scholarly journals Masters of communication: The brain of the banded cleaner shrimp Stenopus hispidus (Olivier, 1811) with an emphasis on sensory processing areas

2019 ◽  
Vol 528 (9) ◽  
pp. 1561-1587 ◽  
Author(s):  
Jakob Krieger ◽  
Marie K. Hörnig ◽  
Renate E. Sandeman ◽  
David C. Sandeman ◽  
Steffen Harzsch
Keyword(s):  
Sensory Processing ◽  
The Brain ◽  
Cleaner Shrimp

scholarly journals Pain Attenuation through Mindfulness is Associated with Decreased Cognitive Control and Increased Sensory Processing in the Brain

2012 ◽  
Vol 55 (2) ◽  
pp. 25-26
Author(s):  
J. Wiedemann ◽  
Tim Gard ◽  
Britta K. Hölzel ◽  
Alexander T. Sack ◽  
Hannes Hempel ◽  
...  
Keyword(s):  
Cognitive Control ◽  
Sensory Processing ◽  
The Brain

scholarly journals Synaptic propagation in neuronal networks with finite-support space dependent coupling

2020 ◽  
Author(s):  
Ricardo Erazo Toscano ◽  
Remus Osan
Keyword(s):  
Time Constant ◽  
Sensory Processing ◽  
Traveling Waves ◽  
Traveling Wave ◽  
Neuronal Networks ◽  
Evolution Equations ◽  
Network Connectivity ◽  
Space Constant ◽  
The Stability ◽  
The Brain

1AbstractTraveling waves of electrical activity are ubiquitous in biological neuronal networks. Traveling waves in the brain are associated with sensory processing, phase coding, and sleep. The neuron and network parameters that determine traveling waves’ evolution are synaptic space constant, synaptic conductance, membrane time constant, and synaptic decay time constant. We used an abstract neuron model to investigate the propagation characteristics of traveling wave activity. We formulated a set of evolution equations based on the network connectivity parameters. We numerically investigated the stability of the traveling wave propagation with a series of perturbations with biological relevance.

Magnetoencephalography

2017 ◽  
Author(s):  
Riitta Hari
Keyword(s):  
Sensory Processing ◽  
Quantum Interference ◽  
Sensor Technology ◽  
Brain Dynamics ◽  
Clinical Neuroscience ◽  
Sensory Stimuli ◽  
Superconducting Quantum Interference Devices ◽  
Brain Signals ◽  
Basic Characteristics ◽  
The Brain

This chapter introduces magnetoencephalography (MEG), a tool to study brain dynamics in basic and clinical neuroscience. MEG picks up brain signals with millisecond resolution, as does electroencephalography, but without distortion by skull and scalp. The chapter describes current instrumentation based on superconducting quantum interference devices (SQUIDs). It delineates basic characteristics of measured signals: (1) brain rhythms and their reactivity during sensory processing and various tasks and (2) evoked responses elicited by sensory stimuli, and the dependence of these responses on various stimulus characteristics. Signals are described from healthy and diseased brains. The chapter presents studies of the brain basis of cognition and social interaction studied in dual-MEG setups and describes how MEG applications can be broadened by innovative setups, including frequency tagging. Progress in the field is predicted regarding sensor technology, data analysis, and multimodal brain imaging, all of which could strengthen MEG’s role in the study of brain dynamics.

scholarly journals Pathophysiology of Migraine: A Disorder of Sensory Processing

2017 ◽  
Vol 97 (2) ◽  
pp. 553-622 ◽  
Author(s):  
Peter J. Goadsby ◽  
Philip R. Holland ◽  
Margarida Martins-Oliveira ◽  
Jan Hoffmann ◽  
Christoph Schankin ◽  
...  
Keyword(s):  
Sensory Processing ◽  
Global Scale ◽  
Functional Brain Imaging ◽  
Cgrp Receptor ◽  
Receptor Agonists ◽  
Calcitonin Gene ◽  
Cgrp Receptor Antagonists ◽  
To Come ◽  
The Brain ◽  
Historical Consideration

Plaguing humans for more than two millennia, manifest on every continent studied, and with more than one billion patients having an attack in any year, migraine stands as the sixth most common cause of disability on the planet. The pathophysiology of migraine has emerged from a historical consideration of the “humors” through mid-20th century distraction of the now defunct Vascular Theory to a clear place as a neurological disorder. It could be said there are three questions: why, how, and when? Why: migraine is largely accepted to be an inherited tendency for the brain to lose control of its inputs. How: the now classical trigeminal durovascular afferent pathway has been explored in laboratory and clinic; interrogated with immunohistochemistry to functional brain imaging to offer a roadmap of the attack. When: migraine attacks emerge due to a disorder of brain sensory processing that itself likely cycles, influenced by genetics and the environment. In the first, premonitory, phase that precedes headache, brain stem and diencephalic systems modulating afferent signals, light-photophobia or sound-phonophobia, begin to dysfunction and eventually to evolve to the pain phase and with time the resolution or postdromal phase. Understanding the biology of migraine through careful bench-based research has led to major classes of therapeutics being identified: triptans, serotonin 5-HT1B/1Dreceptor agonists; gepants, calcitonin gene-related peptide (CGRP) receptor antagonists; ditans, 5-HT1Freceptor agonists, CGRP mechanisms monoclonal antibodies; and glurants, mGlu5modulators; with the promise of more to come. Investment in understanding migraine has been very successful and leaves us at a new dawn, able to transform its impact on a global scale, as well as understand fundamental aspects of human biology.

