AUTHOR=Roland Per E. 

TITLE=How far neuroscience is from understanding brains

JOURNAL=Frontiers in Systems Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2023.1147896

DOI=10.3389/fnsys.2023.1147896

ISSN=1662-5137

ABSTRACT=The cellular biology of brains is relatively well understood, but neuroscientists have not yet generated a theory explaining how brains work. Explanations of how neurons collectively operate to produce what brains can do are tentative and incomplete. Without prior assumptions about brain mechanisms, I attempt here to give an overview of the obstacles for progress in neuroscientific understanding of brains and central nervous systems.  Most of the obstacles for our understanding are conceptual. Neuroscience lacks concepts and models rooted in experimental results explaining how neurons interact at all scales. The cerebral cortex is thought to control awake activities, which contrasts with recent experimental results. There is ambiguity distinguishing task related brain activities from spontaneous activities and organized intrinsic activities. Brains are regarded as driven by external and internal stimuli in contrast to their considerable autonomy. Experimental results are explained by sensory inputs, behavior, and psychological concepts. Time and space are regarded as mutually independent variables for spiking, post-synaptic events, and other measured variables. Spatial dynamics, i.e. the general hypothesis that measurements of changes in fundamental brain variables action potentials, transmitter releases, postsynaptic trans-membrane currents et c. propagating in central nervous systems reveal how they work, carries no assumptions.  Combinations of current techniques could reveal many aspects of spatial dynamics of spiking, post-synaptic processing, and plasticity in insects and rodents to start with. But problems defining baseline and reference conditions hinder interpretations of the results. Further, the facts that pooling and averaging of data destroys their underlying dynamics imply that single trial designs and statistics are necessary.