Local active information storage as a tool to understand distributed neural information processing

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.

Partial and Entropic Information Decompositions of a Neuronal Modulatory Interaction

“…Since at a local or pointwise level [24][25][26][27][28] (i.e., the terms inside the expectation), information is equal to change in surprisal, I ccs seeks to measure shared information as the change in surprisal that is common to the input variables (hence CCS, Common Change in Surprisal). For two inputs, I ccs is defined as:…”

A Refined Multiscale Self-Entropy Approach for the Assessment of Cardiac Control Complexity: Application to Long QT Syndrome Type 1 Patients

“…Indeed, another notable information-theoretic quantity that can be computed in univariate applications, denoted as sE, received a lot of attention for its ability to quantify regularity [8] and information storage [7,9].…”

“…The higher the CE, the higher the complexity of the series is. CE and sE can be seen as portions of the decomposition of the overall amount of information carried by a series given that their sum is the Shannon entropy (ShE) [7][8][9][10]. While the CE is more widely utilized as a measure of complexity of a series [1][2][3], sE is traditionally exploited to assess regularity and predictability of a process [8] or information stored in it [7,9].…”

“…Information domain analysis of time series provides relevant information about the behavior of complex physiological systems [1][2][3][4][5][6][7]. Indeed, in this domain indexes of dynamical complexity are naturally quantified via self-entropy (sE) and conditional entropy (CE), measuring the portions of the amount of information about the current variable that can and cannot be derived from previous past values of the same variable respectively.…”

“…CE and sE can be seen as portions of the decomposition of the overall amount of information carried by a series given that their sum is the Shannon entropy (ShE) [7][8][9][10]. While the CE is more widely utilized as a measure of complexity of a series [1][2][3], sE is traditionally exploited to assess regularity and predictability of a process [8] or information stored in it [7,9]. CE depends on time scales and this dependence, usually assessed via multiscale conditional entropy (MSCE) approach [11] or via its refinement referred to as refined MSCE (RMSCE) [12], provides relevant information about cardiovascular regulation because it allows the focalization of cardiac control mechanisms acting over an assigned time scale [11][12][13][14][15][16][17].…”