scholarly journals Predictive neuronal adaptation as a basis for consciousness.

2021 ◽  
Author(s):  
Artur Luczak
Keyword(s):  
Network Performance ◽  
Brain Activity ◽  
Schema Theory ◽  
Metabolic Energy ◽  
Predictive Processing ◽  
Fast Time ◽  
Neuronal Adaptation ◽  
The Future ◽  
Conscious Processing ◽  
Great Survival

Being able to correctly predict the future and to adjust own actions accordingly, offers great survival advantage. In fact, this could be the main reason for organisms to evolve their brains. The most mysterious feature of brain activity: consciousness, also seems to be related to predicting the future and detecting surprise: a mismatch between actual and predicted situation. Even at the single neuron level, predicting future activity and adapting synaptic inputs accordingly, is the best strategy to maximize metabolic energy for a neuron. Following on those ideas, here we examine if surprise minimization by single neurons could be a basis for consciousness. First, we show in simulations that as a neural network learns a task, then the surprise within neurons, defined as: difference between actual and expected activity, changes similarly as consciousness of a learned skill in humans. Moreover, implementing adaptation of neuronal activity to minimize surprise at fast time scales (tens of ms), resulted in improved network performance. This improvement is likely due to the fact that adapting activity based on the internal predictive model, allows each neuron for a more “educated” response to stimuli. Based on those results, we propose that: neuronal predictive adaptation to minimize surprise could be a basic building block of conscious processing. This is because, adapting activity toward a predicted level, allows neurons to exchange not only information about stimulus but also about its internal model predictions and thus, to build more complex predictive models. To be precise, we provide an equation to quantify consciousness as the amount of surprise minus the size of the adaptation error. Since neuronal adaptation can be studied experimentally, this allows for directly testing our hypothesis. Specifically, we postulate that any substance affecting neuronal adaptation will also affect consciousness. Interestingly, our predictive adaptation hypothesis is consistent with multiple ideas presented previously in diverse theories of consciousness, such as global workspace theory, integrated information, attention schema theory, and predictive processing framework. In summary, we present a theoretical, computational and experimental support for the hypothesis that neuronal adaptation is a possible biological mechanism of conscious processing, and we discuss how this could provide a step toward a unified theory of consciousness.

scholarly journals Predictive Neuronal Adaptation as a Basis for Consciousness

2022 ◽  
Vol 15 ◽  
Author(s):  
Artur Luczak ◽  
Yoshimasa Kubo
Keyword(s):  
Network Performance ◽  
Brain Activity ◽  
Schema Theory ◽  
Metabolic Energy ◽  
Predictive Processing ◽  
Fast Time ◽  
Neuronal Adaptation ◽  
The Future ◽  
Conscious Processing ◽  
The Difference

Being able to correctly predict the future and to adjust own actions accordingly can offer a great survival advantage. In fact, this could be the main reason why brains evolved. Consciousness, the most mysterious feature of brain activity, also seems to be related to predicting the future and detecting surprise: a mismatch between actual and predicted situation. Similarly at a single neuron level, predicting future activity and adapting synaptic inputs accordingly was shown to be the best strategy to maximize the metabolic energy for a neuron. Following on these ideas, here we examined if surprise minimization by single neurons could be a basis for consciousness. First, we showed in simulations that as a neural network learns a new task, then the surprise within neurons (defined as the difference between actual and expected activity) changes similarly to the consciousness of skills in humans. Moreover, implementing adaptation of neuronal activity to minimize surprise at fast time scales (tens of milliseconds) resulted in improved network performance. This improvement is likely because adapting activity based on the internal predictive model allows each neuron to make a more “educated” response to stimuli. Based on those results, we propose that the neuronal predictive adaptation to minimize surprise could be a basic building block of conscious processing. Such adaptation allows neurons to exchange information about own predictions and thus to build more complex predictive models. To be precise, we provide an equation to quantify consciousness as the amount of surprise minus the size of the adaptation error. Since neuronal adaptation can be studied experimentally, this can allow testing directly our hypothesis. Specifically, we postulate that any substance affecting neuronal adaptation will also affect consciousness. Interestingly, our predictive adaptation hypothesis is consistent with multiple ideas presented previously in diverse theories of consciousness, such as global workspace theory, integrated information, attention schema theory, and predictive processing framework. In summary, we present a theoretical, computational, and experimental support for the hypothesis that neuronal adaptation is a possible biological mechanism of conscious processing, and we discuss how this could provide a step toward a unified theory of consciousness.

Building the Arrow of Time… Over Time: A Sequence of Brain Activity Mapping Imagined Events in Time and Space

2018 ◽  
Vol 29 (10) ◽  
pp. 4398-4414 ◽  
Author(s):  
Baptiste Gauthier ◽  
Karin Pestke ◽  
Virginie van Wassenhove
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Spatial Task ◽  
Brain Response ◽  
Time And Space ◽  
Time Resolved ◽  
The Future ◽  
Spatial Dimensions ◽  
Future Events ◽  
Neural Signatures

Abstract When moving, the spatiotemporal unfolding of events is bound to our physical trajectory, and time and space become entangled in episodic memory. When imagining past or future events, or being in different geographical locations, the temporal and spatial dimensions of mental events can be independently accessed and manipulated. Using time-resolved neuroimaging, we characterized brain activity while participants ordered historical events from different mental perspectives in time (e.g., when imagining being 9 years in the future) or in space (e.g., when imagining being in Cayenne). We describe 2 neural signatures of temporal ordinality: an early brain response distinguishing whether participants were mentally in the past, the present or the future (self-projection in time), and a graded activity at event retrieval, indexing the mental distance between the representation of the self in time and the event. Neural signatures of ordinality and symbolic distances in time were distinct from those observed in the homologous spatial task: activity indicating spatial order and distances overlapped in latency in distinct brain regions. We interpret our findings as evidence that the conscious representation of time and space share algorithms (egocentric mapping, distance, and ordinality computations) but different implementations with a distinctive status for the psychological “time arrow.”

