Serotonergic and dopaminergic neurons in the dorsal raphe are differentially altered in a mouse model for Parkinson’s disease DOI Open Access
Laura Boi, Yvonne Johansson, Raffaella Tonini

и другие.

Опубликована: Авг. 21, 2023

Parkinson’s disease (PD) is characterized by motor impairments caused degeneration of dopamine neurons in the substantia nigra pars compacta. In addition to these symptoms, PD patients often suffer from non-motor co-morbidities including sleep and psychiatric disturbances, which are thought depend on concomitant alterations serotonergic noradrenergic transmission. A primary locus dorsal raphe nucleus (DRN), providing brain-wide input. Here, we identified electrophysiological morphological parameters classify dopaminergic murine DRN under control conditions a model, following striatal injection catecholamine toxin, 6-hydroxydopamine (6-OHDA). Electrical properties both neuronal populations were altered 6-OHDA. neurons, most changes reversed when 6-OHDA was injected combination with desipramine, noradrenaline reuptake inhibitor, protecting terminals. Our results show that depletion mouse model causes neural circuitry.

Язык: Английский

Injectable Fluorescent Neural Interfaces for Cell-Specific Stimulating and Imaging DOI
Shumao Xu, Xiao Xiao, Farid Manshaii

и другие.

Nano Letters, Год журнала: 2024, Номер unknown

Опубликована: Апрель 12, 2024

Building on current explorations in chronic optical neural interfaces, it is essential to address the risk of photothermal damage traditional optogenetics. By focusing calcium fluorescence for imaging rather than stimulation, injectable fluorescent interfaces significantly minimize and improve accuracy neuronal imaging. Key advancements including use microelectronics targeted electrical stimulation their integration with cell-specific genetically encoded indicators have been discussed. These electronics that allow post-treatment retrieval offer a minimally invasive solution, enhancing both usability reliability. Furthermore, bioelectronics enables precise recording individual neurons. This shift not only minimizes risks such as conversion but also boosts safety, specificity, effectiveness Embracing these represents significant leap forward biomedical engineering neuroscience, paving way advanced brain–machine interfaces.

Язык: Английский

Процитировано

4

Deregulated mRNA and microRNA Expression Patterns in the Prefrontal Cortex of the BTBR Mouse Model of Autism DOI Creative Commons
Catherine Mooney,

Andrea Parlante,

Giulia Canarutto

и другие.

Molecular Neurobiology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 14, 2025

Abstract Autism spectrum disorder (ASD) is a neurodevelopmental condition caused by both genetic and environmental factors. Since no single gene variant accounts for more than 1% of the cases, converging actions ASD-related genes other factors, including microRNAs (miRNAs), may contribute to ASD pathogenesis. To date, few studies have simultaneously investigated mRNA miRNA profiles in an ASD-relevant model. The BTBR mouse strain displays range behaviors with ASD-like features but little known about protein-coding noncoding expression landscape that underlie phenotype. Here we performed parallel profiling using prefrontal cortex (PFC) C57BL/6 J (B6) mice. This identified 1063 differentially expressed 48 miRNAs. Integration data strong inverse relationship between upregulated (DEGs) downregulated miRNAs, vice versa. Pathway analysis, taking account miRNAs their target mRNAs highlighted significant shared enrichment immune signaling, myelination, processes. Notably, changes were predicted affect synapse-related functions did not find linked cellular components or biological processes related synapses PFC mice, indicating evade control. In contrast, extensive relationships DEGs suggesting role as potential hub coordinators expression. Profiling findings confirmed via qRT-PCR representative transcripts Our study underscores complex interplay regulation within inflammatory pathways model, offering insights into mechanisms ASD. These results support value model identify strategies could adjust molecular therapeutic applications research.

Язык: Английский

Процитировано

0

Neuromodulatory processing in the bi-pathway brain architecture DOI Creative Commons
Funing Li, Changmei Zhang, Jiulin Du

и другие.

Current Opinion in Neurobiology, Год журнала: 2025, Номер 93, С. 103055 - 103055

Опубликована: Май 23, 2025

Язык: Английский

Процитировано

0

Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network DOI Creative Commons

Ikhwan Jeon,

Taegon Kim

Frontiers in Computational Neuroscience, Год журнала: 2023, Номер 17

Опубликована: Июнь 28, 2023

Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information diverse features neurons, synapses, circuits into AI. In review, we described recent attempts build biologically plausible network following neuroscientifically similar strategies optimization or implanting outcome optimization, such as properties single computational units characteristics architecture. addition, proposed formalism relationship between set objectives that attempt achieve, classes categorized how closely their architectural resemble those BNN. This expected define potential roles top-down approaches offer map helping navigation gap AI engineering.

