scholarly journals Outline of a Brain Model for Self-Observing Agents

2021 ◽  
pp. 2150008
Author(s):  
Bernhard J. Mitterauer
Keyword(s):  
Information Processing ◽  
Sensory Information ◽  
Neuronal System ◽  
Qualitative Information ◽  
Machine Consciousness ◽  
Double Structure ◽  
Behavior Intentions ◽  
Elementary Principle ◽  
The Brain

Brain-inspired models for conscious robots should refer to the cellular double structure of the brain, consisting of the neuronal system and the glial system, embodying two ontological realms. Therefore, a purely neurobiological approach to machine consciousness is biased by an ontological fault in exclusively referring to the neuronal system. The brain model for self-observing agents outlined in this paper focuses on the glial-neuronal synaptic units (tripartite synapses). Whereas the neuronal component of the synapse embodies objective subjectivity processing sensory information, the glial component (astrocyte) embodies subjective subjectivity generating subjective behavior (intentions, consciousness) in its interactions with the neuronal part of the synapse. The elementary principle of the implementation of self-observing agents is this: a brain is capable of self-observation, if the concept of intention to observe something and the concept of the observed are located in different places. Based on a formalism of qualitative information processing, the architecture of self-observation is described in increasing complexity, building networks. It is suggested that if a robot brain is equipped with a network of modules for self-observation, the robot may generate subjective perspectives of self-observation indicating self-consciousness.

scholarly journals Attention Promotes the Neural Encoding of Prediction Errors

2019 ◽  
Author(s):  
Cooper A. Smout ◽  
Matthew F. Tang ◽  
Marta I. Garrido ◽  
Jason B. Mattingley
Keyword(s):  
Information Processing ◽  
Coding Theory ◽  
Predictive Coding ◽  
Sensory Information ◽  
Neural Response ◽  
Prediction Errors ◽  
Feature Information ◽  
The Difference ◽  
Stimulus Features ◽  
The Brain

AbstractThe human brain is thought to optimise the encoding of incoming sensory information through two principal mechanisms: prediction uses stored information to guide the interpretation of forthcoming sensory events, and attention prioritizes these events according to their behavioural relevance. Despite the ubiquitous contributions of attention and prediction to various aspects of perception and cognition, it remains unknown how they interact to modulate information processing in the brain. A recent extension of predictive coding theory suggests that attention optimises the expected precision of predictions by modulating the synaptic gain of prediction error units. Since prediction errors code for the difference between predictions and sensory signals, this model would suggest that attention increases the selectivity for mismatch information in the neural response to a surprising stimulus. Alternative predictive coding models proposes that attention increases the activity of prediction (or ‘representation’) neurons, and would therefore suggest that attention and prediction synergistically modulate selectivity for feature information in the brain. Here we applied multivariate forward encoding techniques to neural activity recorded via electroencephalography (EEG) as human observers performed a simple visual task, to test for the effect of attention on both mismatch and feature information in the neural response to surprising stimuli. Participants attended or ignored a periodic stream of gratings, the orientations of which could be either predictable, surprising, or unpredictable. We found that surprising stimuli evoked neural responses that were encoded according to the difference between predicted and observed stimulus features, and that attention facilitated the encoding of this type of information in the brain. These findings advance our understanding of how attention and prediction modulate information processing in the brain, and support the theory that attention optimises precision expectations during hierarchical inference by increasing the gain of prediction errors.

Neurophysiological disturbances of cognitive processing in Alzheimer and multi-infarct dementia. An evoked potential study

1994 ◽  
Vol 6 (4) ◽  
pp. 69-73
Author(s):  
E.J. Colon
Keyword(s):  
Cerebral Cortex ◽  
Information Processing ◽  
Evoked Potentials ◽  
Cognitive Processing ◽  
Glial Cells ◽  
Evoked Potential ◽  
Sensory Information ◽  
Short Latency ◽  
And Storage ◽  
The Brain

SummaryDisturbances in information processing can be established by means of evoked potentials (EP). Sensory information is transported over the white myelinated fibers towards the brain. Classification and storage will take place in the cortical grey. This kind of information processing can be made visible by means of EP's.In 9 patients with dementia. Alzheimer's type and 7 patients with multi-infarct dementia a delay in information processing has been established by means of EP's. In multi-infarct dementia also a delay in short latency components was determined. The generators of various parts of the information processing in the cerebral cortex have been delayed and distorted. We speculate that, beside loss of neurons in the cerebral cortex, an alteration in cortical glial cells might be the cause of some disturbances in patients with dementia.

