scholarly journals The role of rebound spikes in the maintenance of self-sustained neural spiking activity

2021 ◽  
Author(s):  
Bruno Andre Santos ◽  
Rogerio Martins Gomes ◽  
Phil Husbands
Keyword(s):  
Neural Network ◽  
Neural Activity ◽  
Neural Circuit ◽  
Neural Mechanisms ◽  
Neural Systems ◽  
Spiking Neural Network ◽  
Low Frequencies ◽  
Neural Spiking ◽  
Theoretical Issues

AbstractIn general, the mechanisms that maintain the activity of neural systems after a triggering stimulus has been removed are not well understood. Different mechanisms involving at the cellular and network levels have been proposed. In this work, based on analysis of a computational model of a spiking neural network, it is proposed that the spike that occurs after a neuron is inhibited (the rebound spike) can be used to sustain the activity in a recurrent inhibitory neural circuit after the stimulation has been removed. It is shown that, in order to sustain the activity, the neurons participating in the recurrent circuit should fire at low frequencies. It is also shown that the occurrence of a rebound spike depends on a combination of factors including synaptic weights, synaptic conductances and the neuron state. We point out that the model developed here is minimalist and does not aim at empirical accuracy. Its purpose is to raise and discuss theoretical issues that could contribute to the understanding of neural mechanisms underlying self-sustained neural activity.

scholarly journals Exploring the role of expectations and stimulus relevance on stimulus-specific neural representations and conscious report

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Erik L Meijs ◽  
Pim Mostert ◽  
Heleen A Slagter ◽  
Floris P de Lange ◽  
Simon van Gaal
Keyword(s):  
Attentional Blink ◽  
Neural Activity ◽  
Subjective Experience ◽  
Activity Patterns ◽  
Neural Mechanisms ◽  
Top Down ◽  
Neural Representations ◽  
Dependent Signal ◽  
Task Irrelevant

Abstract Subjective experience can be influenced by top-down factors, such as expectations and stimulus relevance. Recently, it has been shown that expectations can enhance the likelihood that a stimulus is consciously reported, but the neural mechanisms supporting this enhancement are still unclear. We manipulated stimulus expectations within the attentional blink (AB) paradigm using letters and combined visual psychophysics with magnetoencephalographic (MEG) recordings to investigate whether prior expectations may enhance conscious access by sharpening stimulus-specific neural representations. We further explored how stimulus-specific neural activity patterns are affected by the factors expectation, stimulus relevance and conscious report. First, we show that valid expectations about the identity of an upcoming stimulus increase the likelihood that it is consciously reported. Second, using a series of multivariate decoding analyses, we show that the identity of letters presented in and out of the AB can be reliably decoded from MEG data. Third, we show that early sensory stimulus-specific neural representations are similar for reported and missed target letters in the AB task (active report required) and an oddball task in which the letter was clearly presented but its identity was task-irrelevant. However, later sustained and stable stimulus-specific representations were uniquely observed when target letters were consciously reported (decision-dependent signal). Fourth, we show that global pre-stimulus neural activity biased perceptual decisions for a ‘seen’ response. Fifth and last, no evidence was obtained for the sharpening of sensory representations by top-down expectations. We discuss these findings in light of emerging models of perception and conscious report highlighting the role of expectations and stimulus relevance.

scholarly journals Human Empathy Through the Lens of Social Neuroscience

2006 ◽  
Vol 6 ◽  
pp. 1146-1163 ◽  
Author(s):  
Jean Decety ◽  
Claus Lamm
Keyword(s):  
Information Processing ◽  
Cognitive Neuroscience ◽  
Executive Control ◽  
Perspective Taking ◽  
Functional Neuroimaging ◽  
Social Neuroscience ◽  
Neural Mechanisms ◽  
The Self ◽  
Neural Systems

