The Major Options

2019 ◽  
pp. 133-144
Author(s):  
Dale Purves
Keyword(s):  
Brain Function ◽  
Predictive Coding ◽  
Physical Reality ◽  
Neural Computation ◽  
Operating Principle ◽  
Nervous Systems ◽  
Empirical Basis ◽  
Statistical Inferences ◽  
Stimulus Features

Given the argument and evidence that the operating principle of nervous systems is to generate neural associations on a wholly empirical basis, it makes sense to compare this framework with other ideas that neuroscientists have proposed for understanding brain function. These concepts fall into several broad categories: (1) neural operation based on detecting, computing, and representing stimulus features as such; (2) neural computation and representation based on statistical inferences about physical reality; and (3) neural operation based on efficient and/or predictive coding. Another concept pertinent to all these ideas, including neural operation as empirical association, is whether nervous systems are carrying out computations. This section reviews these conceptual options and some implications that follow.

A challenge for predictive coding: Representational or experiential diversity?

2020 ◽  
Vol 43 ◽  
Author(s):  
Martina G. Vilas ◽  
Lucia Melloni
Keyword(s):  
Brain Function ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Process Theory

Abstract To become a unifying theory of brain function, predictive processing (PP) must accommodate its rich representational diversity. Gilead et al. claim such diversity requires a multi-process theory, and thus is out of reach for PP, which postulates a universal canonical computation. We contend this argument and instead propose that PP fails to account for the experiential level of representations.

Organisms With Nervous Systems

2019 ◽  
pp. 23-34
Author(s):  
Dale Purves
Keyword(s):  
Nervous System ◽  
Operating Principle ◽  
The Other ◽  
Neural Systems ◽  
Animal Kingdom ◽  
Nervous Systems ◽  
Life On Earth

Definitions of the term “animals” in dictionaries and textbooks are surprisingly vague. The characteristics usually mentioned are eukaryotic, multicellular, heterotrophic, sexually reproducing, and capable of rapid and independent movement. But some or all of these properties are characteristic of many organisms in the other kingdoms of life on Earth. In fact, the major distinguishing feature of animals in most cases is the presence of a nervous system. But if nervous systems are indeed one of the main attributes that distinguish organisms in the animal kingdom, what exactly are nervous systems and what advantages do they bring? Without at least some provisional answers, seeking the operating principle of neural systems would be futile.

scholarly journals Can expectation suppression be explained by reduced attention to predictable stimuli?

2020 ◽  
Author(s):  
Arjen Alink ◽  
Helen Blank
Keyword(s):  
Predictive Coding ◽  
Additional Research ◽  
Prediction Errors ◽  
Evoked Responses ◽  
Suppression Effect ◽  
Attention Effect ◽  
Neural Representations ◽  
Stimulus Features

AbstractThe expectation-suppression effect – reduced stimulus-evoked responses to expected stimuli – is widely considered to be an empirical hallmark of reduced prediction errors in the framework of predictive coding. Here we challenge this notion by proposing that this phenomenon can also be explained by a reduced attention effect. Specifically, we argue that reduced responses to predictable stimuli can also be explained by a reduced saliency-driven allocation of attention. To resolve whether expectation suppression is best explained by attention or predictive coding, additional research is needed to determine whether attention effects precede the encoding of expectation violations (or vice versa) and to reveal how expectations change neural representations of stimulus features.