Язык: Английский

Процитировано

8

Performance evaluation of ferro-fluids flooding in enhanced oil recovery operations based on machine learning DOI
Hossein Saberi, Milad Karimian, Ehsan Esmaeilnezhad

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 132, С. 107908 - 107908

Опубликована: Янв. 23, 2024

Язык: Английский

Процитировано

2

Temporal heterogeneity in cognitive architectures DOI Creative Commons
Carlos Johnnatan Sandoval-Arrayga,

Gustavo Palacios-Ramirez,

Félix Ramos

и другие.

Cognitive Systems Research, Год журнала: 2024, Номер 88, С. 101265 - 101265

Опубликована: Июль 10, 2024

Язык: Английский

Процитировано

1

Histamine in the neocortex: Towards integrating multiscale effectors DOI Creative Commons
Amrita Benoy, Srikanth Ramaswamy

European Journal of Neuroscience, Год журнала: 2024, Номер 60(4), С. 4597 - 4623

Опубликована: Июль 20, 2024

Histamine is a modulatory neurotransmitter, which has received relatively less attention in the central nervous system than other neurotransmitters. The functional role of histamine neocortex, brain region that controls higher-order cognitive functions such as attention, learning and memory, remains largely unknown. This article focuses on emerging roles mechanisms release neocortex. We describe gaps current knowledge propose application interdisciplinary tools to dissect detailed multiscale logic histaminergic action neocortex ranging from sub-cellular, cellular, dendritic synaptic levels microcircuits mesoscale effects.

Язык: Английский

Процитировано

1

Effects of neuromodulation-inspired mechanisms on the performance of deep neural networks in a spatial learning task DOI Creative Commons
Jie Mei, Rouzbeh Meshkinnejad, Yalda Mohsenzadeh

и другие.

iScience, Год журнала: 2023, Номер 26(2), С. 106026 - 106026

Опубликована: Янв. 23, 2023

In recent years, the biological underpinnings of adaptive learning have been modeled, leading to faster model convergence and various behavioral benefits in tasks including spatial navigation cue-reward association. Furthermore, studies investigated how neuromodulatory system, a major driver synaptic plasticity state-dependent changes brain neuronal activities, plays role training deep neural networks (DNNs). this study, we extended previous on neuromodulation-inspired DNNs explored effects components single unit activities task. Under multiscale framework, plastic components, dropout probability modulation, rate decay were added unit, layer, whole network levels DNN models, respectively. We observed smaller error ambulation. then concluded that can affect trajectories, outcomes, component- hyperparameter-dependent manner.

Язык: Английский

Процитировано

3

Toward a Brain-Inspired Theory of Artificial Learning DOI
Jean‐Philippe Thivierge,

Éloïse Giraud,

Michael Lynn

и другие.

Cognitive Computation, Год журнала: 2023, Номер 16(5), С. 2374 - 2381

Опубликована: Янв. 31, 2023

Язык: Английский

Процитировано

3

Exploration-exploitation mechanisms in recurrent neural networks and human learners in restless bandit problems DOI Open Access
Deniz Tuzsus, A.M.A. Brands, Ioannis Pappas

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Апрель 27, 2023

Abstract A key feature of animal and human decision-making is to balance the exploration unknown options for information gain (directed exploration) versus selecting known immediate reward (exploitation), which often examined using restless bandit tasks. Recurrent neural network models (RNNs) have recently gained traction in both systems neuroscience work on reinforcement learning, due their ability show meta-learning task domains. Here we comprehensively compared performance a range RNN architectures as well learners four-armed problems. The best-performing architecture (LSTM with computation noise) exhibited human-level performance. Computational modeling behavior first revealed that behavioral data contain signatures higher-order perseveration, i.e., perseveration beyond last trial, but this effect was more pronounced RNNs. In contrast, learners, not RNNs, positive uncertainty choice probability exploration). hidden unit dynamics exploratory choices were associated disruption predictive signals during states low state value, resembling win-stay-loose-shift strategy, resonating previous single recording findings monkey prefrontal cortex. Our results highlight similarities differences between it emerges computational mechanisms identified cognitive work.

Язык: Английский

Процитировано

2