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| Hauptseite > Publikationsdatenbank > Ultra-high frequency spectrum of neuronal activity > print |
| 001 | 885770 | ||
| 005 | 20240313103123.0 | ||
| 024 | 7 | _ | |a 10.12751/NNCN.BC2020.0080 |2 doi |
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| 037 | _ | _ | |a FZJ-2020-04078 |
| 041 | _ | _ | |a eng |
| 100 | 1 | _ | |a Essink, Simon |0 P:(DE-Juel1)176777 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Bernstein Konferenz 2020 |g BC20 |c online |d 2020-09-29 - 2020-10-01 |w Germany |
| 245 | _ | _ | |a Ultra-high frequency spectrum of neuronal activity |
| 260 | _ | _ | |c 2020 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
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| 520 | _ | _ | |a The activity of spiking network models exhibits fast oscillations (>200 Hz), caused by inhibition-dominated excitatory-inhibitory loops [1, 2]. As correlations between pairs of neurons are weak in nature and models, fast oscillations have so far received little attention.Today’s models of cortical networks with natural numbers of neurons and synapses [3] remove any uncertainty about down-scaling artifacts [4]. Fast oscillations here arise as vertical stripes in raster diagrams. We discuss experimental detectability of oscillations, ask whether they are an artifact of simplified models, and identify adaptations to control them.The population rate spectrum decomposes into single-neuron power spectra (∼N) and cross-spectra of pairs of neurons (∼N2) [5,6]. For low numbers of neurons (100) and weak correlations, the single-neuron spectra dominate the compound spectrum. Coherent oscillations in the population activity may thus go unnoticed in experimental spike recordings. Population measures obtained from large neuron ensembles (e.g., LFP), however, should show a pronounced peak.Cortical network models allow an investigation from different angles. We rule out artifacts of time-discrete simulation and investigate the effect of distributed synaptic delays: exponential distributions decrease the oscillation amplitude, expected by their equivalence to low-pass filtering [7], whereas truncated Gaussian distributions are ineffective.Surprisingly, a model of V1 [8], with the same architecture, but fewer synapses per neuron, does not exhibit fast oscillations. Mean-field theory shows that loops within each inhibitory population cause fast oscillations. Peak frequency and amplitude are determined by eigenvalues of the effective connectivity matrix approaching instability [9]. Reducing the connection density decreases the eigenvalues, increasing their distance to instability; we thus expect weaker oscillations.Counter to expectation and simulation, mean-field theory predicts an increase, explained by an overestimation of the transfer function at high frequencies [10]: the initial network appears to be linearly unstable, with |λ|>1; reduced connectivity seemingly destabilizes the system. A semi-analytical correction restores qualitative agreement with simulation.The work points at the importance of models with realistic cell densities and connectivity, and illustrates the productive interplay of simulation-driven and analytical approaches.References 1. Brunel, N. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. JComputNeurosci 8, 183–208 (2000)., 10.1371/journal.pcbi.1006359 2. Brunel, N. & Wang, X.-J. What Determines the Frequency of Fast Network Oscillations With Irregular Neural Discharges? I. Synaptic Dynamics and Excitation-Inhibition Balance. JNeurophysiol 90, 415–430 (2003)., 10.1152/jn.01095.2002 3. Potjans, T. C. & Diesmann, M. The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model. CerebCortex 24, 785–806 (2014)., 10.1093/cercor/bhs358 4. van Albada, S. J., Helias, M. & Diesmann, M. Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations. ploscb 11, e1004490 (2015)., 10.1371/journal.pcbi.1004490 5. Harris, K. D., & Thiele, A. Cortical state and attention. Nature Reviews Neuroscience, 12(9), 509-523 (2011)., 10.1038/nrn3084 6. Tetzlaff, T., Helias, M., Einevoll, G. T., & Diesmann, M. Decorrelation of neural-network activity by inhibitory feedback. PLoS Comput Biol, 8(8), e1002596 (2012)., 10.1371/journal.pcbi.1002596 7. Mattia, M., Biggio, M., Galluzzi, A. & Storace, M. Dimensional reduction in networks of non-Markovian spiking neurons: Equivalence of synaptic filtering and heterogeneous propagation delays. PLoS Comput Biol 15, e1007404 (2019)., 10.1371/journal.pcbi.1007404 8. Schmidt, M. et al. A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. ploscb 14, e1006359 (2018)., 10.1023/a:1008925309027 9. Bos, H., Diesmann, M. & Helias, M. Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit. ploscb 12, e1005132 (2016)., 10.1371/journal.pcbi.1005132 10. Schuecker, J., Diesmann, M. & Helias, M. Modulated escape from a metastable state driven by colored noise. Phys. Rev. E (2015)., 10.1103/PhysRevE.92.052119 |
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