ArticleFrontiers in computational neuroscience2013
A generative spike train model with time-structured higher order correlations.
Article in Frontiers in computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Subthreshold moment analysis of neuronal populations driven by synchronous synaptic inputs.PLoS computational biology · 2025Article
- Article
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- Balanced networks under spike-time dependent plasticity.PLoS computational biology · 2021Article
- High-order coordination of cortical spiking activity modulates perceptual accuracy.Nature neuroscience · 2019Article
- A theoretical framework for analyzing coupled neuronal networks: Application to the olfactory system.PLoS computational biology · 2017Article
- Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks.PLoS computational biology · 2016Article
- Shifting Spike Times or Adding and Deleting Spikes-How Different Types of Noise Shape Signal Transmission in Neural Populations.Journal of mathematical neuroscience · 2015Article
- Effect of the small-world structure on encoding performance in the primary visual cortex: an electrophysiological and modeling analysis.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2015Article
- Emergent spike patterns in neuronal populations.Journal of computational neuroscience · 2015Article
- Exact Analysis of the Subthreshold Variability for Conductance-Based Neuronal Models with Synchronous Synaptic Inputs.Physical review. XArticle
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4 authors.
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Abstract
Emerging technologies are revealing the spiking activity in ever larger neural ensembles. Frequently, this spiking is far from independent, with correlations in the spike times of different cells. Understanding how such correlations impact the dynamics and function of neural ensembles remains an important open problem. Here we describe a new, generative model for correlated spike trains that can exhibit many of the features observed in data. Extending prior work in mathematical finance, this generalized thinning and shift (GTaS) model creates marginally Poisson spike trains with diverse temporal correlation structures. We give several examples which highlight the model's flexibility and utility. For instance, we use it to examine how a neural network responds to highly structured patterns of inputs. We then show that the GTaS model is analytically tractable, and derive cumulant densities of all orders in terms of model parameters. The GTaS framework can therefore be an important tool in the experimental and theoretical exploration of neural dynamics.
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