This title appears in the Scientific Report :
2016
Please use the identifier:
http://hdl.handle.net/2128/12553 in citations.
How does an oscillatory drive shape the correlations in binary networks?
How does an oscillatory drive shape the correlations in binary networks?
Two important parts of electrophysiological recordings are the spike times and the local field potential (LFP), which is considered to primarily reflect input activity. In [1], it was shown by unitary event analysis [2,3] that excess synchronous spike events are locked to the phase of LFP beta-oscil...
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Personal Name(s): | Kühn, Tobias (Corresponding author) |
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Denker, Michael / Mana, PierGianLuca / Grün, Sonja / Helias, Moritz | |
Contributing Institute: |
Computational and Systems Neuroscience; INM-6 Computational and Systems Neuroscience; IAS-6 |
Imprint: |
2016
|
Conference: | Bernstein Conference 2016, Berlin (Germany), 2016-09-20 - 2016-09-23 |
Document Type: |
Poster |
Research Program: |
Theory of multi-scale neuronal networks Supercomputing and Modelling for the Human Brain The Human Brain Project Theory, modelling and simulation Connectivity and Activity |
Link: |
OpenAccess |
Publikationsportal JuSER |
Two important parts of electrophysiological recordings are the spike times and the local field potential (LFP), which is considered to primarily reflect input activity. In [1], it was shown by unitary event analysis [2,3] that excess synchronous spike events are locked to the phase of LFP beta-oscillations more strongly than spikes not part of such events. Denker et al. proved by a statistical model that this finding could be explained by the existence of cell assemblies, i.e. groups of (excitatory) neurons that are more strongly connected amongst each other than to the rest of the network.To study the influence of the LFP on the correlated single neuron activities first for a simple model capturing the main properties of cortical neural networks, we examine a balanced network of homogeneously connected binary model neurons [4] receiving input from a sinusoidal perturbation [5]. The Glauber dynamics of the network is simulated and approximated by mean-field theory. Treating the periodic input in linear response theory, the cyclostationary first two moments are analytically computed, which agree with their simulated counterparts over a wide parameter range. The deviations of the zero-time lag correlations from their stationary values consist of two summands owing to the modulated susceptibility (one via direct modulation, one via modulated mean activity) and one to the driving of the autocorrelations. For some parameters, this leads to resonant correlations and non-resonant mean activities. Our results can help to answer the question how oscillations in mesoscopic signals and spike correlations interact. As a next step, our model could be extended to include cell assemblies [6], which will allow us to compare our results with the experimental findings more closely.figure caption:A: Contributions to the time-dependent variation of the correlations in linear perturbation theory. B: The deviation of the correlations from their stationary value is maximal for a certain frequency even for this setting with a connectivity matrix having solely purely real eigenvalues. References:[1] Denker M., Cerebral Cortex, 21:2681--2695, 2011, The Local Field Potential Reflects Surplus Spike Synchrony. [2] Grün S, Diesmann M, Aertsen A. Neural Comput., 14:43--80, 2002a, Unitary events in multiple single-neuron spiking activity: I. detection and significance. [3] Grün S, Diesmann M, Aertsen A. Neural Comput., 14:81--119, 2002b, Unitary events in multiple single-neuron spiking activity: II. Nonstationary data. [4] Ginzburg I., Sompolinsky H., Phys. Rev. E 50(4):3171--3191, 1994, Theory of correlations in stochastic neural networks.[5] Kühn T., Helias M., arXiv:1607.08552, 2016, Correlated activity of periodically driven binary networks.[6] Litwin-Kumar A., & Doiron B., Nature Neur., 15(11):1498--1505, 2012, Slow dynamics and high variability in balanced cortical networks with clustered connections. |