Neural Coding of Cell Assemblies via Spike-Timing Self-Information

Abstract

Cracking brain's neural code is of general interest. In contrast to the traditional view that enormous spike variability in resting states and stimulus-triggered responses reflects noise, here, we examine the “Neural Self-Information Theory” that the interspike-interval (ISI), or the silence-duration between 2 adjoining spikes, carries self-information that is inversely proportional to its variability-probability. Specifically, higher-probability ISIs convey minimal information because they reflect the ground state, whereas lower-probability ISIs carry more information, in the form of “positive” or “negative surprisals,” signifying the excitatory or inhibitory shifts from the ground state, respectively. These surprisals serve as the quanta of information to construct temporally coordinated cell-assembly ternary codes representing real-time cognitions. Accordingly, we devised a general decoding method and unbiasedly uncovered 15 cell assemblies underlying different sleep cycles, fear-memory experiences, spatial navigation, and 5-choice serial-reaction time (5CSRT) visual-discrimination behaviors. We further revealed that robust cell-assembly codes were generated by ISI surprisals constituted of ~20% of the skewed ISI gamma-distribution tails, conforming to the “Pareto Principle” that specifies, for many events—including communication—roughly 80% of the output or consequences come from 20% of the input or causes. These results demonstrate that real-time neural coding arises from the temporal assembly of neural-clique members via silence variability-based self-information codes.

Geometrical Information Flow Regulated by Time Lengths: An Initial Approach

“…However, the results points for a perfect prediction for small periods of time, it confirms time as a factor that disrupts predictability regarding behavior of variables rather than potential information entropy [16]. When considering this thesis as a confirmation of the fact that thermodynamics concept given by Shannon [32] assumes, for these results of forecasting, the small effect in which each binary based interaction event tend to give several unpredictable outcomes due to diversity of pathways, but, nonetheless, those features are time regulated [3,4,9,11,13,16,19,22,28,30,31,[33][34][35][36][37][38], and phenomena of this kind are promoted by the flow of sequences among one unit to another one, composing the whole system as far as it has enough time to express its potential phase spaces.…”

“…What if a duality based phenomena or other chaotic systems can be sustained by binary based events and have regulations caused by time lengths? [3,4,9,11,13,16,22,28,30,31] The intervention at binary based thermodynamics scope is obtained as far as its expressions can be time regulated and dependent on the specific internal logic of variables interacting within the system, and it means by axiomatic reasons, making it possible to dismantle (control) the binary valued orientation output of interactions in terms of flow directionality for multiple variable based events. Figure 19, a constant reduction of information flow until it reaches 0 (zero for both variables interaction).…”

“…The transfer of entropy among different samples could be described by appropriate methods in the previous condition adopted in this article example. Also, the proposed model analyzed gives a general view of binary system functioning for realistic events, system main concepts that organizes the events (iteration, frequency, and time regulations), a model for information theory analysis, probabilistic system analysis, empirical experiments analogous to the example given in this article, information entropy flows, and specific researches to be carried out in future time, such as: -microorganism's reproduction flow [33], infection speed [34]; -vector based diseases [35]; -expenditures management and analysis of massive administrative units (agents) [2,28]; -water and electrical energy distribution over massive administration units commonly found in public administration of cities or countries [11,28,31]; -theoretical and empirical model for resource distribution considering containment, reduction, or regulation of resources production Vs consumption [11,13]; -logistics aspects of administration and commerce flow [36]; -mathematical model for entropy, geometry, and time regulations for nonlinear dynamic events [16,21]; -engineering of materials [37]; -civil engineering or related areas [3,4,6,7]; -language processing regarding space and time accelerations or other biological aspects [22]; -metaphysical concepts for physical and mathematical investigations regarding iteration, frequency and time [15,20]; -deterministic to chaos event's investigations [21,37]; -duality based and quantum based phenomena, such as economics and particles and other fields of knowledge related to the issue [30]; and, -computing, networking, and communications [38].…”

“…As a model of analysis, the microstructure of events that compose water expenditures will be represented by a single framework, in which concepts can be analyzed regarding how modifications at the microstructure can affect the macrostructure as well. Considering a main concept of the theory of information, in which low probabilities of events contain more information than high probabilities [22], the methodology demonstrates how in an ideal model, variables assume a Bernoulli binary entropy information modifying its possible probabilities and ergodicity due to the time aspect of the event and the flow of information. The theoretical experiment can result in the following situations according to Figure 3.…”

“…When the probabilities between the two systems are identical and not observable in Bernoulli's method in terms of probability density function regarding the behavior of the two systems as compared to each other in the function Y, it can be of use considering the frequency in which variables x 1 and x 2 have their iterated behavior regulated by the time. Therefore, to conceive the analysis as information entropy [22] of the system, the variables considered assume an evolution on time in bits [24].…”