Random noise promotes slow heterogeneous synaptic dynamics important for robust working memory computation.

Recurrent neural networks (RNNs) based on model neurons that communicate via continuous signals have been widely used to study how cortical neural circuits perform cognitive tasks. Training such networks to perform tasks that require information maintenance over a brief period (i.e.

HED LANG - A Hierarchical Event Descriptors library extension for annotation of language cognition experiments.

Experimental design in language cognition research often involves presenting language material while measuring associated behavior and/or neural activity. To make the collected data easily and fully analyzable by both the original data authors and others, it is important to have detailed information about the stimulus presentation events, including the nature and properties of the presented stimuli, using a common vocabulary and syntax. We present HED LANG, a library extension of the Hierarchical Event Descriptors (HED) event annotation schema for time series behavioral and neuroimaging data.

Sensory Entrained TMS (seTMS) enhances motor cortex excitability.

Transcranial magnetic stimulation (TMS) applied to the motor cortex has revolutionized the study of motor physiology in humans. Despite this, TMS-evoked electrophysiological responses show significant variability, due in part to inconsistencies between TMS pulse timing and ongoing brain oscillations. Variable responses to TMS limit mechanistic insights and clinical efficacy, necessitating the development of methods to precisely coordinate the timing of TMS pulses to the phase of relevant oscillatory activity.

Neurological Evidence of Diverse Self-Help Breathing Training With Virtual Reality and Biofeedback Assistance: Extensive Exploration Study of Electroencephalography Markers.

Background: Recent advancements in virtual reality (VR) and biofeedback (BF) technologies have opened new avenues for breathing training. Breathing training has been suggested as an effective means for mental disorders, but it is difficult to master the technique at the beginning. VR-BF technologies address the problem of breathing, and visualizing breathing may facilitate the learning of breathing training.

Neuromorphic engineering: Artificial brains for artificial intelligence.

Neuromorphic engineering is a research discipline that tries to bridge the gaps between neuroscience and engineering, cognition and algorithms, and natural and artificial intelligence. Neuromorphic engineering promises revolutionary breakthroughs that could rapidly advance our understanding of the brain and pave the way toward more human-like and sustainable artificial intelligence. But first, it will have to find its way out of the laboratory.

A multi-feature fusion graph attention network for decoding motor imagery intention in spinal cord injury patients.

Electroencephalogram (EEG) signals exhibit temporal-frequency-spatial multi-domain feature, and due to the nonplanar nature of the brain surface, the electrode distributions follow non-Euclidean topology. To fully resolve the EEG signals, this study proposes a temporal-frequency-spatial multi-domain feature fusion graph attention network (GAT) for motor imagery (MI) intention recognition in spinal cord injury (SCI) patients.The proposed model uses phase-locked value (PLV) to extract spatial phase connectivity information between EEG channels and continuous wavelet transform to extract valid EEG information in the time-frequency domain.

Exploring the limits of hierarchical world models in reinforcement learning.

Hierarchical model-based reinforcement learning (HMBRL) aims to combine the sample efficiency of model-based reinforcement learning with the abstraction capability of hierarchical reinforcement learning. While HMBRL has great potential, the structural and conceptual complexities of current approaches make it challenging to extract general principles, hindering understanding and adaptation to new use cases, and thereby impeding the overall progress of the field. In this work we describe a novel HMBRL framework and evaluate it thoroughly.

Correction: Linking Dementia Pathology and Alteration in Brain Activation to Complex Daily Functional Decline During the Preclinical Dementia Stages: Protocol for a Prospective Observational Cohort Study.

[This corrects the article DOI: 10.2196/56726.].

Distinct mechanisms and functions of episodic memory.

The concept of episodic memory (EM) faces significant challenges by two claims: EM might not be a distinct memory system, and EM might be an epiphenomenon of a more general capacity for mental time travel (MTT). Nevertheless, the observations leading to these arguments do not preclude the existence of a mechanically and functionally distinct EM system. First, modular systems, like cognition, can have distinct subsystems that may not be distinguishable in the system's final output.

Gain control of sensory input across polysynaptic circuitries in mouse visual cortex by a single G protein-coupled receptor type (5-HT).

Response gain is a crucial means by which modulatory systems control the impact of sensory input. In the visual cortex, the serotonergic 5-HT receptor is key in such modulation. However, due to its expression across different cell types and lack of methods that allow for specific activation, the underlying network mechanisms remain unsolved.

