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Explaining synchrony in feedforward networks: are McCulloch-Pitts neurons good enough?

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posted on 2023-06-07, 20:32 authored by Thomas NowotnyThomas Nowotny, R Huerta
In any scientific theory, the conceptual framework already determines the nature and possible scope of the results. Oversimplification prevents an adequate description of the system, whereas too detailed a description obscures the fundamental principles behind the observed phenomena in addition to misspending time and resources. In theoretical neuroscience, this is an important issue because the description level varies widely from detailed biophysical descriptions to abstract computational models. We discuss the question of the appropriate modeling level in the context of a recent report on synchrony in iteratively constructed feed-forward networks of rat cortex pyramidal neuron somata (Reyes, 2003).


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  • Published


Biological Cybernetics


Springer Verlag





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  • Informatics Publications


Originality: In this paper we have shown that synchrony in feedforward networks can be understood in its entirety based on network connectivity alone. This should mark the endpoint of a long dicussion on this topic ("synfire chains") and reveals an important aspect of models in neuroscience: It is essential to choose the right description to extract the most meaningful results. Rigour: The work uses probability theory to derive analytical expressions for firing probabilities in a McCulloch-Pitts neuron framework. The resulting expressions are evaluated on the computer but the main results are apparent from the direct mathematical treatment. Significance: The work gets to the bottom of where synchrony in feedforward neural networks originates from and should put an end to a long dicussion on this matter. Furthermore it gives guidance on how to use specific models in computational neuroscience efficiently. Impact: This article was specifically selected as a 'letter to the editor' by the editor in chief. The cited half-life of Biological Cybernetics is longer than 10 years (2006 ISI impact factor list).

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