Neural Comp. Sign up for ETOCS
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 QUICK SEARCH:   [advanced]


     


This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Giugliano, M.
Right arrow Articles by Grattarola, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Giugliano, M.
Right arrow Articles by Grattarola, M.
(Neural Computation. 1999;11:1413-1426.)
© 1999 The MIT Press


Letter

Fast Calculation of Short-Term Depressing Synaptic Conductances

Michele Giugliano

Bioelectronics and Neuroengineering Group, Department of Biophysical and Electronic Engineering, V. Opera Pia 11a 16145 Genoa, Italy

Marco Bove

Bioelectronics and Neuroengineering Group, Department of Biophysical and Electronic Engineering, V. Opera Pia 11a 16145 Genoa, Italy

Massimo Grattarola

Bioelectronics and Neuroengineering Group, Department of Biophysical and Electronic Engineering, V. Opera Pia 11a 16145 Genoa, Italy

An efficient implementation of synaptic transmission models in realistic network simulations is an important theme of computational neuroscience. The amount of CPU time required to simulate synaptic interactions can increase as the square of the number of units of such networks, depending on the connectivity convergence. As a consequence, any realistic description of synaptic phenomena, incorporating biophysical details, is computationally highly demanding. We present a consolidating algorithm based on a biophysical extended model of ligand-gated postsynaptic channels, describing short-term plasticity such assynaptic depression. The considerable speedup of simulation times makes this algorithm suitable for investigating emergent collective effects of short-term depression in large-scale networks of model neurons.




This article has been cited by other articles:


Home page
Neural Comput.Home page
A. Saudargiene, B. Porr, and F. Worgotter
How the Shape of Pre- and Postsynaptic Signals Can Influence STDP: A Biophysical Model
Neural Comput., March 1, 2004; 16(3): 595 - 625.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
M. Giugliano
Synthesis of Generalized Algorithms for the Fast Computation of Synaptic Conductances with Markov Kinetic Models in Large Network Simulations
Neural Comput., April 1, 2000; 12(4): 903 - 931.
[Abstract] [Full Text]




HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 1999 by The MIT Press.