AIMC Topic: Prefrontal Cortex

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Enhancing classification accuracy of HRF signals in fNIRS using semi-supervised learning and filtering.

Progress in brain research
This paper introduces a novel approach to enhance the classification accuracy of hemodynamic response function (HRF) signals acquired through functional near-infrared spectroscopy (fNIRS). Leveraging a semi-supervised learning (SSL) framework alongsi...

Study of a PST-trained voice-enabled artificial intelligence counselor for adults with emotional distress (SPEAC-2): Design and methods.

Contemporary clinical trials
BACKGROUND: Novel and scalable psychotherapies are urgently needed to address the depression and anxiety epidemic. Leveraging artificial intelligence (AI), a voice-based virtual coach named Lumen was developed to deliver problem solving treatment (PS...

Interpretable deep learning model for major depressive disorder assessment based on functional near-infrared spectroscopy.

Asian journal of psychiatry
BACKGROUND: Major depressive disorder (MDD) affects a substantial number of individuals worldwide. New approaches are required to improve the diagnosis of MDD, which relies heavily on subjective reports of depression-related symptoms.

Increased functional connectivity coupling with supplementary motor area in blepharospasm at rest.

Brain research
OBJECTIVE: To explore the abnormalities of brain function in blepharospasm (BSP) and to illustrate its neural mechanisms by assuming supplementary motor area (SMA) as the entry point.

Schema formation in a neural population subspace underlies learning-to-learn in flexible sensorimotor problem-solving.

Nature neuroscience
Learning-to-learn, a progressive speedup of learning while solving a series of similar problems, represents a core process of knowledge acquisition that draws attention in both neuroscience and artificial intelligence. To investigate its underlying b...

Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals.

PLoS computational biology
Humans can learn several tasks in succession with minimal mutual interference but perform more poorly when trained on multiple tasks at once. The opposite is true for standard deep neural networks. Here, we propose novel computational constraints for...

Integration of velocity-dependent spatio-temporal structure of place cell activation during navigation in a reservoir model of prefrontal cortex.

Biological cybernetics
Sequential behavior unfolds both in space and in time. The same spatial trajectory can be realized in different manners in the same overall time by changing instantaneous speeds. The current research investigates how speed profiles might be given beh...

Geometry of neural computation unifies working memory and planning.

Proceedings of the National Academy of Sciences of the United States of America
Real-world tasks require coordination of working memory, decision-making, and planning, yet these cognitive functions have disproportionately been studied as independent modular processes in the brain. Here, we propose that contingency representation...

Young children with autism show atypical prefrontal cortical responses to humanoid robots: An fNIRS study.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology
BACKGROUND: Previous behavioral studies have found that children with autism spectrum disorder (ASD) show greater interest in humanoid robots than in humans. However, the neural mechanism underlying this is not clear. This study compared brain activa...

Pruning recurrent neural networks replicates adolescent changes in working memory and reinforcement learning.

Proceedings of the National Academy of Sciences of the United States of America
Adolescent development is characterized by an improvement in multiple cognitive processes. While performance on cognitive operations improves during this period, the ability to learn new skills quickly, for example, a new language, decreases. During ...