scholarly journals Re-membering the body: applications of computational neuroscience to the top-down control of regeneration of limbs and other complex organs

2015 ◽  
Vol 7 (12) ◽  
pp. 1487-1517 ◽  
Author(s):  
G. Pezzulo ◽  
M. Levin
Keyword(s):  
Information Processing ◽  
Pattern Formation ◽  
Computational Neuroscience ◽  
Memory System ◽  
The Body ◽  
Signaling Networks ◽  
Control Mechanisms ◽  
Top Down ◽  
The Brain

How do regenerating bodies know when to stop remodeling? Bioelectric signaling networks guide pattern formation and may implement a somatic memory system. Deep parallels may exist between information processing in the brain and morphogenetic control mechanisms.

Large-scale Networks for Attentional Biases

2014 ◽  
Author(s):  
Anna C. (Kia) Nobre ◽  
M-Marsel Mesulam
Keyword(s):  
Information Processing ◽  
Selective Attention ◽  
Spatial Attention ◽  
Large Scale ◽  
Receptive Fields ◽  
Long Term Memory ◽  
Control Mechanisms ◽  
Top Down ◽  
Visual Spatial Attention ◽  
Visual Spatial

Selective attention is essential for all aspects of cognition. Using the paradigmatic case of visual spatial attention, we present a theoretical account proposing the flexible control of attention through coordinated activity across a large-scale network of brain areas. It reviews evidence supporting top-down control of visual spatial attention by a distributed network, and describes principles emerging from a network approach. Stepping beyond the paradigm of visual spatial attention, we consider attentional control mechanisms more broadly. The chapter suggests that top-down biasing mechanisms originate from multiple sources and can be of several types, carrying information about receptive-field properties such as spatial locations or features of items; but also carrying information about properties that are not easily mapped onto receptive fields, such as the meanings or timings of items. The chapter considers how selective biases can operate on multiple slates of information processing, not restricted to the immediate sensory-motor stream, but also operating within internalized, short-term and long-term memory representations. Selective attention appears to be a general property of information processing systems rather than an independent domain within our cognitive make-up.

Cartesian Robotics

2013 ◽  
Vol 124 (1) ◽  
pp. 43-68 ◽  
Author(s):  
David Bates
Keyword(s):  
Information Processing ◽  
Cognitive Processes ◽  
Processing System ◽  
The Body ◽  
Human Organism ◽  
Information Processing System ◽  
Mind And Body ◽  
New Interpretation ◽  
The Brain ◽  
Physiological Theory

This essay looks closely at René Descartes’s physiological theory, and especially his theorization of the nerves and the brain as an information-processing system, in order to offer a new interpretation of cognition within his philosophy. Rather than opposing mind and body, Descartes showed how the operations of the soul interrupted the automatic cognitive processes of the body to provide adaptive flexibility for the human organism as a whole.

Visual Substitution Systems: Control and Information Processing Considerations

1975 ◽  
Vol 69 (7) ◽  
pp. 300-304
Author(s):  
Raymond M. Fish
Keyword(s):  
Information Processing ◽  
Visual System ◽  
Visual Information ◽  
Control Mechanisms ◽  
Direct Stimulation ◽  
Systems Control ◽  
Display Systems ◽  
The Brain ◽  
Processing Of Visual Information ◽  
Stimulation Of

A detailed discussion of the visual mechanisms found in the higher vertebrates is used as the basis for exploring the problems found in creating visual substitution systems. Specific attention is given to the control mechanisms used in the visual system and to the processing of visual information in the retina and brain. The three types of substitution systems discussed are tactual display systems, audio display systems, and those involving direct stimulation of the brain using electrodes.

scholarly journals Time scale separation of information processing between sensory and associative regions

2020 ◽  
Author(s):  
P Sorrentino ◽  
G Rabuffo ◽  
R Rucco ◽  
F Baselice ◽  
E Troisi Lopez ◽  
...  
Keyword(s):  
Information Processing ◽  
Resting State ◽  
Large Scale ◽  
Functional Organization ◽  
Brain Regions ◽  
High Temporal Resolution ◽  
Top Down ◽  
Bottom Up ◽  
Speed Of Information Processing ◽  
The Brain

