Stochastic description of complex and simple spike ﬁring in cerebellar Purkinje cells
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
Cerebellar Purkinje cells generate two distinct types of spikes, complex and simple spikes, both of which have conventionally been considered to be highly irregular, suggestive of certain types of stochastic processes as underlying mechanisms. Interestingly, however, the interspike interval structures of complex spikes have not been carefully studied so far. We showed in a previous study that simple spike trains are actually composed of regular patterns and single interspike intervals, a mixture that could not be explained by a simple rate-modulated Poisson process. In the present study, we systematically investigated the interspike interval structures of separated complex and simple spike trains recorded in anaesthetized rats, and derived an appropriate stochastic model. We found that: (i) complex spike trains do not exhibit any serial correlations, so they can effectively be generated by a renewal process, (ii) the distribution of intervals between complex spikes exhibits two narrow bands, possibly caused by two oscillatory bands (0.5–1 and 4–8 Hz) in the input to Purkinje cells and (iii) the regularity of regular patterns and single interspike intervals in simple spike trains can be represented by gamma processes of orders, which themselves are drawn from gamma distributions, suggesting that multiple sources modulate the regularity of simple spike trains.
|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
F. Tito Arecchi (2003). Chaotic Neuron Dynamics, Synchronization, and Feature Binding: Quantum Aspects. Mind and Matter 1 (1):15-43.
Jeffrey Dunn (2012). Reliabilism: Holistic or Simple? Episteme 9 (3):225-233.
Eric T. Olson (forthcoming). In Search of the Simple View. In G. Gasser & M. Stefan (eds.), Personal Identity: Complex or Simple? Cambridge University Press
Gualtiero Piccinini & Sonya Bahar (2013). Neural Computation and the Computational Theory of Cognition. Cognitive Science 37 (3):453-488.
M. Kuhlmann (2011). Mechanisms in Dynamically Complex Systems. In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. OUP Oxford
Douglas Patterson (2007). Inconsistency Theories: The Significance of Semantic Ascent. Inquiry 50 (6):575-589.
Howard Rachlin (2007). A Behavioral Science of Mental Life: Comments on Foxall's "Intentional Behaviorism". Behavior and Philosophy 35:131 - 138.
Steven Walt (1998). Richard A. Epstein, Simple Rules for a Complex World:Simple Rules for a Complex World. Ethics 109 (1):193-198.
John W. Donahoe & José E. Burgos (2005). Selectionism: Complex Outcomes From Simple Processes. Behavioral and Brain Sciences 28 (3):429-430.
Franck Varenne (2009). Models and Simulations in the Historical Emergence of the Science of Complexity. In Ma Aziz-Alaoui & C. Bertelle (eds.), From System Complexity to Emergent Properties. Springer 3--21.
Thaddeus J. Marczynski (2000). Novel Concepts of Sleep-Wakefullness and Neuronal Information Coding. Behavioral and Brain Sciences 23 (6):968-971.
Added to index2010-12-22
Total downloads16 ( #236,201 of 1,911,616 )
Recent downloads (6 months)1 ( #457,720 of 1,911,616 )
How can I increase my downloads?