Overview of current large-scale brain activity modeling projects

    In recent years, the field of large-scale modeling of brain activity began to develop actively and an increasing number of mathematicians and neurobiologists are involved in it. In this review I will give a brief overview of the most famous and successful projects in this area. Also, in conclusion, I will describe my thoughts on the prospects and usefulness of the further development of projects of this kind.


    Large-scale models of brain activity

    One of the first projects in this area that was widely publicized and funded was the Blue Brain Project [1], launched by IBM in the summer of 2005 together with the Swiss Federal Institute of Technology in Lausanne.

    The aim of the Blue Brain Project is a detailed simulation of individual neurons and the typical neocortex columns of the brain — neocortical columns — that they form. In the cortex, neurons are organized into elementary units — neocortical columns, of the order of 0.5 mm in diameter and 2 mm in height. Each such column contains about 10 thousand neurons with a complex but ordered structure of communication between themselves and with external neurogroups relative to the column. The factual basis for the simulation was data on the morphology and dynamics of activity of rat neurons and other data on the physiology of the neuron obtained over the past decades of nerve cell research.

    In the framework of this project, the neuron model takes into account the differences between the types of neurons, the spatial geometry of neurons, the distribution of ion channels along the surface of the cell membrane and other parameters of prototype neurons. Developers of the model note that the diversity of the types of neurons combined into a neurogroup is very important for the realization of the cognitive functions of this group, with each type of neuron present in certain layers of the column, and the spatial arrangement, density and volume of distribution of different types of neurons serve as the basis for the ordered distribution of activity over the network generally. The model also takes into account that the exact shape and structure of a neuron affects its electrical properties and the ability to connect with other neurons, and the electrical properties of a neuron are determined by the variety of ion channels.

    For three-dimensional modeling of neurogroups within the framework of the Blue Brain Project, an IBM Blue Gene / L computer is used (Fig. 1), which allows you to simulate the distribution of electrical activity inside a neocortical column in real time.

    Fig. 1. Schematic architecture of the Blue Gene / L supercomputer

    At the end of 2006, it was possible to simulate a single column of a neocortex of a young rat, consisting of 10,000 biologically plausible models of neurons with approximately 3x107 synapses between them.

    At the end of 2007, the completion of Phase I of the Blue Brain project was announced. The results of this phase are:
    • a new model of the grid structure, which automatically, upon request, generates a neural network according to the provided biological data;
    • a new process of simulation and self-regulation, which, before each release, automatically carries out a systematic verification and calibration of the model, to more closely match the biological nature;
    • The first model of a cell-level neocortex column, built exclusively on biological data.

    According to the authors of the project, the obtained cellular models of neurons and the column model as a whole make it possible to directly correlate the simulated processes of activity propagation with similar processes in the biological column of the prototype.

    The continuation of the Blue Brain project is the new IBM project “Cognitive Computing via Synaptronics and Supercomputing” (C2S2), which was announced on November 20, 2008 [2]. The company announced the start of a project to develop a fundamentally new architecture of a computing system that reproduces the organization of interneuronal connections (synapses) and neural networks of the mammalian brain. The project is sponsored by the United States Advanced Defense Research Programs Agency (DARPA) as part of the System of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project. It is this circumstance that explains the actual lack of details about the progress of this project in periodical scientific journals.

    At the center of all research on the C2S2 project is the synapse, which, thanks to its plasticity, provides the formation of individual experience. It is planned to develop models of neural networks with the number and density of distribution of synapses comparable with the corresponding parameters in living organisms. It is noted that the brain, rather, is not a neural, but a synaptic network, and thinking is a result of the biochemical organization of the brain.

    If the project is successful, according to its participants, a fundamentally new class of artificial cognitive systems will be born, a new paradigm of computational architecture with numerous practical applications in all areas of human activity.

    One of the most striking projects for large-scale brain modeling was carried out at The Neuroscience Institute by Eugene Izhikevich and Gerald Edelman.

    In 2007, they modeled the mammalian thalamocortical system based on data on the human brain [3]. This model simulates the work of a million spike neurons, which are calibrated to repeat the behavior of known types of neurons observed in vitro in the rat brain.

    Fig. 2. A simplified diagram of the microcircuit structure of the laminar cortex (above) and the thalamus nuclei (below)

    As a model of the neuron, the feminological model proposed by Izhikevich [4] is used. In the modeling process, 22 types of neural cells were used (Fig. 2), which are obtained by changing the parameters of the Izhikevich model. Almost half a billion synapses with corresponding receptors, short-term synaptic plasticity, and long-term STDP plasticity were used to connect neurons. In fig. 3 presents a dynamic visualization of simulation results.

    Fig. 3. Propagating waves in the Izhikevich model.
    (Red dots indicate spikes of exciting neurons, black dots indicate inhibitory neurons)


    At the end of this review, as I mentioned earlier, I would like to say a few words about the feasibility of such a large-scale modeling.

    In the projects presented above, the brain is considered as some autonomous structure that can exist separately from the rest of the organism and, moreover, the environment. Thus, the question of assessing the quality of simulation results becomes unclear - in which case will we understand that it is successful? Before the simulated brain do not pose any tasks, do not put in any environment. In fact, the need for purposeful behavior and achieving an adaptive result are not considered in such projects. They are aimed only at a detailed reproduction of the physical structure observed in the brain of real animals. Most likely, the consideration of the task of purposeful behavior in such projects is impossible, since with accuracy repeating the physical structure of the brain,

    The participants in the Blue Brain project, in particular, argue that the development of their research will help in the creation of AI in a fairly short time (the next 20-25 years). This statement sounds at least
    quite loud, but there is one fact that does not allow us to believe in it. Basically, these studies are aimed at studying the distribution of activity in the brain and rhythm modeling. However, in the framework of these projects, almost no attention is paid to training, which most likely negates the usefulness of developments in this area as a basis for creating AI.


    [1] . Markram H. "The blue brain project." // Nat Rev Neurosci. Vol. 7, pp. 153-160 (2006).
    [2] . IBM Pressroom [Electronic resource] / "IBM Seeks to Build the Computer of the Future Based on Insights from the Brain" - www-03.ibm.com/press/us/en/pressrelease/26123.wss#release
    [3] . Izhikevich E., Edelman G. "Large-scale model of mammalian thalamocortical systems." // PNAS. Vol. 105, no.9, pp. 3593-3598 (2008).
    [4] . Izhikevich E. "Simple Model of Spiking Neurons." // IEEE Transactions on Neural Networks. Vol. 14, no. 6 (2003).

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