Large-scale models in computational neuroscience: methods and dynamical phenomena

July 19 and 20, 2000
Brugge, Belgium

Several models are being published nowadays, describing neuronal behaviour at the circuit or system's level. These large-scale models use divergent methodologies, but often show convergent dynamical phenomena. This workshop tries to cover the different methods used, and to confront the often similar dynamics observed in models of completely different systems. Attention will be paid to the following topics.

  1. Approaches, Methods and Techniques

    The design of large-scale models requires a choice of state variables and of the level of detail, and this way a selection is already made of which phenomena might be observed. Analyzing data from biological systems may help in this selection and give clues for further modelling efforts. The applied models range from statistical (mean field approach, population models) to deterministic (simulating networks of boolean units, integrate-and-fire point neurons or detailed multicompartmental neurons with active channels). Accordingly, state variables may represent mean firing rates, membrane potentials, input conductances or oscillation phases. Solutions may be obtained by solving differential equations analytically, numerically or qualitatively (phase-plane).

  2. Dynamic Phenomena I: Oscillations and Synchronization

    Similar dynamics (oscillations, propagating waves, synchronizations and desynchronizations) have been observed experimentally and described in models of large-scale systems with different architectures and functions (cerebellum, hippocampus, neocortex). Important questions concern therefore both the mechanisms of generation and the relevance of these oscillations for the network functioning.

  3. Dynamic Phenomena II: Plasticity

    An inceasingly recognized aspect of large-scale dynamics is plasticity. Beyond the usually accepted plasticity-memory formation paradigm, in large-scale networks it is particularly interesting to see how the two levels of dynamics (activity dynamics vs. synaptic weight dynamics) interact, i.e. how oscillations facilitate plasticity, and on the other side, how modification of network structure changes patterns of oscillations and synchrony.

This is a mini-symposium. A provisional list of speakers is as follows.

  1. Approaches, Methods and Techniques
    • Somogyvari, State cycles in random Boolean networks: an analytical study.
    • Kiss, The statistical population model: from brain slices to whole brains.
  2. Dynamic Phenomena I: Oscillations and Synchronization
    • Maex, Effect of synchronous oscillations on firing rate in a model of the cerebellar granular layer.
    • Kozma, Emergence of un-correlated common-mode oscillations in the sensory cortex - modeling and experiments.
  3. Dynamic Phenomena II: Plasticity
    • Salinas, Stability of asynchronous firing states in networks with synaptic adaptation.
    • Lorincz, A two-phase computational model training long-term memories in the entorhinal-hippocampal loop.
    • Zalanyi and Szalisznyo. Excitatory / inhibitory effects of the GABA(A) synapse have a beneficial role in synaptic weight resetting in the hippocampus.

Organized by Mate Lengyel and Reinoud Maex

Note: This workshop is supported by a Hungaro-Flemish collaboration grant. Participants with a Hungarian citizenship may receive a reimbursement. For more details, please mail the second organizer at

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Last modified 24.5.00 Maneesh Sahani.