How Cortical Interconnectedness Varies with Network Size

1989 ◽  
Vol 1 (4) ◽  
pp. 473-479 ◽  
Author(s):  
Charles F. Stevens
Keyword(s):  
Neural Networks ◽  
Synaptic Input ◽  
Brain Function ◽  
Network Size ◽  
Network Property ◽  
Neocortical Neurons ◽  
Unit Unit ◽  
Important Basis ◽  
The Brain ◽  
Insight Into

When model neural networks are used to gain insight into how the brain might carry out its computations, comparisons between features of the network and those of the brain form an important basis for drawing conclusions about the network's relevance to brain function. The most significant features to be compared, of course, relate to behavior of the units. Another network property that would be useful to consider, however, is the extent to which units are interconnected and the law by which unit-unit connections scale as the network is made larger. The goal of this paper is to consider these questions for neocortex. The conclusion will be that neocortical neurons are rather sparsely interconnected — each neuron receives direct synaptic input from fewer than 3% of its neighbors underlying the surrounding square millimeter of cortex — and the extent of connectedness hardly changes for brains that range in size over about four orders of magnitude. These conclusions support the currently popular notion that the brain's circuits are highly modular and suggest that increased cortex size is mainly achieved by adding more modules.

scholarly journals Reflections on the Constraints and Opportunities in Therapy in Rett Syndrome

2006 ◽  
Vol 6 ◽  
pp. 992-997 ◽  
Author(s):  
Alison M. Kerr
Keyword(s):  
Quality Of Life ◽  
Neural Networks ◽  
Rett Syndrome ◽  
Clear Understanding ◽  
Research Experience ◽  
Hormonal Responses ◽  
Physical Limitations ◽  
The Brain ◽  
Insight Into

More than 20 years of clinical and research experience with affected people in the British Isles has provided insight into particular challenges for therapists, educators, or parents wishing to facilitate learning and to support the development of skills in people with Rett syndrome. This paper considers the challenges in two groups: those due to constraints imposed by the disabilities associated with the disorder and those stemming from the opportunities, often masked by the disorder, allowing the development of skills that depend on less-affected areas of the brain. Because the disorder interferes with the synaptic links between neurones, the functions of the brain that are most dependent on complex neural networks are the most profoundly affected. These functions include speech, memory, learning, generation of ideas, and the planning of fine movements, especially those of the hands. In contrast, spontaneous emotional and hormonal responses appear relatively intact. Whereas failure to appreciate the physical limitations of the disease leads to frustration for therapist and client alike, a clear understanding of the better-preserved areas of competence offers avenues for real progress in learning, the building of satisfying relationships, and achievement of a quality of life.

On the Synthesis of Brain-State-in-a-Box Neural Models with Application to Associative Memory

2000 ◽  
Vol 12 (2) ◽  
pp. 451-472 ◽  
Author(s):  
Fation Sevrani ◽  
Kennichi Abe
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Equilibrium Points ◽  
Domain Of Attraction ◽  
Neural Model ◽  
Brain State ◽  
Qualitative Properties ◽  
The Stability ◽  
The Brain ◽  
Insight Into

In this article we present techniques for designing associative memories to be implemented by a class of synchronous discrete-time neural networks based on a generalization of the brain-state-in-a-box neural model. First, we address the local qualitative properties and global qualitative aspects of the class of neural networks considered. Our approach to the stability analysis of the equilibrium points of the network gives insight into the extent of the domain of attraction for the patterns to be stored as asymptotically stable equilibrium points and is useful in the analysis of the retrieval performance of the network and also for design purposes. By making use of the analysis results as constraints, the design for associative memory is performed by solving a constraint optimization problem whereby each of the stored patterns is guaranteed a substantial domain of attraction. The performance of the designed network is illustrated by means of three specific examples.

scholarly journals Rotational remapping between differently prioritized representations in visual working memory

2021 ◽  
Author(s):  
Quan Wan ◽  
Jorge A. Menendez ◽  
Bradley R. Postle
Keyword(s):  
Neural Networks ◽  
Working Memory ◽  
Visual Working Memory ◽  
Principle Component Analysis ◽  
Recurrent Neural Networks ◽  
Memory Task ◽  
Neural Computation ◽  
Hidden Layer ◽  
The Brain ◽  
Insight Into

