scholarly journals Memory for time

2022 ◽  
Author(s):  
Marc Howard
Keyword(s):  
Temporal Resolution ◽  
Computational Models ◽  
Brain Regions ◽  
Scale Invariant ◽  
The Past ◽  
The Brain

The brain maintains a record of recent events including information about the time at which events were experienced. We review behavioral and neurophysiological evidence as well as computational models to better understand memory for time. Neurophysiologically, populations of neurons that record the time of recent events have been observed in many brain regions. Time cells fire in long sequences after a triggering event demonstrating memory for the past. Populations of exponentially-decaying neurons record past events at many delays by decaying at different rates. Both kinds of representations record distant times with less temporal resolution. The work reviewed here converges on the idea that the brain maintains a representation of past events along a scale-invariant compressed timeline.

Neuro-Clinical Signatures of Language Impairments: A Theoretical Framework for Function-to-structure Mapping in Clinics

2020 ◽  
Vol 20 (9) ◽  
pp. 800-811 ◽  
Author(s):  
Ferath Kherif ◽  
Sandrine Muller
Keyword(s):  
Brain Mapping ◽  
Theoretical Framework ◽  
Brain Regions ◽  
Language Impairments ◽  
Structure Mapping ◽  
Language Functions ◽  
The Past ◽  
Language Recovery ◽  
The Brain ◽  
Bow Tie

In the past decades, neuroscientists and clinicians have collected a considerable amount of data and drastically increased our knowledge about the mapping of language in the brain. The emerging picture from the accumulated knowledge is that there are complex and combinatorial relationships between language functions and anatomical brain regions. Understanding the underlying principles of this complex mapping is of paramount importance for the identification of the brain signature of language and Neuro-Clinical signatures that explain language impairments and predict language recovery after stroke. We review recent attempts to addresses this question of language-brain mapping. We introduce the different concepts of mapping (from diffeomorphic one-to-one mapping to many-to-many mapping). We build those different forms of mapping to derive a theoretical framework where the current principles of brain architectures including redundancy, degeneracy, pluri-potentiality and bow-tie network are described.

scholarly journals Transient brain networks underlying interpersonal strategies during synchronized action

2020 ◽  
Author(s):  
Ole Adrian Heggli ◽  
Ivana Konvalinka ◽  
Joana Cabral ◽  
Elvira Brattico ◽  
Morten L Kringelbach ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Human Interaction ◽  
Action Perception ◽  
Network Information ◽  
Mutual Adaptation ◽  
Underlying Mechanisms ◽  
The Brain

Abstract Interpersonal coordination is a core part of human interaction, and its underlying mechanisms have been extensively studied using social paradigms such as joint finger-tapping. Here, individual and dyadic differences have been found to yield a range of dyadic synchronization strategies, such as mutual adaptation, leading–leading, and leading–following behaviour, but the brain mechanisms that underlie these strategies remain poorly understood. To identify individual brain mechanisms underlying emergence of these minimal social interaction strategies, we contrasted EEG-recorded brain activity in two groups of musicians exhibiting the mutual adaptation and leading–leading strategies. We found that the individuals coordinating via mutual adaptation exhibited a more frequent occurrence of phase-locked activity within a transient action–perception-related brain network in the alpha range, as compared to the leading–leading group. Furthermore, we identified parietal and temporal brain regions that changed significantly in the directionality of their within-network information flow. Our results suggest that the stronger weight on extrinsic coupling observed in computational models of mutual adaptation as compared to leading–leading might be facilitated by a higher degree of action–perception network coupling in the brain.

scholarly journals Watershed Brain Regions for Characterizing Brand Equity-Related Mental Processes

2021 ◽  
Vol 11 (12) ◽  
pp. 1619
Author(s):  
Shinya Watanuki
Keyword(s):  
Brand Equity ◽  
Meta Analysis ◽  
Brain Regions ◽  
Parahippocampal Gyrus ◽  
Neural Systems ◽  
Mental Processes ◽  
Chi Square ◽  
The Past ◽  
Lingual Gyrus ◽  
The Brain

