Neural Networks Biology Questions

Posted: September 17th, 2022

Foreword
The Role of Computational Models in Cognitive Neuroscience
The publication of OReilly and Munakatas Computa- tional Explorations in Cognitive Neuroscience comes at an opportune moment. The field is rapidly growing, with centers and institutes springing up everywhere. Researchers from backgrounds ranging from psychol- ogy to molecular biology are pouring into the field. Eric Kandel has suggested that cognitive neuroscience with its concern about perception, action, memory, lan- guage and selective attentionwill increasingly come to represent the central focus of all neurosciences in the twentyfirst century.
Today, quite a bit of the excitement in the field sur- rounds the use of several important new experimental methodologies. For the study of the neural basis of cognition in humans, fMRI and other imaging modal- ities hold great promise to allow us to visualize the brain while cognition is occurring, and it is likely that there will be ongoing breakthroughs in spatial and tem- poral resolution. Working upward from the molecular level, new genetic methods for creating animals with alterations in the basic functional properties of specific groups of neurons and synapses is allowing detailed exploration of how these cellular and synaptic pro- cesses impact higher processes such as spatial learning in animals. Within neurophysiology, the use of multi- electrode arrays to record from as many as 100 separate neurons at a time has led to new insights into the repre- sentation of information during behavior, and the later reactivation of these representations from memory dur- ing sleep.
With all their marvelous tools, the question arises, do we really need computational models in cognitive
neuroscience? Can we not learn everything we need to know about the neural basis of cognition through ex- perimental investigation? Do we need a book like the present one to explore the principles of neural compu- tation and apply them to the task of understanding how cognition arises from neuronal interactions?
The answer is: Yes, we do need computational mod- els in cognitive neuroscience. To support this answer, I will begin by describing what I take to be one of the cen- tral goals of cognitive neuroscience. I will then describe what we mean by the phrase a computational model and consider the role such models can play in address- ing the central goal. Along the way I hope to indicate some of the shortcomings of experimental research un- dertaken without the aid of computational models and how models can be used to go beyond these limitations. The goal is to make clear exactly what models are, and the role they are intended to play.
First, what is this central goal of cognitive neuro- science? To me, and I think to OReilly, Munakata, and many researchers in the field, the goal is to understand how neural processes give rise to cognition. Typically, cognition is broadly construed to include perception, at- tention, language, memory, problem solving, planning, reasoning, and the coordination and execution of ac- tion. And typically some task or tasks are used to make behavioral observations that tap these underlying pro- cesses; aspects of conscious experience are included, to the extent that they can be subjected to scientific scrutiny through observables (including verbal reports or other readout methods). The processes considered may be ones that take place in a brief interval of time, such as the processes that occur when a human observer reads a visually presented word. Or they may be ones that take place over longer periods of time, such as the processes that occur as a child progresses through var-