Towards Generalizing the Information Theory for Neural Communication

Abstract

Neuroscience extensively uses the information theory to describe neural communication, among others, to calculate the amount of information transferred in neural communication and to attempt the cracking of its coding. There are fierce debates on how information is represented in the brain and during transmission inside the brain. The neural information theory attempts to use the assumptions of electronic communication; despite the experimental evidence that the neural spikes carry information on non-discrete states, they have shallow communication speed, and the spikes’ timing precision matters. Furthermore, in biology, the communication channel is active, which enforces an additional power bandwidth limitation to the neural information transfer. The paper revises the notions needed to describe information transfer in technical and biological communication systems. It argues that biology uses Shannon’s idea outside of its range of validity and introduces an adequate interpretation of information. In addition, the presented time-aware approach to the information theory reveals pieces of evidence for the role of processes (as opposed to states) in neural operations. The generalized information theory describes both kinds of communication, and the classic theory is the particular case of the generalized theory.

On the Role of Speed in Technological and Biological Information Transfer for Computations

“…The " neural dynamics " was studied and " spatiotemporal spreading of population activity was mapped " (Plenz and Aertsen 1996 ) by methods used to describe the static computing methods : interspike intervals histograms, auto-correlation and cross-correlation. Because of the peculiarities of this information handling, there are severe doubts whether the notions of the classic neural information theory are valid for biological computing systems (Végh and Berki 2022 ). The correct method of describing biological computation is still missing, given that the significant item of the computing is missed: the time and position are connected through the information transfer speed (called conduction velocity).…”

“…Given that a physical carrier delivers the information (in all implementations, although the representation of the information is different (Végh and Berki 2022 )), the transfer time is crucial in all computing implementations. As the computing model requires, the operand must reach the operator unit’s input section, and for all physical carriers, the transfer speed is finite.…”

“…We use " the broad definition of learning: use present information to adjust a circuit, to improve future performance " (Sterling and Laughlin 2017 ). However, the definition needs to explain also, what is the "present information" (or information at all (Végh and Berki 2022 )). Figure 4 shows how the model explains in what form the information is stored and adjusted in biological neurons.…”

“…The opposite claim is also valid: we cannot describe a neural network without accounting for the temporal behavior of its neurons. Végh and Berki ( 2022 ) provides a more detailed analysis.…”