AIMC Topic: Dopamine

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Learning to Produce Syllabic Speech Sounds via Reward-Modulated Neural Plasticity.

PloS one
At around 7 months of age, human infants begin to reliably produce well-formed syllables containing both consonants and vowels, a behavior called canonical babbling. Over subsequent months, the frequency of canonical babbling continues to increase. H...

Expanding the role of striatal cholinergic interneurons and the midbrain dopamine system in appetitive instrumental conditioning.

Journal of neurophysiology
The basal ganglia are a collection of subcortical nuclei thought to underlie a wide variety of vertebrate behavior. Although a great deal is known about the functional and physiological properties of the basal ganglia, relatively few models have been...

Identifying the Basal Ganglia network model markers for medication-induced impulsivity in Parkinson's disease patients.

PloS one
Impulsivity, i.e. irresistibility in the execution of actions, may be prominent in Parkinson's disease (PD) patients who are treated with dopamine precursors or dopamine receptor agonists. In this study, we combine clinical investigations with comput...

A spiking neural network based on the basal ganglia functional anatomy.

Neural networks : the official journal of the International Neural Network Society
We introduce a spiking neural network of the basal ganglia capable of learning stimulus-action associations. We model learning in the three major basal ganglia pathways, direct, indirect and hyperdirect, by spike time dependent learning and consideri...

Portable and intelligent ratio fluorometry and colorimetry for dual-mode detection of dopamine based on B, N-codoped carbon dots and machine learning.

Talanta
A dual-mode approach was developed for dopamine (DA) assay based on boron (B) and nitrogen (N) co-doped carbon dots (B, N-CDs). This platform enabled highly sensitive and specific detection of DA in biological samples through collaborative ratio fluo...

A Chemistry-Informed Generative Deep Learning Approach for Enhancing Voltammetric Neurochemical Sensing in Living Mouse Brain.

Journal of the American Chemical Society
Exploring the time-resolved dynamics of neurochemicals is essential for deciphering neuronal functions, intercellular communication, and neurophysiological or pathological mechanisms. However, the complex interplay among neurochemicals between neuroc...

The role of the dopamine system in autism spectrum disorder revealed using machine learning: an ABIDE database-based study.

Cerebral cortex (New York, N.Y. : 1991)
This study explores the diagnostic value of dopamine system imaging characteristics in children with autism spectrum disorder. Functional magnetic resonance data from 551 children in the Autism Brain Imaging Data Exchange database were analyzed, focu...

Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Electronic health record (EHR) data may facilitate the identification of rare diseases in patients, such as aromatic l-amino acid decarboxylase deficiency (AADCd), an autosomal recessive disease caused by pathogenic variants in the dopa d...

Simultaneous Estimation of Tonic Dopamine and Serotonin with High Temporal Resolution In Vitro Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Quantitative measurement of the phasic (changes in several seconds) and tonic (changes in minutes to hours) level changes of neurotransmitters is an essential technique for understanding brain functions and brain diseases regulated by the neurotransm...

Reinforcement Learning: Full Glass or Empty - Depends Who You Ask.

Current biology : CB
An extension of the prediction error theory of dopamine, imported from artificial intelligence, represents the full distribution over future rewards rather than only the average and better explains dopamine responses.