Enabling Neural Engineering Ought To Be The Measure Of Neuroscience
The field of neuroscience naturally focuses its inquiry into neurons. This approach to understanding the brain by studying its parts has been thought to have a greater potential than that of psychology to understand how the brain works, a comment made by no less than Daniel L. Schacter, chair of Harvard’s Department of Psychology, in his book, The Seven Sins of Memory.
However promising the field has been thus far, even the most accomplished neuroscientists will admit that we still do not understand how the brain really works. I would submit that the current reductionist nature of neuroscience has shed much light on the dynamics of how neurons work, but has to a far lesser degree shed light on how neurons process information. The difference between these two lines of inquiry is important for making progress in understanding how the brain works.
A computer, at its core, processes information through the physics of transistors, which are essentially switches that are either on or off. What makes transistors such a powerful foundation for modern computing is that they are controlled by electrical signals, which means that transistors can be controlled by other transistors and therefore structured into useful systems. Understanding the physics of transistors, how quickly they can switch from on to off, how their material composition affects their ability to switch, is crucial for building a microchip. However, this level of understanding is not sufficient to build a microchip. For that, one needs to understand how to structure transistors in such a way to produce digital computation.
Single transistors turn on and off. As a medium for constructing computer architectures, they are relatively straightforward to combine into complex circuits that perform useful functions of logic. Single neurons, quite a bit more complicated, have a vast repertoire of behavior that, among other things, involves integrating signals from multiple sources and sending signals to multiple recipients. It is not at all straightforward to construct explanations of how neurons combine into complex circuits to perform useful behavioral functions. Yet, this is the kind of explanation that neuroscience ultimately must seek in order to fulfill the promise of its potential to unlock the secrets of the brain.
Keeping in mind that computers are different that brains in many important ways, in some sense, we are still at the level of understanding the dynamics of the transistors in neuroscience. Cellular neuroscience, as found in journals such as Neuron, has concerned itself with the dynamics of neurons, rather than their role in processing information. This may seems like a bold statement to some; after all, decades of research has been conducted on sensory systems such as vision, and many aspects of the visual pathway are understood. Furthermore, lesion studies have been demonstrating that certain groups of neurons have certain functions throughout the last hundred years. However, despite the current push to apply information theory into the study of sensory systems, even the most cutting edge work in the field of neuroscience is still only just beginning to incorporate the understanding of what single neurons do with a rigorous account of how they carry out the functions they perform.
Other flavors of neuroscience, such as systems neuroscience and cognitive neuroscience have made inroads towards this goal. For example, excellent progress has been made in understanding the olfactory system of the locust. Here is a system where we understand the inputs, we understand the physiology of the neurons in between, and we have ways of analyzing the dynamics of the system that allow us to predict future behavior. And yet, the difficulty of generalizing these findings to more complex neuronal systems looms large as an obstacle to progress. Some of the best accounts of the activity of neurons in the pre-frontal cortex of monkeys still only provide a descriptive model of the data that fits the observations but does not provide a complete explanation for how the system actually carries out the function that is being modeled. This is the rule, rather than the exception in neuroscience.
One of the key difficulties is that processing information does not happen in single transistors by themselves, nor does it happen in single neurons by themselves. Both systems require the coordinated spatiotemporal organization of an complex system. Engineers over the past 60 years have constructed patterns that help organize transistors into useful components that process information. The most basic functions are those of basic logic, AND, OR, and NOT. Using these tools, arithmetic can be carried out to add, subtract, divide, and multiply numbers encoded in ones and zeros. From there, computer programs can be constructed in a straightforward manner and provide the foundation upon which more complex computer programs can be constructed. We have no equivalent explanation for the functions that assemblies of neurons carry out. We know that neurons excite or inhibit one another, and that the influence between two neurons can change. But neuroscience does not yet have the ability to recombine biologically faithful model neurons into novel circuits to perform novel functions. This indicates that the field lacks principles, or at the very least a sufficient set of well-understood patterns, which explain how neurons are organized together to enable an animal to behave in an appropriate manner in its environment.
In summary, while understanding neuronal dynamics is necessary to understanding the brain, it is not sufficient. I would posit that we must understand how the brain processes information in order to understand it as a whole. A prerequisite to understanding how the brain processes information is to describe principles of neural information processing, which a) explain how neurons perform functions collectively, b) help us to explain the functions of those parts of the brain where they are still unknown, and c) are rigorous enough to enable the design of circuits of neurons (model neurons, or eventually real physical neurons) that perform known and novel functions–true neural engineering.
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