Coherency and connectivity in oscillating neural networks: linear partialization analysis |
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Authors: | S Kalitzin Bob W van Dijk H Spekreijse W A van Leeuwen |
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Institution: | (1) Graduate School of Neurosciences Amsterdam, The Netherlands Ophthalmic Research Institute, Department of Visual Systems Analysis, P.O. Box 12141, 1100 AC Amsterdam, The Netherlands, NL;(2) Laboratory for Medical Physics, UvA, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands, NL;(3) Institute for Theoretical Physics, University of Amsterdam, Valckenierstraat 65, 1018 XE Amsterdam, The Netherlands, NL |
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Abstract: | This paper studies the relation between the functional synaptic connections between two artificial neural networks and the
correlation of their spiking activities. The model neurons had realistic non-oscillatory dynamic properties and the networks
showed oscillatory behavior as a result of their internal synaptic connectivity. We found that both excitation and inhibition
cause phase locking of the oscillating activities. When the two networks excite each other the oscillations synchronize with
zero phase lag, whereas mutual inhibition between the networks resulted in an anti-phase (half period phase difference) synchronization.
Correlations between the activities of the two networks can also be caused by correlated external inputs driving the systems
(common input). Our analysis shows that when the networks exhibit oscillatory behavior and the rate of the common input is
smaller than a characteristic network oscillator frequency, the cross-correlation functions between the activities of two
systems still carry information about the mutual synaptic connectivity. This information can be retrieved with linear partialization,
removing the influence of the common input. We further explored the network responses to periodic external input. We found
that when the input is of a frequency smaller than a certain threshold, the network responds with bursts at the same frequency
as the input. Above the threshold, the network responds with a fraction of the input frequency. This frequency threshold,
characterizing the oscillatory properties of the network, is also found to determine the limit to which linear partialization
works.
Received: 20 October 1995 / Accepted in revised form: 20 May 1996 |
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