scholarly journals Bridging the Gap Between Neurons and Cognition Through Assemblies of Neurons

2021 ◽  
pp. 1-16
Author(s):  
Christos H. Papadimitriou ◽  
Angela D. Friederici
Keyword(s):  
Neural Activity ◽  
Cognitive Functions ◽  
Computational Models ◽  
Computational Power ◽  
Higher Cognitive Functions ◽  
Simple Sentences ◽  
The Brain

During recent decades, our understanding of the brain has advanced dramatically at both the cellular and molecular levels and at the cognitive neurofunctional level; however, a huge gap remains between the microlevel of physiology and the macrolevel of cognition. We propose that computational models based on assemblies of neurons can serve as a blueprint for bridging these two scales. We discuss recently developed computational models of assemblies that have been demonstrated to mediate higher cognitive functions such as the processing of simple sentences, to be realistically realizable by neural activity, and to possess general computational power.

Dennett, Functionalism, and Introspection

1985 ◽  
Vol 11 ◽  
pp. 55-83
Author(s):  
William Lyons
Keyword(s):  
Cognitive Functions ◽  
Higher Cognitive Functions ◽  
The Brain

Recent functionalist accounts of the mental, at least on the part of philosophers, have often been a result of dissatisfaction with the reductionist accounts championed by such physicalists as Place, Smart and Feigl. In particular this new account gained momentum from the growing belief that our map of the mental, at least in regard to the higher cognitive functions, does not seem to be a map of the brain and its processes. The more we find out about the working brain, the less we are able to cling to the belief that our talk about beliefs, evaluations, intentions, desires and motives gives us information about the structure or functioning of our brains.

The Cerebellum

2020 ◽  
pp. 152-165
Author(s):  
Frederick L. Coolidge
Keyword(s):  
Cognitive Control ◽  
Cognitive Functions ◽  
Empirical Studies ◽  
Abstract Thinking ◽  
Dendritic Branching ◽  
Human Cerebellum ◽  
Phylogenetic Origin ◽  
Motor Movements ◽  
Higher Cognitive Functions ◽  
The Brain

The cerebellum is estimated to contain about 80% of all of the brain’s neurons. Its original adaptation appears to be the seamless and smooth execution of motor movements. The dendritic branching of the cerebellum’s neurons is the most complex and extensive in the brain. The lateral and posterior portions of modern humans’ cerebellum have a more recent phylogenetic origin than that of other hominins. An exaptation of the human cerebellum is the cognitive control and refinement of higher cognitive functions, including lower- and higher-level abstract thinking. Numerous empirical studies link insight and creativity to the cerebellum.

The brain as an organ of the living being

2017 ◽  
pp. 107-172
Author(s):  
Thomas Fuchs
Keyword(s):  
Implicit Memory ◽  
Cognitive Functions ◽  
Ecological Model ◽  
Conscious Experience ◽  
Feedback Loops ◽  
Gestalt Perception ◽  
Neuronal Patterns ◽  
Higher Cognitive Functions ◽  
The Brain ◽  
Living Being

‘The brain as an organ of the living being’ first presents the brain as the organ of vital regulation, which is connected to the organism through various feedback loops. This constant resonance between brainstem and organism is the basis of a background ‘feeling of being alive’ as the foundation of all conscious experience. Second, the relations of brain, organism, and environment are portrayed by means of the functional cycle of perception and movement. As a consequence of this ecological model, consciousness is regarded as the integral of the functional loops between the organism and the environment. In a third step, the development of capacities is traced back to neuroplasticity and implicit memory. This serves as a basis for the investigation into the higher cognitive functions of the brain, in particular, gestalt perception. The focus lies on the principle of resonance between activated neuronal patterns and configurations of stimuli in the environment.

scholarly journals Estrogen Effects on Cognitive and Synaptic Health Over the Lifecourse

2015 ◽  
Vol 95 (3) ◽  
pp. 785-807 ◽  
Author(s):  
Yuko Hara ◽  
Elizabeth M. Waters ◽  
Bruce S. McEwen ◽  
John H. Morrison
Keyword(s):  
Prefrontal Cortex ◽  
Cognitive Functions ◽  
Hormone Replacement ◽  
Brain Regions ◽  
The Body ◽  
Effective Interventions ◽  
Higher Cognitive Functions ◽  
Effects Of Aging ◽  
And Function ◽  
The Brain

Estrogen facilitates higher cognitive functions by exerting effects on brain regions such as the prefrontal cortex and hippocampus. Estrogen induces spinogenesis and synaptogenesis in these two brain regions and also initiates a complex set of signal transduction pathways via estrogen receptors (ERs). Along with the classical genomic effects mediated by activation of ER α and ER β, there are membrane-bound ER α, ER β, and G protein-coupled estrogen receptor 1 (GPER1) that can mediate rapid nongenomic effects. All key ERs present throughout the body are also present in synapses of the hippocampus and prefrontal cortex. This review summarizes estrogen actions in the brain from the standpoint of their effects on synapse structure and function, noting also the synergistic role of progesterone. We first begin with a review of ER subtypes in the brain and how their abundance and distributions are altered with aging and estrogen loss (e.g., ovariectomy or menopause) in the rodent, monkey, and human brain. As there is much evidence that estrogen loss induced by menopause can exacerbate the effects of aging on cognitive functions, we then review the clinical trials of hormone replacement therapies and their effectiveness on cognitive symptoms experienced by women. Finally, we summarize studies carried out in nonhuman primate models of age- and menopause-related cognitive decline that are highly relevant for developing effective interventions for menopausal women. Together, we highlight a new understanding of how estrogen affects higher cognitive functions and synaptic health that go well beyond its effects on reproduction.

