David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
We studied the dynamics of large networks of spiking neurons with conductance-based (nonlinear) synapses and compared them to net- works with current-based (linear) synapses. For systems with sparse and inhibition-dominated recurrent connectivity, weak external inputs in- duced asynchronous irregular ﬁring at low rates. Membrane potentials ﬂuctuated a few millivolts below threshold, and membrane conductances were increased by a factor 2 to 5 with respect to the resting state. This combination of parameters characterizes the ongoing spiking activity typ- ically recorded in the cortex in vivo. Many aspects of the asynchronous irregular state in conductance-based networks could be sufﬁciently well characterized with a simple numerical mean ﬁeld approach. In particular, it correctly predicted an intriguing property of conductance-based net- works that does not appear to be shared by current-based models: they exhibit states of low-rate asynchronous irregular activity that persist for some period of time even in the absence of external inputs and with- out cortical pacemakers. Simulations of larger networks (up to 350,000 neurons) demonstrated that the survival time of self-sustained activity increases exponentially with network size.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Arvind Kumar, Conditions for Propagating Synchronous Spiking and Asynchronous Firing Rates in a Cortical Network Model.
B. Delord (1999). Attractors and Pathological Aspects in Excitable Cells. Acta Biotheoretica 47 (3-4):239-252.
E. N. Miranda (1997). How Good Are Formal Neurons for Modelling Real Ones? Acta Biotheoretica 45 (2):171-179.
B. Doyon, B. Cessac, M. Quoy & M. Samuelides (1994). On Bifurcations and Chaos in Random Neural Networks. Acta Biotheoretica 42 (2-3):215-225.
Jean-Luc Gouzé (2010). Comparing Boolean and Piecewise Affine Differential Models for Genetic Networks. Acta Biotheoretica 58 (2):217-232.
M. Shanahan (2008). A Spiking Neuron Model of Cortical Broadcast and Competition. Consciousness and Cognition 17 (1):288-303.
Horst M. M.Ü & Ller (1999). The Lexicon From a Neurophysiological View. Behavioral and Brain Sciences 22 (1):50-51.
Dan R. Dalton & Idalene F. Kesner (1988). On the Dynamics of Corporate Size and Illegal Activity: An Empirical Assessment. [REVIEW] Journal of Business Ethics 7 (11):861 - 870.
Richard Alterman (2008). Activity and Convention. Topoi 27 (1-2):127-138.
Elio Conte (2012). Preliminary Considerations on a Possible Quantum Model of Consciousness Interfaced with a Non Lipschitz Chaotic Dynamics of Neural Activity. Journal of Consciousness Exploration and Research 3 (10):905-921.
Axel Cleeremans, Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain.
J. McKenzie Alexander (2003). Random Boolean Networks and Evolutionary Game Theory. Philosophy of Science 70 (5):1289-1304.
John C. Fentress (2000). Emotional Networks: The Heart of Brain Design. Behavioral and Brain Sciences 23 (2):198-199.
Roman Borisyuk, Galina Borisyuk & Yakov Kazanovich (1998). Synchronization of Neural Activity and Information Processing. Behavioral and Brain Sciences 21 (6):833-833.
Added to index2010-12-22
Total downloads7 ( #292,059 of 1,725,477 )
Recent downloads (6 months)6 ( #110,437 of 1,725,477 )
How can I increase my downloads?