Abstract
We investigated the relevance of single-unit recordings in the context of dynamical neural systems with recurrent synapses. The present study focuses on modeling a relatively small, biologically-plausible network of neurons. In the absence of any input, the network activity is self-sustained due to the resonating properties of the neurons. Recording of single units reveals an increasingly complex response to stimulation as one proceeds higher into the processing stream hierarchy. Results suggest that classical analysis methods, using rate and averaging over trials, fail to describe the dynamics of the system, and instead hide the relevant information embedded in the complex states of the network. We conclude that single-unit recordings, which are still extensively used in experimental neuroscience, need to be more carefully interpreted.
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Mureşan, R.C., Pipa, G., Wheeler, D.W. (2005). Single-Unit Recordings Revisited: Activity in Recurrent Microcircuits. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_25
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DOI: https://doi.org/10.1007/11550822_25
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