Large-scale model of mammalian thalamocortical systems

I’ll be the first to admit that my limited focus area in neuroscience, the levels of molecules and cells, biases me to attend to the trees, while missing the patterns in the whole forest. Many of our best recording and imaging methods, single-unit electrophysiology, fluorescence imaging and multi-unit extracellular arrays give us access to only a very tiny piece of the brain at any given moment. Yet endogenous neural activity is dependent on powerful (and subtle) interactions between geographically distant regions of the brain. Whole-brain measurement techniques, such as functional magnetic resonance imaging, magnetoencephalography, and diffusion tensor imgaging, can measure these interactions, but tend to have poor temporal-spatial resolution. Therefore, in order to understand the gestalt of the brain, it is useful to examine models that integrate our knowledge of single cell morphology and activity patterns, local circuitry, and distant connectivity.

The complexity of any brain model is limited by the computational power available. With 10^11 neurons and 10^15 synapses in the brain, each with a wide degree of possible synaptic strength, the computational power required to precisely model the entire brain is currently unavailable (though the Blue Brain project is trying). But maybe we don’t need every neuron. Maybe we can cull this 10^11 number down to something more manageable, a million, that proportionally represent a large number of the neuronal classes of the cortex. In this PNAS paper, Large-scale model of mammalian thalamocortical systems by Eugene Izhikevich and (Nobel laureate) Gerald Edelman, they demonstrate that many endogenous-like patterns of brain activity spontaneous emerge from just this sort of reduced model.

The model reduces the neuron count, leaves out many deep brain structures, and simplifies spike generation, but leaves intact important long-range connections, cellular morphology, dendritic spike initiation, synaptic plasticity and learning rules and dopaminergic modulation. With a sufficient ‘priming of the pump’ by allowing random miniPSPs to percolate through the network, the wiring organizes itself such that spontaneous activity patterns (delta, beta and gamma oscillations, wave propagation, anticorrelated clustering) emerge that are reminescent of in vivo patterns of activity. They also show the system is chaotic; a single spike added or subtracted evolves a totally different activity pattern after half a second, evoking the specter of determinism and the illusion of free will.

These are still early days, as they have not integrated any sensory input into the system and leave out many important brain structures. Nor do they make any testable predictions with the model. Nevertheless, the model appears to be readily extensible, and as greater understanding of brain regions and greater computing power become available, the power of the model may dramatically increase. For now, this paper is a tantalizing reminder of why many of us were originally attracted to the study of neuroscience, the quest to understand how our brain’s activity creates consciousness, and a context in which to place the little trees we struggle to understand.

Props to Neurochannels for the link.