Brand equity is an important intangible for enterprises. As one advantage, products with brand equity can increase revenue, compared with those without such equity. However, unlike tangibles, it is difficult for enterprises to manage brand equity because it exists within consumers’ minds. Although, over the past two decades, numerous consumer neuroscience studies have revealed the brain regions related to brand equity, the identification of unique brain regions related to such equity is still controversial. Therefore, this study identifies the unique brain regions related to brand equity and assesses the mental processes derived from these regions. For this purpose, three analysis methods (i.e., the quantitative meta-analysis, chi-square tests, and machine learning) were conducted. The data were collected in accordance with the general procedures of a qualitative meta-analysis. In total, 65 studies (1412 foci) investigating branded objects with brand equity and unbranded objects without brand equity were examined, whereas the neural systems involved for these two brain regions were contrasted. According to the results, the parahippocampal gyrus and the lingual gyrus were unique brand equity-related brain regions, whereas automatic mental processes based on emotional associative memories derived from these regions were characteristic mental processes that discriminate branded from unbranded objects.

scholarly journals Studies of Altered Social Cognition in Neuropsychiatric Disorders Using Functional Neuroimaging

2002 ◽  
Vol 47 (4) ◽  
pp. 327-336 ◽  
Author(s):  
Cheryl L Grady ◽  
Michelle L Keightley
Keyword(s):  
Social Cognition ◽  
Brain Function ◽  
Functional Neuroimaging ◽  
Neuropsychiatric Disorders ◽  
Brain Regions ◽  
The Past ◽  
Complex Interactions ◽  
Social Abilities ◽  
The Brain ◽  
Self Reference

In this paper, we review studies using functional neuroimaging to examine cognition in neuropsychiatric disorders. The focus is on social cognition, which is a topic that has received increasing attention over the past few years. A network of brain regions is proposed for social cognition that includes regions involved in processes relevant to social functioning (for example, self reference and emotion). We discuss the alterations of activity in these areas in patients with autism, depression, schizophrenia, and posttraumatic stress disorder in relation to deficits in social behaviour and symptoms. The evidence to date suggests that there may be some specificity of the brain regions involved in these 4 disorders, but all are associated with dysfunction in the amygdala and dorsal cingulate gyrus. Although there is much work remaining in this area, we are beginning to understand the complex interactions of brain function and behaviour that lead to disruptions of social abilities.

scholarly journals Computational modelling of social cognition and behaviour—a reinforcement learning primer

2020 ◽  
Author(s):  
Patricia L Lockwood ◽  
Miriam C Klein-Flügge
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Observational Learning ◽  
Impression Formation ◽  
Computational Models ◽  
Computational Modelling ◽  
Neural Systems ◽  
Neural Basis ◽  
The Past ◽  
The Brain

Abstract Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalizing and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.

New Methods of Brain Stimulation Are Improving Research and Therapy in Obsessive-Compulsive Disorder

CNS Spectrums ◽  
2000 ◽  
Vol 5 (S4) ◽  
pp. 12-17 ◽  
Author(s):  
Mark S. George
Keyword(s):  
Obsessive Compulsive Disorder ◽  
Brain Stimulation ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Current Status ◽  
Obsessive Compulsive ◽  
New Techniques ◽  
Compulsive Disorder ◽  
The Past ◽  
The Brain

AbstractOver the past decade, new functional neuroimaging tools have enabled researchers to identify the specific brain regions involved in obsessive-compulsive disorder (OCD). More recently, researchers have perfected several new techniques for stimulating the brain. With some exceptions, these new brain stimulation techniques are regionally specific and less invasive than older methods. As a class, these “somatic interventions” build on prior neuroanatomic information about OCD. This article reviews the past and current status of these brain stimulation methodologies, which promise to revolutionize neuropsychiatric research and therapy over the next 10 to 20 years. As the brain circuits in OCD and the pharmacology within those circuits become better understood, these brain stimulation techniques hold particular promise in helping to understand and perhaps treat OCD.

scholarly journals Computational models of category-selective brain regions enable high-throughput tests of selectivity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
N. Apurva Ratan Murty ◽  
Pouya Bashivan ◽  
Alex Abate ◽  
James J. DiCarlo ◽  
Nancy Kanwisher
Keyword(s):  
Computational Models ◽  
Functional Organization ◽  
Brain Regions ◽  
Human Cognition ◽  
Future Research ◽  
Domain Specific ◽  
Face Area ◽  
Extrastriate Body Area ◽  
Cortical Regions ◽  
The Brain

