In parallel
with technological advances, we need theories to tie together measurements across the spatial and temporal scales and make predictions of the emergent properties of neurons connected in networks. The term emergent property is borrowed from the physics of complex systems, where it refers to phenomena that cannot be directly traced to their individual components, only to how those components interact. Consider the example of weather—the state of the atmosphere. The temperature of the air is not defined at the atomic scale; it is an emergent property of many atmospheric particles. A weather forecast requires a valid theoretical framework: a model. The model incorporates a set of rules worked out by studying interactions among particles; the actual forecast, PI3K inhibitor however, is not predicted
by simulating the position of every molecule. Rather, the forecast is made on the relevant practical scale by means of measurements of the current state of the atmosphere and models formulated with “coarse-grained” variables such as pressure and temperature and parameters such as the physical shapes of landforms. For the most part, this approach works: we can rely on the National Weather Service to predict tomorrow’s rain. While the separation of microscopic and macroscopic scales is less clear in neuroscience than in atmospheric physics, it is nevertheless a useful analogy: using the ability to predict as a surrogate for understanding, understanding higher cortical functions—perception, for example—by quantifying a large number of individual neurons firing across the brain may GSK2118436 ic50 be impractical; instead, it is probably necessary to use intermediary measures and appropriate mathematical models. enough Also, statistical sampling from neurons of known cell type and connectivity would be preferable to merely increasing the numbers of simultaneously captured spikes. This is because our brains, in contrast to those of invertebrates, appear to be built from large populations of neurons performing the same function, collectively and in a probabilistic way. We, humans, can lose neurons from the age of 20
or earlier without a noticeable effect on cognitive performance. For the nematode C. elegans, by contrast, the loss of a single neuron can have catastrophic effects with respect to survival. Thus, intermediary measures reflecting the ensemble activity of neurons of similar types—which can be localized on the cortical sheet—would offer extremely valuable information. Further, a number of different types of measures might be required to provide the critical input to the model. For example, sleep spindles, Up and Down states, and cortical spreading depression could be described by a set of parameters including those related to subthreshold polarization, intracellular concentration of calcium in neurons and glia, blood flow, and energy consumption.
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