Abstract
Every act of information processing can in principle be decomposed into the component operations of information storage, transfer, and modification. Yet, while this is easily done for today's digital computers, the application of these concepts to neural information processing was hampered by the lack of proper mathematical definitions of these operations on information. Recently, definitions were given for the dynamics of these information processing operations on a local scale in space and time in a distributed system, and the specific concept of local active information storage was successfully applied to the analysis and optimization of artificial neural systems. However, no attempt to measure the space-time dynamics of local active information storage in neural data has been made to date. Here we measure local active information storage on a local scale in time and space in voltage sensitive dye imaging data from area 18 of the cat. We show that storage reflects neural properties such as stimulus preferences and surprise upon unexpected stimulus change, and in area 18 reflects the abstract concept of an ongoing stimulus despite the locally random nature of this stimulus. We suggest that LAIS will be a useful quantity to test theories of cortical function, such as predictive coding.
Highlights
It is commonplace to state that brains exist to “process information.” Curiously enough, it is much more difficult to exactly quantify this putative processing of information
To enable local active information storage (LAIS) estimation from a sufficient amount of samples, we considered the data pixels as homogeneous variables executing comparable state transitions, such that the pixels form a physical ensemble in terms of information storage dynamics
In contrast to this spatially highly selective elevation of LAIS values under stimulation, there was a sharp drop in LAIS values at approximately 40 ms after stimulus onset, with negative LAIS values measured at many pixels (Figure 1, 40 ms window; Figure 2, middle column; Figure 3, lower row)
Summary
It is commonplace to state that brains exist to “process information.” Curiously enough, it is much more difficult to exactly quantify this putative processing of information. While in digital computers the distinction between information storage, transfer and modification comes practically for free, in neural systems separating the components of distributed information processing requires thorough mathematical definitions of information storage, transfer and modification. Such definitions, let alone a conceptual understanding of what the terms meant in distributed information processing, were unavailable until very recently (Langton, 1990; Mitchell, 1998; Lizier, 2013)
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