ArticleJournal of neuroscience methods2015
System identification of point-process neural systems using probability based Volterra kernels.
Article in Journal of neuroscience methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Exploring the role of repetitive negative thinking in the transdiagnostic context of depression and anxiety in children.BMC psychology · 2025Article
- Revealing the information contents of memory within the stimulus information representation framework.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Article
- Cannabinoids disrupt memory encoding by functionally isolating hippocampal CA1 from CA3.PLoS computational biology · 2017Article
- Hippocampal closed-loop modeling and implications for seizure stimulation design.Journal of neural engineering · 2015Article
- Understanding spike-triggered covariance using Wiener theory for receptive field identification.Journal of vision · 2015Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundNeural information processing involves a series of nonlinear dynamical input/output transformations between the spike trains of neurons/neuronal ensembles. Understanding and quantifying these transformations is critical both for understanding neural physiology such as short-term potentiation and for developing cognitive neural prosthetics. NEW
methodA novel method for estimating Volterra kernels for systems with point-process inputs and outputs is developed based on elementary probability theory. These Probability Based Volterra (PBV) kernels essentially describe the probability of an output spike given q input spikes at various lags t1, t2, …, tq.
resultsThe PBV kernels are used to estimate both synthetic systems where ground truth is available and data from the CA3 and CA1 regions rodent hippocampus. The PBV kernels give excellent predictive results in both cases. Furthermore, they are shown to be quite robust to noise and to have good convergence and overfitting properties. Through a slight modification, the PBV kernels are shown to also deal well with correlated point-process inputs. COMPARISON WITH EXISTING
methodsThe PBV kernels were compared with kernels estimated through least squares estimation (LSE) and through the Laguerre expansion technique (LET). The LSE kernels were shown to fair very poorly with real data due to the large amount of input noise. Although the LET kernels gave the best predictive results in all cases, they require prior parameter estimation. It was shown how the PBV and LET methods can be combined synergistically to maximize performance.
conclusionsThe PBV kernels provide a novel and intuitive method of characterizing point-process input-output nonlinear systems.
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