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A2: Dynamics of population activity and spike synchronization in realistic cortical network models

A2: Dynamics of population activity and spike synchronization in realistic cortical network models

Ad AertsenA and Markus DiesmannQ

A = Neurobiology and Biophysics
Q = Riken Brain Science Institute

 

Scientific background

Recently, evidence has been accumulating that cortical neurons in vivo can generate action potentials at high temporal accuracy, in systematic relation to stimuli and behavior, suggesting that precise spike timing may play a functional role in cortical computation. Independent evidence for precise spike timing in cortical neurons came from intracellular recordings in vitro. The synfire chain model presents an attractive conceptual framework for implementing cortical computation by jointly precise spike timing among groups of neurons. Theoretical work on simplified models and network simulations rigorously defined conditions under which synchronous spike volleys may successfully propagate through the cortical network. Other studies addressed related properties, such as propagation velocity, influence of firing rate, and correlations, memory capacity, and learning.

Objectives

We aim to characterize the dynamics of population activity and spike synchronization in biologically realistic large-scale cortical network models, in an effort to decide whether cortical computation on the basis of precise spike timing is biologically feasible. This involves to consider the necessary physiological detail at the level of synapses and neurons, to assess the influence of the activity dynamics in the embedding network, to incorporate more realistic network architectures, and to impose dynamically structured input and ongoing activity patterns as measured in vivo. The combined results of these studies will enable us to determine the biological constraints on fine-temporal synchronization dynamics in cortical networks and the feasibility of solving real-world problems by computation in such biological networks.


List of project-related publications

  1. Helias M, Deger M, Diesmann M, Rotter S (2010) Equilibrium and response properties of the integrate-and-fire neuron in discrete time Front Comput Neurosci 3, 29 doi:10.3389/neuro.10.029.2009
  2. Kriener B, Helias M, Aertsen A, Rotter S. (2009) Correlations in spiking neuronal networks with distance dependent connection, Journal of Computational Neuroscience, Volume 27(2): 177-200
  3. Eppler JM, Helias M, Muller E, Diesmann M, Gewaltig M (2009). Pynest: a convenient interface to the nest simulator. Front Neuroinform 2, 12. doi:10.3389/neuro.11.012.2008
  4. Goedeke S, Diesmann M (2008) The mechanism of synchronization in feed-forward neuronal networks. New Journal of Physics 10: 015007; doi:10.1088/1367-2630/10/1/015007
  5. Kriener B, Tetzlaff T, Aertsen A, Diesmann M, Rotter S (2008) Correlations and population dynamics in cortical networks. Neural Computation, 20, 2185-2226
  6. Kumar A, Rotter S, Aertsen A (2008) Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model. J Neurosci, 28(20): 5268
  7. Morrison A, Diesmann M, Gerstner W (2008). Phenomenological models of synaptic plasticity based on spike-timing. Biol Cybern 98(6), 459-478
  8. Plesser HE, Diesmann M (2009). Simplicity and efficiency of integrate-and-fire neuron models. Neural Computation, 21, 353-359
  9. Schrader S, Gruen S, Diesmann M, Gerstein G (2008). Detecting synfire chain activity using massively parallel spike train recording. J Neurophysiol 100, 2165-2176
  10. Tetzlaff T, Rotter S, Stark E, Abeles M, Aertsen A, Diesmann M (2008). Dependence of neuronal correlations on filter characteristics and marginal spike-train statistics. Neural Computation, 20, 2133-2184


Abstracts:

  1. Helias M., Deger M., Rotter S. & Diesmann, M. (2010) Instantaneous non-linear processing by pulse-coupled threshold units. Workshop on circuit modeling and simulation, 15 - 16 March, Ritsumeikan University, Siga, Japan
  2. Helias M., Deger M., Rotter S. & Diesmann, M. (2010) Beyond linear perturbation theory: the instantaneous response of the integrate-and-fire model. Cosyne - Computational and Systems Neuroscience 2010, 25 Feb 2010 - 28 Feb 2010, Salt-Lake City, UT, doi: 10.3389/conf.fnins.2010.03.00081
  3. Diesmann M., Helias M., Deger M., Rotter S. (2009) The non-linear response of the integrate-and-fire neuron to finite synaptic potentials. Neuroscience Research 65 (suppl. 1): S78, P1-a38, doi:10.1016/j.neures.2009.09.290
  4. Helias M., Deger M., Rotter S. & Diesmann M. (2009) Finite synaptic potentials cause a non-linear instantaneous response of the integrate-and-fire model. Eighteenth Annual Computational Neuroscience Meeting CNS*2009, July 18th - 23rd, Berlin, Germany, BMC Neuroscience 2009, 10(Suppl 1):P225
  5. Deger M., Cardanobile S., Helias M., Rotter S. (2009) The Poisson process with dead time captures important statistical features of neural activity. Eighteenth Annual Computational Neuroscience Meeting CNS*2009, July 18th - 23rd, Berlin, Germany, BMC Neuroscience 2009, 10(Suppl 1):P225
  6. Goedeke S, Schwalger T, Diesmann M (2008). Theory of neuronal spike densities for synchronous activity in cortical feed-forward networks. P143 In: 17th Annual Computational Neuroscience Meeting, Portland, USA
  7. Kriener K, Tetzlaff T, Aertsen A, Rotter S (2005) The effect of Dale's principle on network dynamics. 1st Bernstein Symposium for Computational Neuroscience, BCCN Freiburg
  8. Kriener B, Tetzlaff T, Aertsen, A, Diesmann M, Rotter S (2007) Correlations and population dynamics in cortical networks. III-73. In: Computational and Systems Neuroscience, Salt Lake City, USA.
  9. Schrader S, Gruen S, Diesmann M, Gerstein G (2007). Exhibiting the signature of synfire activity in massively parallel spike trains. 103.5 In Proc 37th Meeting Soc for Neurosci, San Diego, USA
  10. Tetzlaff T, Aertsen A, Diesmann M (2005) Time-scale dependence of inter-neuronal spike correlations. In: Zimmermann H, Krieglstein K (eds). Proc 6th Meeting German Neurosci Soc, 30th Göttingen Neurobiol Conf. Neuroforum 1/2005, Suppl: 207B
  11. Tetzlaff T, Rotter S, Aertsen A, Diesmann, M (2006). Time scale dependence of neuronal correlations. T18. In Computational Neuroscience Meeting, Edinburgh, UK.
  12. Tetzlaff T, Rotter S, Aertsen A, Diesmann M (2007) Time scale dependence of neuronal correlations. T37-1B. In: Neuroforum, Feb. vol. XIII, supplement. 7th Göttingen Meeting of the German Neuroscience Society.
  13. Tetzlaff T, Rotter S, Aertsen A, Diesmann M (2007) Time scale dependence of neuronal correlations. 2nd Bernstein Symposium for Computational Neuroscience, Berlin
  14. Tetzlaff T, Rotter S, Aertsen, A, Diesmann M. (2007) Time scale dependence of neuronal correlations. II-15. In: Computational and Systems Neuroscience, Salt Lake City, USA.
     

 

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