How does the brain prioritize among the contents of working memory to appropriately guide behavior? Using inverted encoding modeling (IEM), previous work (Wan et al., 2020) showed that unprioritized memory items (UMI) are actively represented in the brain but in a “flipped”, or opposite, format compared to prioritized memory items (PMI). To gain insight into the mechanisms underlying the UMI-to-PMI representational transformation, we trained recurrent neural networks (RNNs) with an LSTM architecture to perform a 2-back working memory task. Visualization of the LSTM hidden layer activity using Principle Component Analysis (PCA) revealed that the UMI representation is rotationally remapped to that of PMI, and this was quantified and confirmed via demixed PCA. The application of the same analyses to the EEG dataset of Wan et al. (2020) revealed similar rotational remapping between the UMI and PMI representations. These results identify rotational remapping as a candidate neural computation employed in the dynamic prioritization within contents of working memory.

Implications of neural networks for how we think about brain function

1992 ◽  
Vol 15 (4) ◽  
pp. 644-655
Author(s):  
David A. Robinson
Keyword(s):  
Neural Networks ◽  
Control Systems ◽  
Brain Function ◽  
Single Unit ◽  
Task Complexity ◽  
The Other ◽  
Network Function ◽  
A Cell ◽  
Single Unit Recordings ◽  
The Brain

Abstract Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the other hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization.

scholarly journals Predictive coding is a consequence of energy efficiency in recurrent neural networks

2021 ◽  
Author(s):  
Abdullahi Ali ◽  
Nasir Ahmad ◽  
Elgar de Groot ◽  
Marcel A. J. van Gerven ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Energy Efficiency ◽  
Recurrent Neural Networks ◽  
Brain Function ◽  
Message Passing ◽  
Sensory Input ◽  
Predictive Coding ◽  
Computational Modelling ◽  
Top Down ◽  
The Brain

AbstractPredictive coding represents a promising framework for understanding brain function. It postulates that the brain continuously inhibits predictable sensory input, ensuring a preferential processing of surprising elements. A central aspect of this view is its hierarchical connectivity, involving recurrent message passing between excitatory bottom-up signals and inhibitory top-down feedback. Here we use computational modelling to demonstrate that such architectural hard-wiring is not necessary. Rather, predictive coding is shown to emerge as a consequence of energy efficiency. When training recurrent neural networks to minimise their energy consumption while operating in predictive environments, the networks self-organise into prediction and error units with appropriate inhibitory and excitatory interconnections, and learn to inhibit predictable sensory input. Moving beyond the view of purely top-down driven predictions, we demonstrate via virtual lesioning experiments that networks perform predictions on two timescales: fast lateral predictions among sensory units, and slower prediction cycles that integrate evidence over time.

scholarly journals Toward the Decipherment of Molecular Interactions in the Diabetic Brain

Biomedicines ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 115
Author(s):  
Maria Chomova
Keyword(s):  
Brain Function ◽  
Potential Contribution ◽  
Metabolic Disturbances ◽  
Cognitive Changes ◽  
Molecular Events ◽  
Diabetic Brain ◽  
Insulin Uptake ◽  
The Brain ◽  
Insight Into

Diabetes mellitus (DM) has been associated with cognitive complications in the brain resulting from acute and chronic metabolic disturbances happening peripherally and centrally. Numerous studies have reported on the morphological, electrophysiological, biochemical, and cognitive changes in the brains of diabetic individuals. The detailed pathophysiological mechanisms implicated in the development of the diabetic cognitive phenotype remain unclear due to intricate molecular changes evolving over time and space. This review provides an insight into recent advances in understanding molecular events in the diabetic brain, focusing on cerebral glucose and insulin uptake, insulin action in the brain, and the role of the brain in the regulation of glucose homeostasis. Fully competent mitochondria are essential for energy metabolism and proper brain function; hence, the potential contribution of mitochondria to the DM-induced impairment of the brain is also discussed.

scholarly journals Neuromodulatory connectivity defines the structure of a behavioral neural network

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Feici Diao ◽  
Amicia D Elliott ◽  
Fengqiu Diao ◽  
Sarav Shah ◽  
Benjamin H White
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Synaptic Connectivity ◽  
Behavioral Sequence ◽  
Key Factors ◽  
Functional Architecture ◽  
Network Function ◽  
Input Layer ◽  
The Brain ◽  
Insight Into