Empathy is the ability to experience and understand what others feel without confusion between oneself and others. Knowing what someone else is feeling plays a fundamental role in interpersonal interactions. In this paper, we articulate evidence from social psychology and cognitive neuroscience, and argue that empathy involves both emotion sharing (bottom-up information processing) and executive control to regulate and modulate this experience (top-down information processing), underpinned by specific and interacting neural systems. Furthermore, awareness of a distinction between the experiences of the self and others constitutes a crucial aspect of empathy. We discuss data from recent behavioral and functional neuroimaging studies with an emphasis on the perception of pain in others, and highlight the role of different neural mechanisms that underpin the experience of empathy, including emotion sharing, perspective taking, and emotion regulation.

scholarly journals Investigating the role of orientation information in face processing within a spiking neural network

2021 ◽  
Vol 21 (9) ◽  
pp. 2766
Author(s):  
Matthew Bennett ◽  
Tushar Chauhan ◽  
Benoît Cottereau ◽  
Valerie Goffaux
Keyword(s):  
Neural Network ◽  
Face Processing ◽  
Spiking Neural Network ◽  
Orientation Information

scholarly journals The brain and its time: intrinsic neural timescales are key for input processing

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mehrshad Golesorkhi ◽  
Javier Gomez-Pilar ◽  
Federico Zilio ◽  
Nareg Berberian ◽  
Annemarie Wolff ◽  
...  
Keyword(s):  
Neural Activity ◽  
Recent Literature ◽  
Neural Mechanisms ◽  
Temporal Organization ◽  
Multiple Timescales ◽  
Input Processing ◽  
Evolutionary Context ◽  
Input Sharing ◽  
The Brain

AbstractWe process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs’ stochastics with the ongoing temporal statistics of the brain’s neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.

ROLE OF CHAOS IN TRIAL-AND-ERROR PROBLEM SOLVING BY AN ARTIFICIAL NEURAL NETWORK

1996 ◽  
Vol 07 (01) ◽  
pp. 101-108 ◽  
Author(s):  
ICHIRO OBANA ◽  
YASUHIRO FUKUI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Problem Solving ◽  
Motor Neurons ◽  
Neural Activity ◽  
Random Walking ◽  
Trial And Error ◽  
Naturally Occurring ◽  
The Neural Network

One role of chaotic neural activity is illustrated by means of computer simulations of an imaginary agent’s goal-oriented behavior. The agent has a simplified neural network with seven neurons and three legs. The neural network consists of one photosensory neuron and three pairs of inter- and motor neurons. The three legs whose movements are governed by the three motor neurons allow the agent to walk in six concentric radial directions on a plane. It is intended that the neural network causes the agent to walk in a direction of greater brightness, to reach finally the most brightly lit place on the plane. The presence of only one sensory neuron has an important meaning. That is, no immediate information on directions of greater brightness is sensed by the agent. In other words, random walking in the manner of trial-and-error problem solving must be involved in the agent’s walking. Chaotic firing of the motor neurons is intended to play a crucial role in generating the random walking. Brief random walking and rapid straight walking in a direction of greater brightness were observed to occur alternately in the computer simulation. Controlled chaos in naturally occurring neural networks may play a similar role.

scholarly journals Human parietal cortex lesions impact the precision of spatial working memory

2016 ◽  
Vol 116 (3) ◽  
pp. 1049-1054 ◽  
Author(s):  
Wayne E. Mackey ◽  
Orrin Devinsky ◽  
Werner K. Doyle ◽  
John G. Golfinos ◽  
Clayton E. Curtis
Keyword(s):  
Working Memory ◽  
Parietal Cortex ◽  
Neural Activity ◽  
Spatial Working Memory ◽  
Great Sensitivity ◽  
Neural Mechanisms ◽  
Visually Guided ◽  
Visuomotor Processing ◽  
Relationship Of

