Predicting Single-Neuron Activity in Locally Connected Networks

DOI10.1162/NECO_a_00343zbMath1268.92018arXiv1506.04301WikidataQ39191401 ScholiaQ39191401MaRDI QIDQ2840862

Feraz Azhar, William S. Anderson

Publication date: 23 July 2013

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1506.04301

Mathematics Subject Classification ID

Neural biology (92C20) Neural networks for/in biological studies, artificial life and related topics (92B20) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55) Computational methods for problems pertaining to biology (92-08)

Cites Work

• A mathematical framework for inferring connectivity in probabilistic neuronal networks
• Maximum likelihood analysis of spike trains of interacting nerve cells
• Maximum likelihood identification of neural point process systems
• The role of a transient potassium current in a bursting neuron model
• Changing excitation and inhibition in simulated neural networks: effects on induced bursting behavior
• Studies of stimulus parameters for seizure disruption using neural network simulations
• Nonconvergence in Logistic and Poisson Models for Neural Spiking
• An Introduction to the Theory of Point Processes
• Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity

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