Neural networks are typically defined by their synaptic connectivity, yet synaptic wiring diagrams often provide limited insight into network function. This is due partly to the importance of non-synaptic communication by neuromodulators, which can dynamically reconfigure circuit activity to alter its output. Here, we systematically map the patterns of neuromodulatory connectivity in a network that governs a developmentally critical behavioral sequence in Drosophila. This sequence, which mediates pupal ecdysis, is governed by the serial release of several key factors, which act both somatically as hormones and within the brain as neuromodulators. By identifying and characterizing the functions of the neuronal targets of these factors, we find that they define hierarchically organized layers of the network controlling the pupal ecdysis sequence: a modular input layer, an intermediate central pattern generating layer, and a motor output layer. Mapping neuromodulatory connections in this system thus defines the functional architecture of the network.

scholarly journals Short-term sleep deprivation in mice induces B cell migration to the brain compartment

SLEEP ◽  
2019 ◽  
Author(s):  
Ben Korin ◽  
Shimrit Avraham ◽  
Hilla Azulay-Debby ◽  
Dorit Farfara ◽  
Fahed Hakim ◽  
...  
Keyword(s):  
B Cells ◽  
Sleep Deprivation ◽  
Immune Cells ◽  
Brain Function ◽  
Brain Activity ◽  
Short Term ◽  
Elevated Expression ◽  
Acute Sleep Deprivation ◽  
The Brain ◽  
Insight Into

Abstract Increasing evidence highlight the involvement of immune cells in brain activity and its dysfunction. The brain’s immune compartment is a dynamic ensemble of cells that can fluctuate even in naive animals. However, the dynamics and factors that can affect the composition of immune cells in the naive brain are largely unknown. Here, we examined whether acute sleep deprivation can affect the brain’s immune compartment (parenchyma, meninges, and choroid plexus). Using high-dimensional mass cytometry analysis, we broadly characterized the effects of short-term sleep deprivation on the immune composition in the mouse brain. We found that after 6 h of sleep deprivation, there was a significant increase in the abundance of B cells in the brain compartment. This effect can be accounted for, at least in part, by the elevated expression of the migration-related receptor, CXCR5, on B cells and its ligand, cxcl13, in the meninges following sleep deprivation. Thus, our study reveals that short-term sleep deprivation affects the brain’s immune compartment, offering a new insight into how sleep disorders can affect brain function and potentially contribute to neurodegeneration and neuroinflammation.

Seeing the Brain in Action: How Multiphoton Imaging Has Advanced Our Understanding of Neuronal Function

2008 ◽  
Vol 14 (6) ◽  
pp. 482-491 ◽  
Author(s):  
Grace Stutzmann
Keyword(s):  
Brain Function ◽  
Structural Information ◽  
Multiphoton Microscopy ◽  
Biological Research ◽  
Neuronal Function ◽  
Multiphoton Imaging ◽  
New Era ◽  
The Brain ◽  
Insight Into ◽  
Gaining Insight

AbstractGaining insight into how the nervous system functions is a challenge for scientists, particularly because the static morphology of the brain and the cells within tell little about how they actually work. Fixed specimens can provide critical structural information, but the jump to functional neurobiology in living cells is obviated with these preparations. In order to grasp the complexity of neuronal activity, it is necessary to observe the brain in action, from the level of subcellular signaling to the whole organism. Recent advances in nonlinear microscopy have given rise to a new era for biological research. In particular, the introduction of multiphoton excitation has drastically improved the depth and speed to which we can probe brain function. In order to better appreciate recent contributions of multiphoton microscopy to our current and future understanding of biological systems, an historical awareness of past microscopy applications is useful.

scholarly journals Applying gene regulatory network logic to the evolution of social behavior

2017 ◽  
Vol 114 (23) ◽  
pp. 5886-5893 ◽  
Author(s):  
Nicole M. Baran ◽  
Patrick T. McGrath ◽  
J. Todd Streelman
Keyword(s):  
Neural Networks ◽  
Social Behavior ◽  
Brain Function ◽  
Regulatory Networks ◽  
Developmental Networks ◽  
Study Behavior ◽  
Gene Regulatory ◽  
Brain And Behavior ◽  
And Behavior ◽  
The Brain

Animal behavior is ultimately the product of gene regulatory networks (GRNs) for brain development and neural networks for brain function. The GRN approach has advanced the fields of genomics and development, and we identify organizational similarities between networks of genes that build the brain and networks of neurons that encode brain function. In this perspective, we engage the analogy between developmental networks and neural networks, exploring the advantages of using GRN logic to study behavior. Applying the GRN approach to the brain and behavior provides a quantitative and manipulative framework for discovery. We illustrate features of this framework using the example of social behavior and the neural circuitry of aggression.

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