scholarly journals Fast and Robust Learning by Reinforcement Signals: Explorations in the Insect Brain

2009 ◽  
Vol 21 (8) ◽  
pp. 2123-2151 ◽  
Author(s):  
Ramón Huerta ◽  
Thomas Nowotny
Keyword(s):  
Pattern Recognition ◽  
Sensory Processing ◽  
Brain Structures ◽  
Insect Brain ◽  
Proof Of Concept ◽  
Robust Learning ◽  
Fast Learning ◽  
Body Of Knowledge ◽  
Stationary Conditions ◽  
The Brain

We propose a model for pattern recognition in the insect brain. Departing from a well-known body of knowledge about the insect brain, we investigate which of the potentially present features may be useful to learn input patterns rapidly and in a stable manner. The plasticity underlying pattern recognition is situated in the insect mushroom bodies and requires an error signal to associate the stimulus with a proper response. As a proof of concept, we used our model insect brain to classify the well-known MNIST database of handwritten digits, a popular benchmark for classifiers. We show that the structural organization of the insect brain appears to be suitable for both fast learning of new stimuli and reasonable performance in stationary conditions. Furthermore, it is extremely robust to damage to the brain structures involved in sensory processing. Finally, we suggest that spatiotemporal dynamics can improve the level of confidence in a classification decision. The proposed approach allows testing the effect of hypothesized mechanisms rather than speculating on their benefit for system performance or confidence in its responses.

scholarly journals The color phi phenomenon: Not so special, after all?

2021 ◽  
Vol 17 (9) ◽  
pp. e1009344
Author(s):  
Lars Keuninckx ◽  
Axel Cleeremans
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Sensory Processing ◽  
Time Reversal ◽  
Echo State Network ◽  
Connectionist Models ◽  
Long Distance ◽  
Minimal Set ◽  
The Brain

We show how anomalous time reversal of stimuli and their associated responses can exist in very small connectionist models. These networks are built from dynamical toy model neurons which adhere to a minimal set of biologically plausible properties. The appearance of a “ghost” response, temporally and spatially located in between responses caused by actual stimuli, as in the phi phenomenon, is demonstrated in a similar small network, where it is caused by priming and long-distance feedforward paths. We then demonstrate that the color phi phenomenon can be present in an echo state network, a recurrent neural network, without explicitly training for the presence of the effect, such that it emerges as an artifact of the dynamical processing. Our results suggest that the color phi phenomenon might simply be a feature of the inherent dynamical and nonlinear sensory processing in the brain and in and of itself is not related to consciousness.

scholarly journals State-dependent linearisation of mixture representations by the olfactory bulb

2021 ◽  
Author(s):  
Aliya Mari Adefuin ◽  
Janine K Reinert ◽  
Sannder Lindeman ◽  
Izumi Fukunaga
Keyword(s):  
Olfactory Bulb ◽  
Sensory Processing ◽  
Piriform Cortex ◽  
Brain State ◽  
Complex Signals ◽  
Two Photon ◽  
State Dependent ◽  
The Difference ◽  
Apical Dendrites ◽  
The Brain

Sensory systems are often tasked to analyse complex signals from the environment, to separate relevant from irrelevant parts. This process of decomposing signals is challenging when component signals interfere with each other. For example, when a mixture of signals does not equal the sum of its parts, this leads to an unpredictable corruption of signal patterns, making the target recognition harder. In olfaction, nonlinear summation is prevalent at various stages of sensory processing, from stimulus transduction in the nasal epithelium to higher areas, including the olfactory bulb (OB) and the piriform cortex. Here, we investigate how the olfactory system deals with binary mixtures of odours, using two-photon imaging with several behavioural paradigms. Unlike previous studies using anaesthetised animals, we found the mixture summation to be substantially more linear when using awake, head-fixed mice performing an odour detection task. This linearisation was also observed in awake, untrained mice, in both engaged and disengaged states, revealing that the bulk of the difference in mixture summation is explained by the brain state. However, in the apical dendrites of M/T cells, mixture representation is dominated by sublinear summation. Altogether, our results demonstrate that the property of mixture representation in the primary olfactory area likely reflects state-dependent differences in sensory processing.

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