Diese Anwendung erfordert Java-Skript.

Bitte aktivieren Sie Java-Script in den Browser-Einstellungen.
Network dynamics of spiking neurons with adaptation
Deger, Moritz; Wolf, Fred (2013)
clipboard
mla
clipboard		 Deger M., et al. "Network dynamics of spiking neurons with adaptation.", timms video, Universität Tübingen (2013): https://timms.uni-tuebingen.de:443/tp/UT_20130927_003_bestcon_0001. Accessed 05 Nov 2025.
apa
clipboard		 Deger, M. & Wolf, F. (2013). Network dynamics of spiking neurons with adaptation. timms video: Universität Tübingen. Retrieved November 05, 2025 from the World Wide Web https://timms.uni-tuebingen.de:443/tp/UT_20130927_003_bestcon_0001
harvard
clipboard		 Deger, M. and Wolf, F. (2013). Network dynamics of spiking neurons with adaptation [Online video]. 27 September. Available at: https://timms.uni-tuebingen.de:443/tp/UT_20130927_003_bestcon_0001 (Accessed: 5 November 2025).
file download bibtex   endnote
Information
title: Network dynamics of spiking neurons with adaptation
alt. title: Bernstein Conference 2013: Cortical Dynamics and Circuits
creators: Deger, Moritz (author), Wolf, Fred (annotator)
subjects: Bernstein Conference, Computational Neuroscience, Cortical Dynamics and Circuits, Network Dynamics, Spiking Neurons, Adaptation, Moritz Deger
description: Bernstein Conference 2013, 24. bis 27. September 2013
abstract: Local neuronal circuits in the neocortex consist of hundreds of neurons [1]. These neurons typically show refractoriness after emitting an action potential (spike), and accumulating refractory effects result in adaptation on multiple time scales [3]. Both finite network size and neuronal adaptation make it difficult to derive population dynamics using traditional, stochastic process approaches. Here we present a novel theory of the population activity of finite-sized, randomly connected networks of spiking neurons with adaptation, which is obtained by approximating neuronal spike emission by a quasi-renewal process [2]. Our theory describes the average firing rate and its spectral density in coupled networks, exemplified for the typical case of a network of excitatory and inhibitory neurons. Furthermore, we show how correlated noise influences the stationary population activity, and how it can contribute to synchronize or desynchronize neuronal circuits. References [1] S. Lefort, C. Tomm, J.-C. F. Sarria, and C. C. H. Petersen. Neuron 61(2):301-316, (2009). [2] R. Naud and W. Gerstner. PLoS Comput Biol, 8(10):e1002711, (2012). [3] C. A. Pozzorini, R. Naud, S. Mensi, and W. Gerstner. Nat Neurosci, (2013). in press.
publisher: ZDV Universität Tübingen
contributors: Bernstein Center for Computational Neuroscience Tübingen (BCCN) (producer), Bethge, Matthias (organizer), Wichmann, Felix (organizer), Lam, Judith (organizer), Macke, Jakob (organizer)
creation date: 2013-09-27
dc type: image
localtype: video
identifier: UT_20130927_003_bestcon_0001
language: eng
rights: Url: https://timmsstatic.uni-tuebingen.de/jtimms/TimmsDisclaimer.html?638979100766718886