The neural mechanisms that support working memory (WM) depend on persistent neural activity. Within topographically organized maps of space in dorsal parietal cortex, spatially selective neural activity persists during WM for location. However, to date, the necessity of these topographic subregions of human parietal cortex for WM remains unknown. To test the causal relationship of these areas to WM, we compared the performance of patients with lesions to topographically organized parietal cortex with those of controls on a memory-guided saccade (MGS) task as well as a visually guided saccade (VGS) task. The MGS task allowed us to measure WM precision continuously with great sensitivity, whereas the VGS task allowed us to control for any deficits in general spatial or visuomotor processing. Compared with controls, patients generated memory-guided saccades that were significantly slower and less accurate, whereas visually guided saccades were unaffected. These results provide key missing evidence for the causal role of topographic areas in human parietal cortex for WM, as well as the neural mechanisms supporting WM.

scholarly journals α-Synuclein aggregation in the olfactory bulb induces olfactory deficits by perturbing granule cells and granular–mitral synaptic transmission

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Fengjiao Chen ◽  
Wei Liu ◽  
Penglai Liu ◽  
Zhen Wang ◽  
You Zhou ◽  
...  
Keyword(s):  
Olfactory Bulb ◽  
Neural Circuit ◽  
Granule Cells ◽  
Gamma Oscillations ◽  
Olfactory Dysfunction ◽  
Synaptic Function ◽  
Neural Mechanisms ◽  
Motor Symptom ◽  
High Gamma

AbstractOlfactory dysfunction is an early pre-motor symptom of Parkinson’s disease (PD) but the neural mechanisms underlying this dysfunction remain largely unknown. Aggregation of α-synuclein is observed in the olfactory bulb (OB) during the early stages of PD, indicating a relationship between α-synuclein pathology and hyposmia. Here we investigate whether and how α-synuclein aggregates modulate neural activity in the OB at the single-cell and synaptic levels. We induced α-synuclein aggregation specifically in the OB via overexpression of double-mutant human α-synuclein by an adeno-associated viral (AAV) vector. We found that α-synuclein aggregation in the OB decreased the ability of mice to detect odors and to perceive attractive odors. The spontaneous activity and odor-evoked firing rates of single mitral/tufted cells (M/Ts) were increased by α-synuclein aggregates with the amplitude of odor-evoked high-gamma oscillations increased. Furthermore, the decreased activity in granule cells (GCs) and impaired inhibitory synaptic function were responsible for the observed hyperactivity of M/Ts induced by α-synuclein aggregates. These results provide direct evidences of the role of α-synuclein aggregates on PD-related olfactory dysfunction and reveal the neural circuit mechanisms by which olfaction is modulated by α-synuclein pathology.

scholarly journals A Neuroscience-Inspired Spiking Neural Network for Auditory Spatial Attention Detection Using Single-Trial EEG

2021 ◽  
Author(s):  
Faramarz Faghihi ◽  
Siqi Cai ◽  
Ahmed Moustafa
Keyword(s):  
Neural Network ◽  
Spatial Attention ◽  
Sparse Coding ◽  
Spiking Neurons ◽  
Training Data ◽  
Spiking Neural Network ◽  
Single Trial ◽  
Auditory Spatial Attention ◽  
Eeg Data

Recently, studies have shown that the alpha band (8-13 Hz) EEG signals enable the decoding of auditory spatial attention. However, deep learning methods typically requires a large amount of training data. Inspired by sparse coding in cortical neurons, we propose a spiking neural network model for auditory spatial attention detection. The model is composed of three neural layers, two of them are spiking neurons. We formulate a new learning rule that is based on firing rate of pre synaptic and post-synaptic neurons in the first layer and the second layer of spiking neurons. The third layer consists of 10 spiking neurons that the pattern of their firing rate after training is used in test phase of the method. The proposed method extracts the patterns of recorded EEG of leftward and rightward attention, independently, and uses them to train network to detect the auditory spatial attention. In addition, a computational approach is presented to find the best single-trial EEG data as training samples of leftward and rightward attention EEG. In this model, the role of using low connectivity rate of the layers and specific range of learning parameters in sparse coding is studied. Importantly, unlike most prior model, our method requires 10% of EEG data as training data and has shown 90% accuracy in average. This study suggests new insights into the role of sparse coding in both biological networks and brain-inspired machine learning.

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