Neural Representation of the Horizontal Extent of Spatial Boundary Cues

Abstract Precise auditory perception at a subcortical level (neural representation and encoding of sound) has been suggested as a form of implicit L2 aptitude in naturalistic settings. Emerging evidence suggests that such implicit aptitude explains some variance in L2 speech perception and production among adult learners with different first language backgrounds and immersion experience. By examining 46 Chinese learners of English, the current study longitudinally investigated the extent to which explicit and implicit auditory processing ability could predict L2 segmental and prosody acquisition over a 5-month early immersion. According to the results, participants’ L2 gains were associated with more explicit and integrative auditory processing ability (remembering and reproducing music sequences), while the role of implicit, preconscious perception appeared to be negligible at the initial stage of postpubertal L2 speech learning.

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Adult‐like neural representation of species‐specific songs in the auditory forebrain of zebra finch nestlings

Developmental Neurobiology ◽

10.1002/dneu.22802 ◽

2020 ◽

Author(s):

Katie M. Schroeder ◽

Luke Remage‐Healey

Keyword(s):

Zebra Finch ◽

Neural Representation ◽

Auditory Forebrain ◽

Species Specific

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Mirror neurons are modulated by grip force and reward expectation in the sensorimotor cortices (S1, M1, PMd, PMv)

Scientific Reports ◽

10.1038/s41598-021-95536-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Md Moin Uddin Atique ◽

Joseph Thachil Francis

Keyword(s):

Neural Activity ◽

Visual Information ◽

Mirror Neurons ◽

Grip Force ◽

Neural Representation ◽

Virtual Object ◽

Time Lags ◽

Subjective Value ◽

Machine Interface ◽

Definition Of

AbstractMirror Neurons (MNs) respond similarly when primates make or observe grasping movements. Recent work indicates that reward expectation influences rostral M1 (rM1) during manual, observational, and Brain Machine Interface (BMI) reaching movements. Previous work showed MNs are modulated by subjective value. Here we expand on the above work utilizing two non-human primates (NHPs), one male Macaca Radiata (NHP S) and one female Macaca Mulatta (NHP P), that were trained to perform a cued reward level isometric grip-force task, where the NHPs had to apply visually cued grip-force to move and transport a virtual object. We found a population of (S1 area 1–2, rM1, PMd, PMv) units that significantly represented grip-force during manual and observational trials. We found the neural representation of visually cued force was similar during observational trials and manual trials for the same units; however, the representation was weaker during observational trials. Comparing changes in neural time lags between manual and observational tasks indicated that a subpopulation fit the standard MN definition of observational neural activity lagging the visual information. Neural activity in (S1 areas 1–2, rM1, PMd, PMv) significantly represented force and reward expectation. In summary, we present results indicating that sensorimotor cortices have MNs for visually cued force and value.

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Brain reactivity to humorous films is affected by insomnia

SLEEP ◽

10.1093/sleep/zsab081 ◽

2021 ◽

Author(s):

Ernesto Sanz-Arigita ◽

Yannick Daviaux ◽

Marc Joliot ◽

Bixente Dilharreguy ◽

Jean-Arthur Micoulaud-Franchi ◽

...

Keyword(s):

Emotional Reactivity ◽

Brain Activity ◽

Brain Network ◽

Anterior Cingulate ◽

Neural Representation ◽

Cingulate Gyrus ◽

Anterior Cingulate Gyrus ◽

Left Caudate ◽

Neural Reactivity ◽

The Right

Abstract Study objectives Emotional reactivity to negative stimuli has been investigated in insomnia, but little is known about emotional reactivity to positive stimuli and its neural representation. Methods We used 3T fMRI to determine neural reactivity during the presentation of standardized short, 10-40-s, humorous films in insomnia patients (n=20, 18 females, aged 27.7 +/- 8.6 years) and age-matched individuals without insomnia (n=20, 19 females, aged 26.7 +/- 7.0 years), and assessed humour ratings through a visual analogue scale (VAS). Seed-based functional connectivity was analysed for left and right amygdala networks: group-level mixed-effects analysis (FLAME; FSL) was used to compare amygdala connectivity maps between groups. Results fMRI seed-based analysis of the amygdala revealed stronger neural reactivity in insomnia patients than in controls in several brain network clusters within the reward brain network, without humour rating differences between groups (p = 0.6). For left amygdala connectivity, cluster maxima were in the left caudate (Z=3.88), left putamen (Z=3.79) and left anterior cingulate gyrus (Z=4.11), while for right amygdala connectivity, cluster maxima were in the left caudate (Z=4.05), right insula (Z=3.83) and left anterior cingulate gyrus (Z=4.29). Cluster maxima of the right amygdala network were correlated with hyperarousal scores in insomnia patients only. Conclusions Presentation of humorous films leads to increased brain activity in the neural reward network for insomnia patients compared to controls, related to hyperarousal features in insomnia patients, in the absence of humor rating group differences. These novel findings may benefit insomnia treatment interventions.

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A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

Computational Intelligence and Neuroscience ◽

10.1155/2011/869348 ◽

2011 ◽

Vol 2011 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Karim El-Laithy ◽

Martin Bogdan

Keyword(s):

Reinforcement Learning ◽

Spike Timing ◽

Neural Representation ◽

Model Parameters ◽

Learning Framework ◽

Reference Target ◽

Wide Range ◽

Spiking Network ◽

Dynamic Synapses ◽

Exclusive Or

An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

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A Sequential Autoencoder for Teleconnection Analysis

Remote Sensing ◽

10.3390/rs12050851 ◽

2020 ◽

Vol 12 (5) ◽

pp. 851 ◽

Cited By ~ 1

Author(s):

Jiena He ◽

J. Ronald Eastman

Keyword(s):

Time Series ◽

Neural Representation ◽

Strongly Correlated ◽

Atmospheric Sciences ◽

Teleconnection Patterns ◽

The Earth ◽

Unsupervised Neural Network ◽

Residual Series ◽

Components Analysis ◽

Computationally Intensive

Many aspects of the earth system are known to have preferred patterns of variability, variously known in the atmospheric sciences as modes or teleconnections. Approaches to discovering these patterns have included principal components analysis and empirical orthogonal teleconnection (EOT) analysis. The latter is very effective but is computationally intensive. Here, we present a sequential autoencoder for teleconnection analysis (SATA). Like EOT, it discovers teleconnections sequentially, with subsequent analyses being based on residual series. However, unlike EOT, SATA uses a basic linear autoencoder as the primary tool for analysis. An autoencoder is an unsupervised neural network that learns an efficient neural representation of input data. With SATA, the input is an image time series and the neural representation is a unidimensional time series. SATA then locates the 0.5% of locations with the strongest correlation with the neural representation and averages their temporal vectors to characterize the teleconnection. Evaluation of the procedure showed that it is several orders of magnitude faster than other approaches to EOT, produces teleconnection patterns that are more strongly correlated to well-known teleconnections, and is particularly effective in finding teleconnections with multiple centers of action (such as dipoles).

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