AbstractStimulus perception is assumed to involve the (fast) detection of sensory inputs and their (slower) integration. The capacity of the brain to quickly adapt, at all times, to unexpected stimuli suggests that the interplay between the slow and fast processes happens at short timescales. We hypothesised that, even during resting-state, the flow of information across the brain regions should evolve quickly, but not homogeneously in time. Here we used high temporal-resolution Magnetoencephalography (MEG) signals to estimate the persistence of the information in functional links across the brain. We show that short- and long-lasting retention of the information, entailing different speeds in the update rate, naturally split the brain into two anatomically distinct subnetworks. The “fast updating network” (FUN) is localized in the regions that typically belong to the dorsal and ventral streams during perceptive tasks, while the “slow updating network” (SUN) hinges classically associative areas. Finally, we show that only a subset of the brain regions, which we name the multi-storage core (MSC), belongs to both subnetworks. The MSC is hypothesized to play a role in the communication between the (otherwise) segregated subnetworks.Significance statementThe human brain constantly scans the environment in search of relevant incoming stimuli, and appropriately reconfigures its large-scale activation according to environmental requests. The functional organization substanding these bottom-up and top-down processes, however, is not understood. Studying the speed of information processing between brain regions during resting state, we show the existence of two spatially segregated subnetworks processing information at fast- and slow-rates. Notably, these networks involve the regions that typically belong to the perception stream and the associative regions, respectively. Therefore, we provide evidence that, regardless of the presence of a stimulus, the bottom-up and top-down perceptive pathways are inherent to the resting state dynamics.

scholarly journals Top-down control of hippocampal signal-to-noise by prefrontal long-range inhibition

2021 ◽  
Author(s):  
Ruchi Malik ◽  
Yi Li ◽  
Selin Schamiloglu ◽  
Vikaas S. Sohal
Keyword(s):  
Information Processing ◽  
Long Range ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Top Down ◽  
Object Exploration ◽  
Hippocampal Activity ◽  
The Brain ◽  
Stimulation Of

SummaryThe prefrontal cortex (PFC) is postulated to exert ‘top-down control’ by modulating information processing throughout the brain to promote specific actions based on current goals. However, the pathways mediating top-down control remain poorly understood. In particular, knowledge about direct prefrontal connections that might facilitate top-down prefrontal control of information processing in the hippocampus remains sparse. Here we describe novel monosynaptic long-range GABAergic projections from PFC to hippocampus. These preferentially inhibit vasoactive intestinal polypeptide expressing interneurons, which are known to disinhibit hippocampal microcircuits. Indeed, stimulating prefrontal–hippocampal GABAergic projections increases hippocampal feedforward inhibition and reduces hippocampal activity in vivo. The net effect of these actions is to specifically enhance the signal-to-noise ratio for hippocampal representations of objects. Correspondingly, stimulation of PFC-to-hippocampus GABAergic projections promotes object exploration. Together, these results elucidate a novel top-down pathway in which long-range GABAergic projections target disinhibitory microcircuits, thereby enhancing signals and network dynamics underlying exploratory behavior.

scholarly journals A global framework for a systemic view of brain modeling

2020 ◽  
Author(s):  
Frédéric Alexandre
Keyword(s):  
Computational Neuroscience ◽  
Instrumental Conditioning ◽  
Cognitive Architecture ◽  
The Body ◽  
Clear Understanding ◽  
Posterior Cortex ◽  
Reciprocal Relationships ◽  
Brain Modeling ◽  
Definition Of ◽  
The Brain

Abstract Background: The brain is a complex system, due to the heterogeneity of its structure, the diversity of the functions in which it participates and to its reciprocal relationships with the body and the environment. Researchers in cognitive computational neuroscience generally focused on one specific task, lack this global view and a clear understanding of how their model is inserted in a global cognitive architecture. A systemic description of the brain is presented here, as a general framework where specific models in computational neuroscience can be integrated and associated with global information flows and cognitive functions. Results: In an enactive view, this framework integrates the fundamental organization of the brain in sensorimotor loops with the internal and the external worlds, answering four fundamental questions (what, why, where and how). Our survival-oriented definition of behavior gives a prominent role to pavlovian and instrumental conditioning, augmented during phylogeny by the specific contribution of other kinds of learning, related to semantic memory in the posterior cortex, episodic memory in the hippocampus and working memory in the frontal cortex. Conclusions: This framework highlights that responses can be prepared in different ways, from pavlovian reflexes and habitual behavior to deliberations for goal-directed planning and reasoning, and explains that these different kinds of responses coexist, collaborate and compete for the control of behavior. It also lays emphasis on the fact that cognition can be described as a dynamical system of interacting memories, some acting to provide information to others, to replace them when they are not efficient enough, or to help for their improvement. Describing the brain as an architecture of learning systems has also strong implications in Machine Learning. Our biologically informed view of pavlovian and instrumental conditioning can be very precious to revisit classical Reinforcement Learning and provide a basis to ensure really autonomous learning.