AbstractCortical regions apparently selective to faces, places, and bodies have provided important evidence for domain-specific theories of human cognition, development, and evolution. But claims of category selectivity are not quantitatively precise and remain vulnerable to empirical refutation. Here we develop artificial neural network-based encoding models that accurately predict the response to novel images in the fusiform face area, parahippocampal place area, and extrastriate body area, outperforming descriptive models and experts. We use these models to subject claims of category selectivity to strong tests, by screening for and synthesizing images predicted to produce high responses. We find that these high-response-predicted images are all unambiguous members of the hypothesized preferred category for each region. These results provide accurate, image-computable encoding models of each category-selective region, strengthen evidence for domain specificity in the brain, and point the way for future research characterizing the functional organization of the brain with unprecedented computational precision.

scholarly journals Computational modelling of social cognition and behaviour – a reinforcement learning primer

2019 ◽  
Author(s):  
Patricia Lockwood ◽  
Miriam Klein-Flugge
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Observational Learning ◽  
Impression Formation ◽  
Computational Models ◽  
Computational Modelling ◽  
Neural Systems ◽  
Neural Basis ◽  
The Past ◽  
The Brain

Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalising and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.

scholarly journals Quasi-universal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics

2021 ◽  
Author(s):  
Guillermo B. Morales ◽  
Serena Di Santo ◽  
Miguel A Muñoz
Keyword(s):  
Mouse Brain ◽  
Neuronal Activity ◽  
Scale Invariance ◽  
Brain Networks ◽  
Brain Regions ◽  
Statistical Characteristics ◽  
Neural Population ◽  
Dynamical Regime ◽  
Scale Invariant ◽  
The Brain

The brain is in a state of perpetual reverberant neural activity, even in the absence of specific tasks or stimuli. Shedding light on the origin and functional significance of such activity is essential to understanding how the brain transmits, processes, and stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics of empirically observed neuronal activity can be understood by assuming that brain networks operate in a dynamical regime near the edge of a phase transition. Moreover, the resulting critical behavior, with its concomitant scale invariance, is assumed to carry crucial functional advantages. Here, we present a data-driven analysis based on simultaneous high-throughput recordings of the activity of thousands of individual neurons in various regions of the mouse brain. To analyze these data, we construct a unified theoretical framework that synergistically combines cutting-edge methods for the study of brain activity (such as a phenomenological renormalization group approach and techniques that infer the general dynamical state of a neural population), while designing complementary tools. This unified approach allows us to uncover strong signatures of scale invariance that is "quasi-universal" across brain regions and reveal that these areas operate, to a greater or lesser extent, at the edge of instability. Furthermore, this framework allows us to distinguish between quasi-universal background activity and non-universal input-related activity. Taken together, the following study provides strong evidence that brain networks actually operate in a critical regime which, among other functional advantages, provides them with a scale-invariant substrate of activity in which optimal input representations can be sustained.

scholarly journals Frequency-Resolved Functional Connectivity: Role of Delay and the Strength of Connections

2021 ◽  
Vol 15 ◽  
Author(s):  
Abolfazl Ziaeemehr ◽  
Alireza Valizadeh
Keyword(s):  
Functional Connectivity ◽  
Temporal Resolution ◽  
Computational Study ◽  
Brain Regions ◽  
Kuramoto Model ◽  
Functional Networks ◽  
High Temporal Resolution ◽  
Frequency Bands ◽  
The Brain

The brain functional network extracted from the BOLD signals reveals the correlated activity of the different brain regions, which is hypothesized to underlie the integration of the information across functionally specialized areas. Functional networks are not static and change over time and in different brain states, enabling the nervous system to engage and disengage different local areas in specific tasks on demand. Due to the low temporal resolution, however, BOLD signals do not allow the exploration of spectral properties of the brain dynamics over different frequency bands which are known to be important in cognitive processes. Recent studies using imaging tools with a high temporal resolution has made it possible to explore the correlation between the regions at multiple frequency bands. These studies introduce the frequency as a new dimension over which the functional networks change, enabling brain networks to transmit multiplex of information at any time. In this computational study, we explore the functional connectivity at different frequency ranges and highlight the role of the distance between the nodes in their correlation. We run the generalized Kuramoto model with delayed interactions on top of the brain's connectome and show that how the transmission delay and the strength of the connections, affect the correlation between the pair of nodes over different frequency bands.

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