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.

scholarly journals Predictive pre-activation of orthographic and lexical-semantic representations facilitates visual word recognition

2020 ◽  
Author(s):  
Susanne Eisenhauer ◽  
Benjamin Gagl ◽  
Christian J. Fiebach
Keyword(s):  
Word Recognition ◽  
Visual Word Recognition ◽  
Predictive Coding ◽  
Visual Word ◽  
Semantic Representations ◽  
Lexical Semantic ◽  
Brain Responses ◽  
Linguistic Representations ◽  
Context Dependent ◽  
Stimulus Features

AbstractTo a crucial extent, the efficiency of reading results from the fact that visual word recognition is faster in predictive contexts. Predictive coding models suggest that this facilitation results from pre-activation of predictable stimulus features across multiple representational levels before stimulus onset. Still, it is not sufficiently understood which aspects of the rich set of linguistic representations that are activated during reading – visual, orthographic, phonological, and/or lexical-semantic – contribute to context-dependent facilitation. To investigate in detail which linguistic representations are pre-activated in a predictive context and how they affect subsequent stimulus processing, we combined a well-controlled repetition priming paradigm, including words and pseudowords (i.e., pronounceable nonwords), with behavioral and magnetoencephalography measurements. For statistical analysis, we used linear mixed modeling, which we found had a higher statistical power compared to conventional multivariate pattern decoding analysis. Behavioral data from 49 participants indicate that word predictability (i.e., context present vs. absent) facilitated orthographic and lexical-semantic, but not visual or phonological processes. Magnetoencephalography data from 38 participants show sustained activation of orthographic and lexical-semantic representations in the interval before processing the predicted stimulus, suggesting selective pre-activation at multiple levels of linguistic representation as proposed by predictive coding. However, we found more robust lexical-semantic representations when processing predictable in contrast to unpredictable letter strings, and pre-activation effects mainly resembled brain responses elicited when processing the expected letter string. This finding suggests that pre-activation did not result in ‘explaining away’ predictable stimulus features, but rather in a ‘sharpening’ of brain responses involved in word processing.Impact StatementVisual word recognition is facilitated in predictive contexts. Predictive coding postulates that context-dependent facilitation involves the pre-activation of expected stimulus features, but it is not clear on which linguistic representations this mechanism relies during word recognition. Combining magnetoencephalography with high-powered linear mixed modeling, we show that context-dependent facilitation relies on pre-activation of orthographic and lexical-semantic representations in neuronal signals before actually perceiving an expected word.

Temporal Order Detection and Coding in Nervous Systems

2013 ◽  
Vol 25 (2) ◽  
pp. 510-531 ◽  
Author(s):  
Klaus M. Stiefel ◽  
Jonathan Tapson ◽  
André van Schaik
Keyword(s):  
Cortical Neurons ◽  
Temporal Order ◽  
Detection System ◽  
Temporal Coding ◽  
Visual Direction ◽  
Nervous Systems ◽  
Somatosensory Stimulus ◽  
Direction Detection ◽  
Location Detection ◽  
Stimulus Features

This letter discusses temporal order coding and detection in nervous systems. Detection of temporal order in the external world is an adaptive function of nervous systems. In addition, coding based on the temporal order of signals can be used as an internal code. Such temporal order coding is a subset of temporal coding. We discuss two examples of processing the temporal order of external events: the auditory location detection system in birds and the visual direction detection system in flies. We then discuss how somatosensory stimulus intensities are translated into a temporal order code in the human peripheral nervous system. We next turn our attention to input order coding in the mammalian cortex. We review work demonstrating the capabilities of cortical neurons for detecting input order. We then discuss research refuting and demonstrating the representation of stimulus features in the cortex by means of input order. After some general theoretical considerations on input order detection and coding, we conclude by discussing the existing and potential use of input order coding in neuromorphic engineering.

