AIMC Topic: Frontal Lobe

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Structured sequence processing and combinatorial binding: neurobiologically and computationally informed hypotheses.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Understanding how the brain forms representations of structured information distributed in time is a challenging endeavour for the neuroscientific community, requiring computationally and neurobiologically informed approaches. The neural mechanisms f...

Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers.

British journal of anaesthesia
BACKGROUND: Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised that integration of multiple QEEG features into a single sedation-level estimator using a machine le...

Bayesian Computation through Cortical Latent Dynamics.

Neuron
Statistical regularities in the environment create prior beliefs that we rely on to optimize our behavior when sensory information is uncertain. Bayesian theory formalizes how prior beliefs can be leveraged and has had a major impact on models of per...

A neural network model for the orbitofrontal cortex and task space acquisition during reinforcement learning.

PLoS computational biology
Reinforcement learning has been widely used in explaining animal behavior. In reinforcement learning, the agent learns the value of the states in the task, collectively constituting the task state space, and uses the knowledge to choose actions and a...

A neural model of the frontal eye fields with reward-based learning.

Neural networks : the official journal of the International Neural Network Society
Decision-making is a flexible process dependent on the accumulation of various kinds of information; however, the corresponding neural mechanisms are far from clear. We extended a layered model of the frontal eye field to a learning-based model, usin...

Modality-independent representations of small quantities based on brain activation patterns.

Human brain mapping
Machine learning or MVPA (Multi Voxel Pattern Analysis) studies have shown that the neural representation of quantities of objects can be decoded from fMRI patterns, in cases where the quantities were visually displayed. Here we apply these technique...

Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
BACKGROUND AND PURPOSE: We analyzed the ability of a machine learning approach that uses diffusion tensor imaging (DTI) structural connectomes to determine lateralization of epileptogenicity in temporal lobe epilepsy (TLE).

Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI.

International journal of neural systems
Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mecha...

Transforming text to music using artificial intelligence improves the frontal lobe function of normal older adults.

Brain and behavior
INTRODUCTION: Recent advances in artificial intelligence (AI) have been substantial. We investigated the effectiveness of an online meeting in which normal older adults (otokai) used a music-generative AI that transforms text to music (Music Trinity ...