Signal Transmission Theory and the Brain

In doing the readings this week what was most salient to me was the similarities of signal transmission theory, the transmission of data, and the brain. Shannon’s information transmission theory as explained by Professor Irvine in his article requires the inception, transmission, and then interpretation of signal by a separate entity. This means that if I wanted to send a message or even an image what is required is the successful transmission of a message, even when marred by error, is left interpretable by the receiving entity. This is in many ways how the brain works.

Take for instance when we see, there is a transmission of information from our eyes, through a nerve, to the vision area in our brain to encode and decode that visual information. The brain doesn’t have access to the visual precept but many small precepts that are strung together to create whatever we are seeing. In the example of using an apple, our eyes break down that apple, and our brain reassembles the message. Now, not all messages will come through, and even some are conflicting but our brain using the information it has and all the other information around it will help fill in the likely gaps (not perfectly but works well enough). This representation is very much like how the internet works, the deconstruction and reconstruction of information to transfer it from one place to the other as mentioned by Professor Irvine and, Denning and Martell.

These signals by themselves have no semantic value though, though we may be able to see the apple at this stage we do not know what the apple is, how it tastes, what it’s color is, or all the things an apple can be used for. This is a completely different stage in the brain and in a lot of ways a different stage of computing. This meaning-making is the next step of the process for computing and requires more than just Shannon’s transference of information. This transference of information is important in making sure the data being received is consistent and interpretable but beyond that point, it requires another process to understand if the message itself is worthwhile.

I am excited to see where this goes and how computers are attempting to jump from information transmitters to information interpreters.


Irvine, Introducing Information Theory: The Context of Electrical Signals Engineering and Digital Encoding (2021).

Prof. Irvine, “Using the Principle of “Levels of Abstraction” to Understand “Information,” “Data,” and “Meaning” (Internet design) (2021).

Peter J. Denning and Craig H. Martell. Great Principles of Computing. Cambridge, MA: The MIT Press, 2015.