ArticleJournal of computational neuroscience2013
Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions.
Article in Journal of computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Integrating neural decoding, memristive materials, and adaptive control frameworks for next-generation hippocampal memory prosthetics.iScience · 2026Review
- Inferring effective networks of spiking neurons using a continuous-time estimator of transfer entropy.PLoS computational biology · 2025Article
- Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall of stimulus features and categories.Frontiers in computational neuroscience · 2024Article
- A Double-Layer Multi-Resolution Classification Model for Decoding Spatiotemporal Patterns of Spikes With Small Sample Size.Neural computation · 2021Article
- Axonal Anatomy Optimizes Spatial Encoding in the Rat Entorhinal-Dentate System: A Computational Study.IEEE transactions on bio-medical engineering · 2019Article
- Multi-Input, Multi-Output Neuronal Mode Network Approach to Modeling the Encoding Dynamics and Functional Connectivity of Neural Systems.Neural computation · 2019Article
- Adjusted regularization of cortical covariance.Journal of computational neuroscience · 2018Article
- Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall.Journal of neural engineering · 2018Article
- Sparse Large-Scale Nonlinear Dynamical Modeling of Human Hippocampus for Memory Prostheses.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2018Article
- Characterisation of ictal and interictal states of epilepsy: A system dynamic approach of principal dynamic modes analysis.PloS one · 2018Article
- Methodology of Recurrent Laguerre-Volterra Network for Modeling Nonlinear Dynamic Systems.IEEE transactions on neural networks and learning systems · 2017Article
- Estimating short-term synaptic plasticity from pre- and postsynaptic spiking.PLoS computational biology · 2017Article
- Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank.Frontiers in neuroscience · 2017Article
- Empirical Bayesian significance measure of neuronal spike response.BMC neuroscience · 2016Article
- Evaluating the Small-World-Ness of a Sampled Network: Functional Connectivity of Entorhinal-Hippocampal Circuitry.Scientific reports · 2016Article
- Neurons and networks organizing and sequencing memories.Brain research · 2015Article
- Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity.Journal of computational neuroscience · 2015Article
- Identification of functional synaptic plasticity from spiking activities using nonlinear dynamical modeling.Journal of neuroscience methods · 2015Article
- Identifying and tracking simulated synaptic inputs from neuronal firing: insights from in vitro experiments.PLoS computational biology · 2015Article
- Volterra representation enables modeling of complex synaptic nonlinear dynamics in large-scale simulations.Frontiers in computational neuroscience · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
One key problem in computational neuroscience and neural engineering is the identification and modeling of functional connectivity in the brain using spike train data. To reduce model complexity, alleviate overfitting, and thus facilitate model interpretation, sparse representation and estimation of functional connectivity is needed. Sparsities include global sparsity, which captures the sparse connectivities between neurons, and local sparsity, which reflects the active temporal ranges of the input-output dynamical interactions. In this paper, we formulate a generalized functional additive model (GFAM) and develop the associated penalized likelihood estimation methods for such a modeling problem. A GFAM consists of a set of basis functions convolving the input signals, and a link function generating the firing probability of the output neuron from the summation of the convolutions weighted by the sought model coefficients. Model sparsities are achieved by using various penalized likelihood estimations and basis functions. Specifically, we introduce two variations of the GFAM using a global basis (e.g., Laguerre basis) and group LASSO estimation, and a local basis (e.g., B-spline basis) and group bridge estimation, respectively. We further develop an optimization method based on quadratic approximation of the likelihood function for the estimation of these models. Simulation and experimental results show that both group-LASSO-Laguerre and group-bridge-B-spline can capture faithfully the global sparsities, while the latter can replicate accurately and simultaneously both global and local sparsities. The sparse models outperform the full models estimated with the standard maximum likelihood method in out-of-sample predictions.
Indexed as
Identifiers
What OpenQuestion holds
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.