Large-scale model of mammalian thalamocortical systems

Abstract

The understanding of the structural and dynamic complexity of mammalian brains is greatly facilitated by computer simulations. We present here a detailed large-scale thalamocortical model based on experimental measures in several mammalian species. The model spans three anatomical scales. (i) It is based on global (white-matter) thalamocortical anatomy obtained by means of diffusion tensor imaging (DTI) of a human brain. (ii) It includes multiple thalamic nuclei and six-layered cortical microcircuitry based on in vitro labeling and three-dimensional reconstruction of single neurons of cat visual cortex. (iii) It has 22 basic types of neurons with appropriate laminar distribution of their branching dendritic trees. The model simulates one million multicompartmental spiking neurons calibrated to reproduce known types of responses recorded in vitro in rats. It has almost half a billion synapses with appropriate receptor kinetics, short-term plasticity, and long-term dendritic spike-timing-dependent synaptic plasticity (dendritic STDP). The model exhibits behavioral regimes of normal brain activity that were not explicitly built-in but emerged spontaneously as the result of interactions among anatomical and dynamic processes. We describe spontaneous activity, sensitivity to changes in individual neurons, emergence of waves and rhythms, and functional connectivity on different scales.brain models ͉ cerebral cortex ͉ diffusion tensor imaging ͉ oscillations ͉ spike-timing-dependent synaptic plasticity T he last decade has seen great progress in our understanding of brain dynamics and underlying neuronal mechanisms. Linking these mechanisms to behavior such as perception is facilitated by large-scale computer simulations of anatomically detailed models of the cerebral cortex (1-3). Although these models have stressed microcircuitry and local dynamics, they have not incorporated multiple cortical regions, corticocortical connections, and synaptic plasticity. In the present article, we describe a large-scale model of the mammalian thalamocortical system that includes these components.Spatiotemporal dynamics of the simulation show that some features of normal brain activity, although not explicitly built into the model, emerged spontaneously. The model exhibited selfsustained activity in the absence of any external sources of input. The behavior of the model was extremely sensitive to contributions of individual spikes: adding or removing one spike of one neuron completely changed the state of the entire cortex in Ͻ0.5 s. Regions of the model brain exhibited collective waves and oscillations of local field potentials in the delta, alpha, and beta ranges, similar to those recorded in humans (4). Simulated fMRI signals exhibited slow fronto-parietal anti-phase oscillations, as seen in humans (5).The shape and connectivity of the model were determined by diffusion tensor imaging (DTI) data for a human brain. Experimental data from three species, human, cat, and rat, were incorporated to build other details of the model.Model ...

MR connectomics: Principles and challenges

“…Towards this objective, a number of computational studies have attempted to combine interregional connectivity data with intra-regional circuit and cellular properties to produce large-scale models of cortical (Ghosh et al, 2008;Honey et al, 2007Honey et al, , 2009Knock et al, 2009) and corticothalamic function (Izhikevich and Edelman, 2008). The value of modeling studies is twofold.…”

Sensory experience modifies spontaneous state dynamics in a large-scale barrel cortical model

“…The dynamics of each neuron in the network is modelled as described by Izhikevich & Edelman (2008). Depending on the values of the parameters a, b, c and d, this model reproduces the spiking dynamics of each of the three neuronal types: RS, FS and LTS (Izhikevich & Edelman 2008).…”

“…Depending on the values of the parameters a, b, c and d, this model reproduces the spiking dynamics of each of the three neuronal types: RS, FS and LTS (Izhikevich & Edelman 2008). The values of a, b, c, d, v r , v t , v peak , C and k used in this study are shown in Table 1.…”

“…Specifically, Holmgren et al (2003) Izhikevich & Edelman (2008). The entries with asterisks are taken from Lefort et al (2009 reported a decrease from 9% probability of two cells within ± 25 µm of each other being connected, to 1% at distances > 100 µm for p II/III -p II/III synapses in visual and somatosensory cortex.…”

“…In particular, spontaneous activity is often found to die out over time (Izhikevich & Edelman 2008) -i.e. it is unstable.…”