Brain plasticity, cognitive functions and neural stem cells: a pivotal role for the brain-specific neural master gene |-SRGAP2–FAM72-|

2017 ◽  
Vol 399 (1) ◽  
pp. 55-61 ◽  
Author(s):  
Nguyen Thi Thanh Ho ◽  
Arne Kutzner ◽  
Klaus Heese
Keyword(s):  
Cognitive Functions ◽  
Rho Gtpase ◽  
Brain Plasticity ◽  
Sequence Similarity ◽  
The Novel ◽  
Therapeutic Approaches ◽  
Cellular Events ◽  
Specific Regulation ◽  
Higher Cognitive Functions ◽  
The Brain

AbstractDue to an aging society with an increased dementia-induced threat to higher cognitive functions, it has become imperative to understand the molecular and cellular events controlling the memory and learning processes in the brain. Here, we suggest that the novel master gene pair |-SRGAP2–FAM72-| (SLIT-ROBO Rho GTPase activating the protein 2, family with sequence similarity to 72) reveals a new dogma for the regulation of neural stem cell (NSC) gene expression and is a distinctive player in the control of human brain plasticity. Insight into the specific regulation of the brain-specific neural master gene |-SRGAP2–FAM72-| may essentially contribute to novel therapeutic approaches to restore or improve higher cognitive functions.

Sex Differences in Cognition

2019 ◽  
pp. 41-66
Author(s):  
Elizabeth Hampson
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Sex Differences ◽  
Spatial Cognition ◽  
Sex Steroids ◽  
Cognitive Functions ◽  
Laboratory Animals ◽  
Human Central Nervous System ◽  
The Brain

Organizational and activational effects of sex steroids were first discovered in laboratory animals, but these concepts extend to hormonal actions in the human central nervous system. This chapter begins with a brief overview of how sex steroids act in the brain and how the organizational-activational hypothesis originated in the field of endocrinology. It then reviews common methods used to study these effects in humans. Interestingly, certain cognitive functions appear to be subject to modification by sex steroids, and these endocrine influences may help explain the sex differences often seen in these functions. The chapter considers spatial cognition as a representative example because the spatial family of functions has received the most study by researchers interested in the biological roots of sex differences in cognition. The chapter reviews evidence that supports an influence of both androgens and estrogens on spatial functions, and concludes with a glimpse of where the field is headed.

scholarly journals Power Failure of Mitochondria and Oxidative Stress in Neurodegeneration and Its Computational Models

Antioxidants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 229
Author(s):  
JunHyuk Woo ◽  
Hyesun Cho ◽  
YunHee Seol ◽  
Soon Ho Kim ◽  
Chanhyeok Park ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Mitochondrial Dysfunction ◽  
Computational Models ◽  
Neuronal Damage ◽  
Living Organism ◽  
The Body ◽  
Mitochondrial Functions ◽  
A Cell ◽  
The Brain ◽  
And Oxidative Stress

The brain needs more energy than other organs in the body. Mitochondria are the generator of vital power in the living organism. Not only do mitochondria sense signals from the outside of a cell, but they also orchestrate the cascade of subcellular events by supplying adenosine-5′-triphosphate (ATP), the biochemical energy. It is known that impaired mitochondrial function and oxidative stress contribute or lead to neuronal damage and degeneration of the brain. This mini-review focuses on addressing how mitochondrial dysfunction and oxidative stress are associated with the pathogenesis of neurodegenerative disorders including Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease. In addition, we discuss state-of-the-art computational models of mitochondrial functions in relation to oxidative stress and neurodegeneration. Together, a better understanding of brain disease-specific mitochondrial dysfunction and oxidative stress can pave the way to developing antioxidant therapeutic strategies to ameliorate neuronal activity and prevent neurodegeneration.

scholarly journals Neural Components of Reading Revealed by Distributed and Symbolic Computational Models

2020 ◽  
Vol 1 (4) ◽  
pp. 381-401
Author(s):  
Ryan Staples ◽  
William W. Graves
Keyword(s):  
Neural Activity ◽  
Neural Coding ◽  
Computational Models ◽  
Ann Model ◽  
Distributed Representations ◽  
Neural Data ◽  
Cognitive Components ◽  
Components Of Reading ◽  
Artificial Neural Network Ann ◽  
Models Of Reading

Determining how the cognitive components of reading—orthographic, phonological, and semantic representations—are instantiated in the brain has been a long-standing goal of psychology and human cognitive neuroscience. The two most prominent computational models of reading instantiate different cognitive processes, implying different neural processes. Artificial neural network (ANN) models of reading posit nonsymbolic, distributed representations. The dual-route cascaded (DRC) model instead suggests two routes of processing, one representing symbolic rules of spelling–to–sound correspondence, the other representing orthographic and phonological lexicons. These models are not adjudicated by behavioral data and have never before been directly compared in terms of neural plausibility. We used representational similarity analysis to compare the predictions of these models to neural data from participants reading aloud. Both the ANN and DRC model representations corresponded to neural activity. However, the ANN model representations correlated to more reading-relevant areas of cortex. When contributions from the DRC model were statistically controlled, partial correlations revealed that the ANN model accounted for significant variance in the neural data. The opposite analysis, examining the variance explained by the DRC model with contributions from the ANN model factored out, revealed no correspondence to neural activity. Our results suggest that ANNs trained using distributed representations provide a better correspondence between cognitive and neural coding. Additionally, this framework provides a principled approach for comparing computational models of cognitive function to gain insight into neural representations.

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