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.
Keywords
- Firing Rate
- Subthreshold Oscillation
- Experimental Neuroscience
- Classical Analysis Method
- Multiunit Recording
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2005 Springer-Verlag Berlin Heidelberg
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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
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28752-0
Online ISBN: 978-3-540-28754-4
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