Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

Sensors, Год журнала: 2023, Номер 23(6), С. 3062 - 3062

Опубликована: Март 13, 2023

Artificial intelligence (AI) is a field of computer science that deals with the simulation human using machines so such gain problem-solving and decision-making capabilities similar to brain. Neuroscience scientific study struczture cognitive functions AI are mutually interrelated. These two fields help each other in their advancements. The theory neuroscience has brought many distinct improvisations into field. biological neural network led realization complex deep architectures used develop versatile applications, as text processing, speech recognition, object detection, etc. Additionally, helps validate existing AI-based models. Reinforcement learning humans animals inspired scientists algorithms for reinforcement artificial systems, which enables those systems learn strategies without explicit instruction. Such building like robot-based surgery, autonomous vehicles, gaming In turn, its ability intelligently analyze data extract hidden patterns, fits perfect choice analyzing very complex. Large-scale simulations neuroscientists test hypotheses. Through an interface brain, system can brain signals commands generated according signals. fed devices, robotic arm, movement paralyzed muscles or parts. several use cases neuroimaging reducing workload radiologists. early detection diagnosis neurological disorders. same way, effectively be applied prediction Thus, this paper, scoping review been carried out on mutual relationship between neuroscience, emphasizing convergence order detect predict various

Язык: Английский

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Neuromorphic learning, working memory, and metaplasticity in nanowire networks

Science Advances, Год журнала: 2023, Номер 9(16)

Опубликована: Апрель 21, 2023

Nanowire networks (NWNs) mimic the brain's neurosynaptic connectivity and emergent dynamics. Consequently, NWNs may also emulate synaptic processes that enable higher-order cognitive functions such as learning memory. A quintessential task used to measure human working memory is n-back task. In this study, variations inspired by are implemented in a NWN device, external feedback applied brain-like supervised reinforcement learning. found retain information at least n = 7 steps back, remarkably similar originally proposed "seven plus or minus two" rule for subjects. Simulations elucidate how synapse-like junction plasticity depends on previous modifications, analogous "synaptic metaplasticity" brain, consolidated via strengthening pruning of conductance pathways.

Язык: Английский

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Emergent behaviour and neural dynamics in artificial agents tracking odour plumes

Nature Machine Intelligence, Год журнала: 2023, Номер 5(1), С. 58 - 70

Опубликована: Янв. 25, 2023

Tracking an odour plume to locate its source under variable wind and statistics is a complex task. Flying insects routinely accomplish such tracking, often over long distances, in pursuit of food or mates. Several aspects this remarkable behaviour underlying neural circuitry have been studied experimentally. Here we take complementary silico approach develop integrated understanding their computations. Specifically, train artificial recurrent network agents using deep reinforcement learning the simulated plumes that mimic features turbulent flow. Interestingly, agents' emergent behaviours resemble those flying insects, networks learn compute task-relevant variables with distinct dynamic structures population activity. Our analyses put forward testable behavioural hypothesis for tracking changing direction, provide key intuitions memory requirements dynamics tracking.

Язык: Английский

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Conflicting evidence fusion using a correlation coefficient-based approach in complex network

Chaos Solitons & Fractals, Год журнала: 2023, Номер 176, С. 114087 - 114087

Опубликована: Сен. 29, 2023

Язык: Английский

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Connectome-based reservoir computing with the conn2res toolbox

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Янв. 22, 2024

Abstract The connection patterns of neural circuits form a complex network. How signaling in these manifests as cognition and adaptive behaviour remains the central question neuroscience. Concomitant advances connectomics artificial intelligence open fundamentally new opportunities to understand how shape computational capacity biological brain networks. Reservoir computing is versatile paradigm that uses high-dimensional, nonlinear dynamical systems perform computations approximate cognitive functions. Here we present : an open-source Python toolbox for implementing networks modular, allowing arbitrary network architecture dynamics be imposed. allows researchers input connectomes reconstructed using multiple techniques, from tract tracing noninvasive diffusion imaging, impose systems, spiking neurons memristive dynamics. versatility us ask questions at confluence neuroscience intelligence. By reconceptualizing function computation, sets stage more mechanistic understanding structure-function relationships

Язык: Английский

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Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond

