ArticleJournal of computational neuroscience2019
Stability of point process spiking neuron models.
Article in Journal of computational neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- ReBaCCA-ss: Relevance-Balanced Continuum Correlation Analysis With Smoothing and Surrogating for Quantifying Similarity Between Population Spiking Activities.Neural computation · 2026Article
- A Population Coupling Model Identifies Reduced Propagation from V1 to Higher Visual Areas During Locomotion.bioRxiv : the preprint server for biology · 2026Article
- Article
- Identifying interactions across brain areas while accounting for individual-neuron dynamics with a Transformer-based variational autoencoder.Advances in neural information processing systems · 2025Article
- Identification of interacting neural populations: methods and statistical considerations.Journal of neurophysiology · 2023Review
- A biophysical and statistical modeling paradigm for connecting neural physiology and function.Journal of computational neuroscience · 2023Article
- Characterizing and dissociating multiple time-varying modulatory computations influencing neuronal activity.PLoS computational biology · 2019Article
- Stability of stochastic finite-size spiking-neuron networks: Comparing mean-field, 1-loop correction and quasi-renewal approximations.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2019Article
- Emerging techniques in statistical analysis of neural data.Journal of computational neuroscience · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Point process regression models, based on generalized linear model (GLM) technology, have been widely used for spike train analysis, but a recent paper by Gerhard et al. described a kind of instability, in which fitted models can generate simulated spike trains with explosive firing rates. We analyze the problem by extending the methods of Gerhard et al. First, we improve their instability diagnostic and extend it to a wider class of models. Next, we point out some common situations in which instability can be traced to model lack of fit. Finally, we investigate distinctions between models that use a single filter to represent the effects of all spikes prior to any particular time t, as in a 2008 paper by Pillow et al., and those that allow different filters for each spike prior to time t, as in a 2001 paper by Kass and Ventura. We re-analyze the data sets used by Gerhard et al., introduce an additional data set that exhibits bursting, and use a well-known model described by Izhikevich to simulate spike trains from various ground truth scenarios. We conclude that models with multiple filters tend to avoid instability, but there are unlikely to be universal rules. Instead, care in data fitting is required and models need to be assessed for each unique set of data.
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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.