Neural Dynamic Principles for an Intentional Embodied Agent.

How situated embodied agents may achieve goals using knowledge is the classical question of natural and artificial intelligence. How organisms achieve this with their nervous systems is a central challenge for a neural theory of embodied cognition. To structure this challenge, we borrow terms from Searle's analysis of intentionality in its two directions of fit and six psychological modes (perception, memory, belief, intention-in-action, prior intention, desire).

The effect of Zentangle on cognitive focus, emotional well-being, and stress levels: A neural perspective.

Purpose: This study aimed to assess how a Zentangle intervention influences cognitive focus, emotional well-being, stress levels, and neural activity patterns across brain regions and frequency bands.

Method: A cohort of 30 healthy adults, all without prior Zentangle experience, participated in this study. Electroencephalography (EEG) was used to measure their brain activity, and self-reported data were collected through questionnaires to assess subjects' concentration levels, emotional calm, and stress and anxiety.

How are segmentation and binding computed and represented in the brain?

A programmatically described solution to the segmentation problem is taken as opportunity to dicuss the neural architecture problem of vision. At the center of this problem is the formation of holistic entities (the Gestalt phenomenon) out of masses of neurons (the binding problem). As formulated in the Dynamic Net Architecture (DNA), neurons can become part of a (short-term) stable state only if supported inside a coherent network ('net').

A neural network model for online one-shot storage of pattern sequences.

Based on the CRISP theory (Content Representation, Intrinsic Sequences, and Pattern completion), we present a computational model of the hippocampus that allows for online one-shot storage of pattern sequences without the need for a consolidation process. In our model, CA3 provides a pre-trained sequence that is hetero-associated with the input sequence, rather than storing a sequence in CA3. That is, plasticity on a short timescale only occurs in the incoming and outgoing connections of CA3, not in its recurrent connections.

Multimodal fusion for anticipating human decision performance.

Anticipating human decisions while performing complex tasks remains a formidable challenge. This study proposes a multimodal machine-learning approach that leverages image features and electroencephalography (EEG) data to predict human response correctness in a demanding visual searching task. Notably, we extract a novel set of image features pertaining to object relationships using the Segment Anything Model (SAM), which enhances prediction accuracy compared to traditional features.

Events in context-The HED framework for the study of brain, experience and behavior.

The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is driven on multiple time scales by an ever-evolving flow of sensory, proprioceptive, and idiothetic experience. Neuroimaging experiments seek to isolate and focus on some aspect of these complex dynamics to better understand how human experience, cognition, behavior, and health are supported by brain activity.

Linking Dementia Pathology and Alteration in Brain Activation to Complex Daily Functional Decline During the Preclinical Dementia Stages: Protocol for a Prospective Observational Cohort Study.

Background: Progressive difficulty in performing everyday functional activities is a key diagnostic feature of dementia syndromes. However, not much is known about the neural signature of functional decline, particularly during the very early stages of dementia. Early intervention before overt impairment is observed offers the best hope of reducing the burdens of Alzheimer disease (AD) and other dementias.

Decoding imagined speech with delay differential analysis.

Speech decoding from non-invasive EEG signals can achieve relatively high accuracy (70-80%) for strictly delimited classification tasks, but for more complex tasks non-invasive speech decoding typically yields a 20-50% classification accuracy. However, decoder generalization, or how well algorithms perform objectively across datasets, is complicated by the small size and heterogeneity of existing EEG datasets. Furthermore, the limited availability of open access code hampers a comparison between methods.

A user-friendly visual brain-computer interface based on high-frequency steady-state visual evoked fields recorded by OPM-MEG.

. Magnetoencephalography (MEG) shares a comparable time resolution with electroencephalography. However, MEG excels in spatial resolution, enabling it to capture even the subtlest and weakest brain signals for brain-computer interfaces (BCIs).

Older adults use fewer muscles to overcome perturbations during a seated locomotor task.

Locomotor perturbations provide insights into humans' response to motor errors. We investigated the differences in motor adaptation and muscle cocontraction between young and older adults during perturbed-arm and -leg recumbent stepping. We hypothesized that besides prolonged adaptation due to use-dependent learning, older adults would exhibit greater muscle cocontraction than young adults in response to the perturbations.