Passive Nonlinear Dendritic Interactions as a Computational Resource in Spiking Neural Networks

DOI10.1162/neco_a_01338zbMath1469.92027arXiv1904.11713OpenAlexW3094403118WikidataQ100745203 ScholiaQ100745203MaRDI QIDQ5004284

Andreas Stöckel, Chris Eliasmith

Publication date: 30 July 2021

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

Full work available at URL: https://arxiv.org/abs/1904.11713

zbMATH Keywords

dendritic treespiking neural networksneural engineering framework

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (1)

The passive properties of dendrites modulate the propagation of slowly-varying firing rate in feedforward networks

Uses Software

• CVXOPT
• OSQP
• Nengo
• BioSpaun
• SpiNNaker

Cites Work

• OSQP: An Operator Splitting Solver for Quadratic Programs
• Multilayer feedforward networks are universal approximators
• Integrating behavioral and neural data in a model of zebrafish network interaction
• Solving the Problem of Negative Synaptic Weights in Cortical Models
• The polynomial solvability of convex quadratic programming
• Numerical Optimization
• Information Processing in Dendritic Trees
• Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells
• The Competing Benefits of Noise and Heterogeneity in Neural Coding
• Diffusion approximation and first passage time problem for a model neuron

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