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
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- Effect of steady-state response versus excitatory/inhibitory balance on spiking synchronization in neural networks with log-normal synaptic weight distribution.Cognitive neurodynamics · 2022Article
- High-frequency oscillations and sequence generation in two-population models of hippocampal region CA1.PLoS computational biology · 2022Article
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- A convolutional neural network for estimating synaptic connectivity from spike trains.Scientific reports · 2021Article
- Temporal-specific complexity of spiking patterns in spontaneous activity induced by a dual complex network structure.Scientific reports · 2019Article
- Large time step discrete-time modeling of sharp wave activity in hippocampal area CA3.Communications in nonlinear science & numerical simulation · 2019Article
- Computer Model of Synapse Loss During an Alzheimer's Disease-Like Pathology in Hippocampal Subregions DG, CA3 and CA1-The Way to Chaos and Information Transfer.Entropy (Basel, Switzerland) · 2019Article
- Rich-club connectivity, diverse population coupling, and dynamical activity patterns emerging from local cortical circuits.PLoS computational biology · 2019Article
- Circuit mechanisms of hippocampal reactivation during sleep.Neurobiology of learning and memory · 2019Article
- Dendritic processing of spontaneous neuronal sequences for single-trial learning.Scientific reports · 2018Article
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The strength of cortical synapses distributes lognormally, with a long tail of strong synapses. Various properties of neuronal activity, such as the average firing rates of neurons, the rate and magnitude of spike bursts, the magnitude of population synchrony, and the correlations between presynaptic and postsynaptic spikes, also obey lognormal-like distributions reported in the rodent hippocampal CA1 and CA3 areas. Theoretical models have demonstrated how such a firing rate distribution emerges from neural network dynamics. However, how the other properties also display lognormal patterns remain unknown. Because these features are likely to originate from neural dynamics in CA3, we model a recurrent neural network with the weights of recurrent excitatory connections distributed lognormally to explore the underlying mechanisms and their functional implications. Using multi-timescale adaptive threshold neurons, we construct a low-frequency spontaneous firing state of bursty neurons. This state well replicates the observed statistical properties of population synchrony in hippocampal pyramidal cells. Our results show that the lognormal distribution of synaptic weights consistently accounts for the observed long-tailed features of hippocampal activity. Furthermore, our model demonstrates that bursts spread over the lognormal network much more effectively than single spikes, implying an advantage of spike bursts in information transfer. This efficiency in burst propagation is not found in neural network models with Gaussian-weighted recurrent excitatory synapses. Our model proposes a potential network mechanism to generate sharp waves in CA3 and associated ripples in CA1 because bursts occur in CA3 pyramidal neurons most frequently during sharp waves.
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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.