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Cognitive Science: Neurons, Their Neighbors, and the Computable Mind

The entire brain is a set of binary coding: a single neuron is either excitatory (1) or inhibitory (0).  An individual neuron, regardless of being a 1 or a 0, sends messages to its neighbors via neurotransmitters and electrical synapses.  Pending what type of messages are sent, the next connected neuron is assigned either a 1 or a 0, then sends its own messages to more neighbors based on its assignment.  This simple pattern of 1s and 0s cascades throughout the entire brain, ultimately resulting in every cognitive function your body performs.  The breaths you are taking, the words you see on this screen, and the posture you have while sitting at your computer are all a result of a specific neural network of 1s and 0s in your brain.

Imagine neurons in the human brains as nodes in the network maps we have been studying.  By this analogy, the edges in neural network maps are the connections between neurons, either excitatory or inhibitory.  The neurons in more active parts of the brain (like the lateral geniculate nucleus, the primary vision center) will be connected to more neighboring neurons than others.  As you descend the neural network from a “focal” neuron, the number of edges branching from neural nodes decreases.  These neurons are more specialized, and only have a little power in the brain to control particular, highly-specialized functions—like, for example, hearing a sound and then recognizing a certain frequency of wave that corresponds to your best friend’s voice.   Similarly, many independent neurons can process a bulk of information and filter it through a neural network to one very specific neural output, such as photoreceptors translating light and color into seeing specific objects.

In this way, the strength of the ties between neurons indicates the particular function of a piece of a neural network.  As we learned in class, power comes with a stronger position in the multi-node network.  Further, local bridges provide connections between larger groups of interacting nodes.  In the same ways, neural networks are designed so that excitatory neurons in a broad functional region with a lot of power can pass messages to more specialized neurons further down the “edge” connections in neural maps.  Some neurons from the edges of particular functional groups form local bridge connections, allowing for dynamic interaction between cognitive functions such as reading a textbook and then remembering a piece of information.  In this way, the entire brain is a network of binary nodes with edges organized with great complexity, working together to form the entire cognitive pathway.

So, since we’re wired with binary code like computer, is the mind programmable?  That is, can a computer be taught to receive particular inputs and produce “human-like” output?  This has been the great debate of cognitive science since the early 20th century, when Alan Turing first conceptualized the idea of programmable computation.  It seems that investigative neuroscience supports the possibility of such artificial intelligence, and it all relies on the complexity of neural networks.

– ejp74

Cognitive Science in One Lesson:

A rendering of a complex neural network:

Information on Alan Turing, the father of computer science:


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