Rethinking Brain Mechanisms in the Light of Evolution

Join us for a talk by Paul Cisek, Professor in the Department of Neuroscience at University of Montreal. This talk is part of the Kempner Seminar Series, a research-level seminar series on recent advances in the field.

In theoretical neuroscience, it is generally assumed that the principal function of the brain is “information processing”: the encoding and manipulation of information about the world that can be used to build knowledge, make decisions and produce plans of action. This view leads to a subdivision of brain functions into putative processes such as object recognition, memory storage and retrieval, decision-making, action planning, etc., inspiring the search for the neural correlates of these processes and the design of artificial systems that mimic them. However, neurophysiological data do not support many of the predictions of these classic subdivisions. Instead, there is divergence and broad distribution of functions that should be unified, mixed representations combining functions that should be distinct, and a general incompatibility with the conceptual subdivisions posited by classical theories of information processing. In this talk, I will explore the possibility of resynthesizing a different set of functional distinctions, guided by the growing body of data on the evolutionary process that produced the human brain. I will summarize, in chronological order, a proposed sequence of innovations that appeared in nervous systems along the lineage that leads from the earliest multicellular animals to humans. Along the way, functional subdivisions and elaborations will be introduced in parallel with the neural specializations that made them possible, gradually building up an alternative conceptual taxonomy of brain functions. The resulting theoretical framework supports the proposal, made for many decades, that the principal function of the brain is not to build knowledge about the world, but to govern interaction with the world through closed-loop feedback control. This shift of perspective leads one to emphasize computational mechanisms that prioritize pragmatic outcomes over decoding accuracy, mixing variables in just the kinds of ways observed in real neural data. I suggest that this alternative, evolution-based taxonomy may better delineate the functional pieces into which the brain is organized, can offer a more natural mapping between behavior and neural mechanisms, and can inspire architectures for artificial systems that can more robustly manage real-time interaction with the real world.