EXPERIMENTAL ASSESSMENT OF THE NANOPARTICLES TOXICITY OF NICKEL OXIDE IN TWO SIZES IN THE SUBCHRONIC EXPERIMENT

2018 ◽  
pp. 30-35
Author(s):  
M.P. Sutunkova ◽  
B.A. Katsnelson ◽  
L.I. Privalova ◽  
S.N. Solovjeva ◽  
V.B. Gurvich ◽  
...  
Keyword(s):  
Nickel Oxide ◽  
Nervous Activity ◽  
Equal Mass ◽  
The Body ◽  
Subchronic Toxicity ◽  
Physiological Mechanisms ◽  
Nickel Oxide Nanoparticles ◽  
The Relationship ◽  
The Brain ◽  
Nanoparticles Toxicity

We conducted a comparative assessment of the nickel oxide nanoparticles toxicity (NiO) of two sizes (11 and 25 nm) according to a number of indicators of the body state after repeated intraperitoneal injections of these particles suspensions. At equal mass doses, NiO nanoparticles have been found to cause various manifestations of systemic subchronic toxicity with a particularly pronounced effect on liver, kidney function, the body’s antioxidant system, lipid metabolism, white and red blood, redox metabolism, spleen damage, and some disorders of nervous activity allegedly related to the possibility of nickel penetration into the brain from the blood. The relationship between the diameter and toxicity of particles is ambiguous, which may be due to differences in toxicokinetics, which is controlled by both physiological mechanisms and direct penetration of nanoparticles through biological barriers and, finally, unequal solubility.

Perception

2018 ◽  
Author(s):  
Joel Z. Leibo ◽  
Tomaso Poggio
Keyword(s):  
Computational Neuroscience ◽  
Feature Detection ◽  
Pedestrian Detection ◽  
Detection Systems ◽  
Catching Up ◽  
Feature Detectors ◽  
Recognition Systems ◽  
Engineered Systems ◽  
Perceptual Systems ◽  
The Brain

This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex feature detection emerges by combining simple feature detectors in a hierarchical fashion. We also explore how the microcircuits of the neocortex implement such schemes pointing out similarities to progress in the field of machine vision driven deep learning algorithms. We see signs that engineered systems are catching up with the brain. For example, vision-based pedestrian detection systems are now accurate enough to be installed as safety devices in (for now) human-driven vehicles and the speech recognition systems embedded in smartphones have become increasingly impressive. While not being entirely biologically based, we note that computational neuroscience, as described in this chapter, makes up a considerable portion of such systems’ intellectual pedigree.

The musculature and nervous system of the plerocercoid larva of Dibothriorhynchus grossum (Rud.)

Parasitology ◽  
1941 ◽  
Vol 33 (4) ◽  
pp. 373-389 ◽  
Author(s):  
Gwendolen Rees
Keyword(s):  
Nervous System ◽  
Muscle Mass ◽  
Nerve Cells ◽  
The Body ◽  
Lateral Nerve ◽  
Posterior Median ◽  
The Brain ◽  
Ventral Muscle ◽  
Nerve Cords ◽  
Extrinsic Muscles

1. The structure of the proboscides of the larva of Dibothriorhynchus grossum (Rud.) is described. Each proboscis is provided with four sets of extrinsic muscles, and there is an anterior dorso-ventral muscle mass connected to all four proboscides.2. The musculature of the body and scolex is described.3. The nervous system consists of a brain, two lateral nerve cords, two outer and inner anterior nerves on each side, twenty-five pairs of bothridial nerves to each bothridium, four longitudinal bothridial nerves connecting these latter before their entry into the bothridia, four proboscis nerves arising from the brain, and a series of lateral nerves supplying the lateral regions of the body.4. The so-called ganglia contain no nerve cells, these are present only in the posterior median commissure which is therefore the nerve centre.

Sign in / Sign up

Export Citation Format

Share Document