Credo for optimality

2018 ◽  
Vol 41 ◽  
Author(s):  
Alan A. Stocker
Keyword(s):  
Decision Making ◽  
Information Processing ◽  
Sensory Information ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Sensory Information Processing ◽  
The Brain ◽  
Additional Constraints

AbstractOptimal or suboptimal, Rahnev & Denison (R&D) rightly argue that this ill-defined distinction is not useful when comparing models of perceptual decision making. However, what they miss is how valuable the focus on optimality has been in deriving these models in the first place. Rather than prematurely abandon the optimality assumption, we should refine this successful normative hypothesis with additional constraints that capture specific limitations of (sensory) information processing in the brain.

Relation Between Level of Prefrontal Activity and Subject's Performance

1999 ◽  
Vol 13 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Laurence Casini ◽  
Françoise Macar ◽  
Marie-Hélène Giard
Keyword(s):  
Brain Activity ◽  
Sensory Information ◽  
Practice Phase ◽  
Trained Subjects ◽  
Anagram Task ◽  
Economic Information ◽  
Long Duration ◽  
Level Of Activity ◽  
The Brain ◽  
Processing Mechanism

Abstract The experiment reported here was aimed at determining whether the level of brain activity can be related to performance in trained subjects. Two tasks were compared: a temporal and a linguistic task. An array of four letters appeared on a screen. In the temporal task, subjects had to decide whether the letters remained on the screen for a short or a long duration as learned in a practice phase. In the linguistic task, they had to determine whether the four letters could form a word or not (anagram task). These tasks allowed us to compare the level of brain activity obtained in correct and incorrect responses. The current density measures recorded over prefrontal areas showed a relationship between the performance and the level of activity in the temporal task only. The level of activity obtained with correct responses was lower than that obtained with incorrect responses. This suggests that a good temporal performance could be the result of an efficacious, but economic, information-processing mechanism in the brain. In addition, the absence of this relation in the anagram task results in the question of whether this relation is specific to the processing of sensory information only.

Measuring the World

2018 ◽  
Author(s):  
Ann-Sophie Barwich
Keyword(s):  
Sensory Information ◽  
Network Models ◽  
Model System ◽  
Stimulus Input ◽  
Expectancy Effects ◽  
Neural Network Models ◽  
Perceptual Object ◽  
External Objects ◽  
The Common ◽  
The Brain

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes.

Event-Related Potentials in Psychiatry: Approaches to Research and Clinical Applications

1983 ◽  
Vol 17 (4) ◽  
pp. 307-318 ◽  
Author(s):  
H. G. Stampfer
Keyword(s):  
Information Processing ◽  
Psychiatric Disorders ◽  
Rapid Development ◽  
Event Related Potentials ◽  
Clinical Applications ◽  
Direct Access ◽  
Practical Application ◽  
Clinical Methods ◽  
Related Potentials ◽  
The Brain

This article suggests that the potential usefulness of event-related potentials in psychiatry has not been fully explored because of the limitations of various approaches to research adopted to date, and because the field is still undergoing rapid development. Newer approaches to data acquisition and methods of analysis, combined with closer co-operation between medical and physical scientists, will help to establish the practical application of these signals in psychiatric disorders and assist our understanding of psychophysiological information processing in the brain. Finally, it is suggested that psychiatrists should seek to understand these techniques and the data they generate, since they provide more direct access to measures of complex cerebral processes than current clinical methods.

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