Detection and Ignoring of Blackhole Attack in Vanets Networks

2016 ◽  
Vol 6 (2) ◽  
pp. 1-10
Author(s):  
Chaima Bensaid ◽  
Sofiane Boukli Hacene ◽  
Kamel Mohamed Faraoun
Keyword(s):  
Black Hole ◽  
Communication Networks ◽  
Network Performance ◽  
Vehicular Networks ◽  
Data Communication ◽  
Main Idea ◽  
Black Hole Attack ◽  
Malicious Nodes ◽  
The Future ◽  
Detailed Simulation

Vehicular networks or VANET announce as the communication networks of the future, where the mobility is the main idea. These networks should be able to interconnect vehicles. The optimal goal is that these networks will contribute to safer roads and more effective in the future by providing timely information to drivers and concerned authorities. They are therefore vulnerable to many types of attacks among them the black hole attack. In this attack, a malicious node disseminates spurious replies for any route discovery in order to monopolize all data communication and deteriorate network performance. Many studies have focused on detecting and isolating malicious nodes in VANET. In this paper, the authors present two mechanisms to detect this attack. The main goal is detecting as well as bypass cooperative black hole attack. The authors' approaches have been evaluated by the detailed simulation study with NS2 and the simulation results shows an improvement of protocol performance.

scholarly journals Predictive processing: Is the future just a memory?

2020 ◽  
Vol 52 (10) ◽  
pp. 4230-4232
Author(s):  
Thiago F.A. França
Keyword(s):  
Predictive Processing ◽  
The Future

scholarly journals Content-based Dissociation of Hippocampal Involvement in Prediction

2020 ◽  
Vol 32 (3) ◽  
pp. 527-545 ◽  
Author(s):  
Peter Kok ◽  
Lindsay I. Rait ◽  
Nicholas B. Turk-Browne
Keyword(s):  
Brain Activity ◽  
Predictive Processing ◽  
Auditory Cues ◽  
Domain Specific ◽  
Visual Objects ◽  
Complex Shapes ◽  
Different Types ◽  
Human Participants ◽  
Abstract Shapes

Recent work suggests that a key function of the hippocampus is to predict the future. This is thought to depend on its ability to bind inputs over time and space and to retrieve upcoming or missing inputs based on partial cues. In line with this, previous research has revealed prediction-related signals in the hippocampus for complex visual objects, such as fractals and abstract shapes. Implicit in such accounts is that these computations in the hippocampus reflect domain-general processes that apply across different types and modalities of stimuli. An alternative is that the hippocampus plays a more domain-specific role in predictive processing, with the type of stimuli being predicted determining its involvement. To investigate this, we compared hippocampal responses to auditory cues predicting abstract shapes (Experiment 1) versus oriented gratings (Experiment 2). We measured brain activity in male and female human participants using high-resolution fMRI, in combination with inverted encoding models to reconstruct shape and orientation information. Our results revealed that expectations about shape and orientation evoked distinct representations in the hippocampus. For complex shapes, the hippocampus represented which shape was expected, potentially serving as a source of top–down predictions. In contrast, for simple gratings, the hippocampus represented only unexpected orientations, more reminiscent of a prediction error. We discuss several potential explanations for this content-based dissociation in hippocampal function, concluding that the computational role of the hippocampus in predictive processing may depend on the nature and complexity of stimuli.

The Information Theoretic Perspective on Context Aware Network Design

2012 ◽  
Vol 433-440 ◽  
pp. 5073-5077
Author(s):  
Jing Yao Wang ◽  
Meng Jia Li ◽  
Mei Song ◽  
Ying Hai Zhang
Keyword(s):  
Information Theory ◽  
Network Design ◽  
Information Entropy ◽  
Communication Systems ◽  
Network Performance ◽  
Theoretical Framework ◽  
Context Aware ◽  
Information Theoretic ◽  
The Future ◽  
The Relationship

Information theory has made great impact on the research of communication systems. However, analyze and design of networks has not benefited too much from information theory. Therefore, in this paper, we propose the information-theoretical framework of context aware network to explore the relationship between the information and the network performance. We also analyze the information traffic process in context aware network. To illustrate our approach, we analyze the architecture of context aware network by the information entropy produced in the network, and discuss the way to improve the performance of context aware in an information-theoretic perspective. The results in this paper may be also used to design other network and guide the future network design.

scholarly journals Brain Activity in Valuation Regions while Thinking about the Future Predicts Individual Discount Rates

2013 ◽  
Vol 33 (32) ◽  
pp. 13150-13156 ◽  
Author(s):  
N. Cooper ◽  
J. W. Kable ◽  
B. K. Kim ◽  
G. Zauberman
Keyword(s):  
Brain Activity ◽  
Discount Rates ◽  
The Future
Sign in / Sign up

Export Citation Format

Share Document