A journey into our neural code (and the mind itself)

8-bit brain credit to Mitch Bolton.

The brain is cool, but it's also really confusing. Given how complex it is, can we develop a model of information from it?

Often, when we study the brain, we get caught up in the middle of a lot of things, both scientifically and metaphysically. It's easy to become dazed and confused when you realize you're trying to understand one of the most "human" parts of science, but also (arguably) the most complex biological thing on the in the universe. And, when you're in the middle of everything, where are you?

Rosa Cao, recently appointed Assistant Professor of IU's Philosophy Department, finds herself at the intersection of philosophy of mind, neuroscience, and cognitive science, and, in her work on frameworks of signaling in the brain, she explains how our different interpretations of what neurons are and how they interact with another can lead us to different conclusions of our information models. Specifically, what we define us the most fundamental functional units and how those units come together change the way we can create cognitive science theories and challenge our understanding of information theory itself.

In order to really understand what science can tell us about notions of information itself, we need a system of how parts of the nerve cells function within their own framework. From our own understanding, we know the brain is made up of different units, from synapses, glia, neurons, and different other things inside our nervous system at various levels. They might even tell us things about how we perceive the world. For example, we know that parts of the brain send signals to other parts, and if the contents of those transmissions are actual states of the world outside of our brain, then it would be difficult for single cells to receive and interpret those signals. But, maybe we don't have to explain everything we perceive outside of the brain? In this case, those transmissions might just be states of the brain itself, and the signals received by synapses would have different information than otherwise. Dr. Cao puts the functions of different units together to better understand how we can apply them to information theory, and, if we change our models of the brain, does that mean we should use something "better" than information to understand it. She asks, "synapses fit the action potential picture more cleanly, and glial activities (e.g., in astrocytes) might also be characterized as signaling. Are synapses or nonneuronal cells better candidates to play the role of functional units? Will informational signaling still be the best model for brain function if we move beyond the neuron doctrine?"

Many of us think about synapses, the junctions between nerve cells, the means through which we process information, and, through the axon tails, the impulses of information move to other cells. The main issue when explaining how information gets sent throughout the central nervous system is that a set of discrete nerve cells doesn't adequately explain how information can be easily transmitted and received. A bunch of more diverse nuclei formed together would be much better, and, from this explanation, Dr. Cao introduces a more "neuron-centric" view (rather than the alternate "synapse-centric" view) of information processing for the brain. In this sense, the neurons play the main role in transmitting information while synapses form as the consequence of functional filtering of incoming signals. By changing the models on which the most basic scientific phenomena contribute to greater models, and, based on the functions of the nervous system at different locations, different models might be suited for different purposes.

Some might say "information" is a cause-effect relationship. According to the David Lewis model of conventional signaling, we can imagine senders and receivers as the basis of information. A "sender" can see the world but not act except to create signs of some kind that can be seen by a receiver. The "receiver" can act, but can only see signs sent by the sender. Put together, the senders and receivers decide on which acts could be good or bad for them. They can choose what is desired and abandon what isn't. This sender-receiver framework allows for signals entering a system to affect the receivers of the system, and the system (receivers and senders together) can generate a response. In this way, we have a cause-effect relationship, and we can build more complex systems on top of it. The sender-receiver framework was used by Claude E. Shannon, the founder of mathematical information theory itself. These information systems can be applied to particles in a physical system, organisms in a population, or even gene networks in a cell. In the 1980's the theory was brought into the philosophy of the mind.

Logical models can help us explain biological functions.

Though Dr. Cao isn't so sure that neurons of the brain can entirely use this model, if we treat the neural framework like this sender-receiver model, we can at least account for things like perception and action. The neurons in our brain send and receive information through a system of senders and receivers. We can also memory as another level of information transmission, but it's still difficult for us to make a sender-receiver framework apply to all sorts of memories.

I became interested with Dr. Cao's work after I realized she had completed a PhD in Cognitive Science before a PhD in Philosophy, and, from this background, she has been able to weave the two disparate fields together like a craft. Being a sucker for the intersection of science and philosophy, I couldn't resist taking a look at her research work. Her switch from science to philosophy might have been a smooth extension from her previous area of research, guided by both the scientific and speculative questions that we all hope to answer. Though I have only explained neuroscience in the context of information theory, the scientific theories can be looked at through other aspects of the mind, such as perception, attention, ideas.

There are many questions that still remain about the information systems. How can an equilibrium be attained? Is there a common interest that the entire system aims for? If this information model is just something we have within all of our cells, then how can there be anything really unique about it to explain our own minds? Maybe we need to look beyond "information" or even abandon our idea of the brain as a "computer" or "system" that processes information. We'll keep thinking about it.

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