Brain-Based Devices for the Study of Nervous Systems and the Development of Intelligent Machines

2005 ◽  
Vol 11 (1-2) ◽  
pp. 63-77 ◽  
Author(s):  
Jeffrey L. Krichmar ◽  
Gerald M. Edelman
Keyword(s):  
Nervous System ◽  
Animal Models ◽  
Computational Modeling ◽  
Brain Function ◽  
Behavioral Task ◽  
Nervous Systems ◽  
Practical Applications ◽  
Intelligent Machines ◽  
Levels Of Organization ◽  
Simultaneous Study

The simultaneous study of brain function at all levels of organization is difficult to undertake with current experimental tools. Present day electrophysiology only allows the recording of at most hundreds of neurons while an animal is performing a behavioral task. Because of this limitation and the sheer complexity of the nervous system, computational modeling has become essential in developing theories of brain function. Accordingly, our group has constructed a series of brain-based devices (BBDs), that is, physical devices with simulated nervous systems that guide behavior, to serve as a heuristic for testing theories of brain function. Unlike animal models, BBDs permit analysis of activity at all levels of the nervous system as the device behaves in its environment. Although the principal focus of developing BBDs has been to test theories of brain function, this type of modeling may also provide a basis for robotic design and practical applications.

Grounding predictive coding models in empirical neuroscience research

2013 ◽  
Vol 36 (3) ◽  
pp. 210-211 ◽  
Author(s):  
Tobias Egner ◽  
Christopher Summerfield
Keyword(s):  
General Theory ◽  
Brain Function ◽  
Predictive Coding ◽  
Status Quo ◽  
Convincing Case ◽  
Neuroscience Research ◽  
The Status

AbstractClark makes a convincing case for the merits of conceptualizing brains as hierarchical prediction machines. This perspective has the potential to provide an elegant and powerful general theory of brain function, but it will ultimately stand or fall with evidence from basic neuroscience research. Here, we characterize the status quo of that evidence and highlight important avenues for future investigations.

scholarly journals Experience-dependent refinement of natural appetitive visual behavior in mice

2020 ◽  
Author(s):  
Nicole M Procacci ◽  
Kelsey M Allen ◽  
Gael E Robb ◽  
Rebecca Ijekah ◽  
Jennifer L Hoy
Keyword(s):  
Prey Capture ◽  
Predictive Coding ◽  
Neural Mechanisms ◽  
Lower Visual Field ◽  
Visually Guided ◽  
Visual Objects ◽  
State Dependent ◽  
Appetitive Stimuli ◽  
Approach Behaviors ◽  
Stimulus Features

Specific features of visual objects innately draw orienting and approach responses in animals, and provide natural signals of potential reward. In addition, the rapid refinement of innate approach responses enhances the ability of an animal to effectively and conditionally forage, capture prey or initiate a rewarding social experience. However, the neural mechanisms underlying how the brain encodes naturally appetitive stimuli and conditionally transforms stimuli into approach behavior remain unclear. As a first step towards this goal, we have developed a behavioral assay to quantify innate, visually-evoked approach behaviors in freely moving mice presented with simple, computer generated stimuli of varying sizes and speeds in the lower visual field. We found that specific combinations of stimulus features selectively evoked innate approach versus freezing behavioral responses. Surprisingly, we also discovered that prey capture experience selectively modified a range of visually-guided appetitive behaviors, including increasing the probability of approach and pursuit of moving stimuli, as well as altering those visual features that evoked approach. These findings will enable the use of sophisticated genetic strategies to uncover novel neural mechanisms underlying predictive coding, innate behavioral choice, and flexible, state-dependent processing of stimuli in the mouse visual system.

scholarly journals The brain as a dynamically active organ

2020 ◽  
Author(s):  
Björn Brembs
Keyword(s):  
Brain Function ◽  
Sensory Feedback ◽  
Nervous Systems ◽  
Historical Perspectives ◽  
The World ◽  
External Stimuli ◽  
The Brain

Nervous systems are typically described as static networks passively responding to external stimuli (i.e., the ‘sensorimotor hypothesis’). However, for more than a century now, evidence has been accumulating that this passive-static perspective is wrong. Instead, evidence suggests that nervous systems dynamically change their connectivity and actively generate behavior so their owners can achieve goals in the world, some of which involve controlling their sensory feedback. This review provides a brief overview of the different historical perspectives on general brain function and details some select modern examples falsifying the sensorimotor hypothesis.

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