Information processing in the brain, in particular in the cortex, is based on spatiotemporal patterns of electrical activity in neuronal networks. Recently introduced experimental techniques allow the monitoring of these patterns in great detail by simultaneously recording of neuronal activity from a large number of locations in the network (e.g., in cortical brain slices and cultured neuronal networks). To be able to analyze and interpret the flood of data these new techniques produce, we intend to develop (i) mathematical and statistical methods for analyzing spatiotemporal patterns of neuronal activity, and (ii) computational models of neuronal networks to simulate these patterns and understand them in relation to structural and functional connectivity within the network. Goals (i) and (ii) will be pursued in close interaction, whereby the computational models are used to help to develop statistical methods, and the statistical methods in turn are used to test whether the model can capture the characteristics of the experimentally observed patterns. An essential part of (ii) is the development of a stochastic model for the generation of network connectivity and its variation. The methods and models will be validated with the extensive data we have on spatiotemporal patterns in cortical brain slices and cultured neuronal networks. Ultimately, these methods and models are indispensable in the search for key genetic regulators of network development, network activity and animal behavior. In particular, the data on spatiotemporal activity in brain slices obtained from normal (wild type) versus mutant mice (with a known genetic mutation) will be compared.

NETMORPH

We created NETMORPH, a novel simulation framework, unique in that it generates 3D large-scale neuronal networks with realistic neuron morphologies in a stochastic and developmental way. The few modeling frameworks that exist for the generation of neuronal networks either do not consider detailed neuronal morphology (van Ooyen et al., 1995; Butz and van Ooyen, 2006; R. and Ben-Jacob, 2000) or, if they do, do not take into account the developmental aspects of neuronal morphogenesis and synapse formation (Ascoli et al., 2001b; Ascoli, 1999; Gleeson et al., 2007).

NEMORPH incorporates development explicitly, and neuronal morphogenesis is simulated from the perspective of the individual growth cone. The various actions of the growth cone, such as elongation, branching and turning, are described in a stochastic manner that also supports a phenomenological implementation of biophysical processes involved in neurite outgrowth, such as competition for resources between different growth cones of a dendrite or an axon.  In this way, neurons with realistic axonal and dendritic morphologies, including neurite curvature, are generated.

Synapses are formed as neurons grow out, and are determined on the basis of proximity between axonal and dendritic branches. The synaptic connectivity that emerges during successive stages of development can be used to simulate activity dynamics. The most common tools for simulating electrical activity in neurons with realistic morphology and biophysics are NEURON (Hines and Moore, 1993; Moore and Hines, 1995) and GENESIS (Nenadic et al., 2003), and, for simulating activity in large scale neuronal networks, NEOSIM (Howell et al., 2002). However, all these tools do not provide programs for generating neuronal morphologies or detailed connectivity patterns. Simulations of network activity are possible within NETMORPH.

The many applications of our framework include creating detailed connectivity patterns, with axo-dendritic synapses at specific locations in the neuronal morphology; studying how typical connectivity patterns come about during development (e.g., small-world connectivity; Sporns et al., 2004) and how characteristic changes in synaptic connectivity, as observed in brain diseases such as Alzheimer and autism, may arise (Belmonte and Bourgeron, 2006; Scheff et al., 2007); and investigating the complex relationship between neuronal morphology and local and global patterns of synaptic connectivity. Simulations of network development enable analysis of emergent network connectivity and spatio-temporal patterns of activity. One of the first applications of NETMORPH   has been to generate networks that aim to replicate stages of development in cultured networks of dissociated cortical tissue (van Pelt et al., 2004).

See here for some demonstrations of NETMORPH simulations.

CASPAN & Neurovers-IT

The web site of project coordinator Jaap van Pelt: neurodynamics.nl

The Collaborative Discussions (access for CASPAN members only).