Conditions for Propagating Synchronous Spiking and Asynchronous Firing Rates in a Cortical Network Model
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Isolated feedforward networks (FFNs) of spiking neurons have been studied extensively for their ability to propagate transient synchrony and asynchronous firing rates, in the presence of activity independent synaptic background noise (Diesmann et al., 1999; van Rossum et al., 2002). In a biologically realistic scenario, however, the FFN should be embedded in a recurrent network, such that the activity in the FFN and the network activity may dynamically interact. Previously, transient synchrony propagating in an FFN was found to destabilize the dynamics of the embedding network (Mehring et al., 2003). Here, we show that by modeling synapses as conductance transients, rather than current sources, it is possible to embed and propagate transient synchrony in the FFN, without destabilizing the background network dynamics. However, the network activity has a strong impact on the type of activity that can be propagated in the embedded FFN. Global synchrony and high firing rates in the embedding network prohibit the propagation of both, synchronous and asynchronous spiking activity. In contrast, asynchronous low-rate network states support the propagation of both, synchronous spiking and asynchro- nous, but only low firing rates. In either case, spiking activity tends to synchronize as it propagates, challenging the feasibility to transmit information in asynchronous firing rates. Finally, asynchronous network activity allows to embed more than one FFN, with the amount of cross talk depending on the degree of overlap in the FFNs. This opens the possibility of computational mechanisms using transient synchrony among the activities in multiple FFNs.
|Keywords||No keywords specified (fix it)|
No categories specified
(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
E. N. Miranda (1997). How Good Are Formal Neurons for Modelling Real Ones? Acta Biotheoretica 45 (2).
Edmund T. Rolls (2001). Representations in the Brain. Synthese 129 (2):153-171.
John Miller (1999). Critical Thinking and Asynchronous Discussion. Inquiry 19 (1):18-27.
Richard Brown (2006). What is a Brain State? Philosophical Psychology 19 (6):729-742.
Terence V. Sewards & Mark A. Sewards (2001). On the Correlation Between Synchronized Oscillatory Activities and Consciousness. Consciousness and Cognition 10 (4):485-495.
David Gamez (2010). Information Integration Based Predictions About the Conscious States of a Spiking Neural Network. Consciousness and Cognition 19 (1):294-310.
Alessandro Treves (1997). Synthesizing Synchrony Versus Dissecting Dissonance. Behavioral and Brain Sciences 20 (4):700-700.
Reinhard Eckhorn, H. J. Reitbock, M. Arndt & P. Dicke (1989). A Neural Network for Feature Linking Via Synchronous Activity: Results From Cat Visual Cortex and From Simulations. In Rodney M. J. Cotterill (ed.), Models of Brain Function. Cambridge University Press.
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.
Paul Thagard & Brandon M. Wagar, Spiking Phineas Gage: A Neurocomputational Theory of Cognitive–Affective Integration in Decision Making.
Marcel Kinsbourne (2000). How is Consciousness Expressed in the Cerebral Activation Manifold? Brain and Mind 1 (2):265-74.
Sorry, there are not enough data points to plot this chart.
Added to index2010-12-22
Total downloads1 ( #424,058 of 1,096,820 )
Recent downloads (6 months)0
How can I increase my downloads?