BioMedInformatics, Год журнала: 2024, Номер 4(2), С. 1441 - 1456

Опубликована: Июнь 6, 2024

This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research development. Through various instances examples, we illuminate how algorithms, capable simulating vast chemical spaces predicting molecular properties, are increasingly integrated with biological systems expedite discovery. By harnessing power computational models machine learning, researchers can design novel compounds tailored specific targets, optimize candidates, simulate behavior virtual environments. paradigm shift offers unprecedented opportunities for accelerating development, reducing costs, and, ultimately, improving patient outcomes. As navigate this rapidly evolving landscape, collaboration between interdisciplinary teams continued innovation will be paramount in realizing promise advancing

Язык: Английский

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Artificial intelligence models for refrigeration, air conditioning and heat pump systems

Energy Reports, Год журнала: 2022, Номер 8, С. 8451 - 8466

Опубликована: Июль 1, 2022

Artificial intelligence (AI) models for refrigeration, heat pumps, and air conditioners have emerged in recent decades. The universal approximation accuracy prediction performances of various AI structures like feedforward neural networks, radial basis function adaptive neuro-fuzzy inference recurrent networks are encouraging interest. This review discusses existing topographies network RHVAC system modelling, energy fault(s), detection diagnosis. Studies show that require standardization improvement tuning hyperparameters (like weight, bias, activation functions, number hidden layers neurons). selection validation, learning algorithms depends on author's suitability a particular application. Backpropagation, error trial the layer, layers' neurons, Levenberg–Marquardt algorithms, remain prevalent methodologies developing structures. major limitations to application systems include exploding or/and vanishing gradients, interpretability, trade off, training saturation limited sensitivity. aims give up-to-date applications different architectures identify associated prospects.

Язык: Английский

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Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

Nature Machine Intelligence, Год журнала: 2023, Номер 5(12), С. 1369 - 1381

Опубликована: Ноя. 20, 2023

Abstract Brain networks exist within the confines of resource limitations. As a result, brain network must overcome metabolic costs growing and sustaining its physical space, while simultaneously implementing required information processing. Here, to observe effect these processes, we introduce spatially embedded recurrent neural (seRNN). seRNNs learn basic task-related inferences existing three-dimensional Euclidean where communication constituent neurons is constrained by sparse connectome. We find that converge on structural functional features are also commonly found in primate cerebral cortices. Specifically, they solving using modular small-world networks, which functionally similar units configure themselves utilize an energetically efficient mixed-selective code. Because emerge unison, reveal how many common motifs strongly intertwined can be attributed biological optimization processes. incorporate biophysical constraints fully artificial system serve as bridge between research communities move neuroscientific understanding forwards.

Язык: Английский

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Machine learning in neuroimaging: from research to clinical practice

Deleted Journal, Год журнала: 2022, Номер 62(S1), С. 1 - 10

Опубликована: Авг. 31, 2022

Abstract Neuroimaging is critical in clinical care and research, enabling us to investigate the brain health disease. There a complex link between brain’s morphological structure, physiological architecture, corresponding imaging characteristics. The shape, function, relationships various areas change during development throughout life, disease, recovery. Like few other areas, neuroimaging benefits from advanced analysis techniques fully exploit data for studying its function. Recently, machine learning has started contribute (a) anatomical measurements, detection, segmentation, quantification of lesions disease patterns, (b) rapid identification acute conditions such as stroke, or (c) tracking changes over time. As our ability image analyze advances, so does understanding intricate their role therapeutic decision-making. Here, we review current state art using providing an overview applications contribution fundamental computational neuroscience.

Язык: Английский

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Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain

PLoS Computational Biology, Год журнала: 2022, Номер 18(6), С. e1010250 - e1010250

Опубликована: Июнь 17, 2022

Lesion inference analysis is a fundamental approach for characterizing the causal contributions of neural elements to brain function. This has gained new prominence through arrival modern perturbation techniques with unprecedented levels spatiotemporal precision. While inferences drawn from perturbations are conceptually powerful, they face methodological difficulties. Particularly, challenged disentangle true involved elements, since often functions arise coalitions distributed, interacting and localized have unknown global consequences. To elucidate these limitations, we systematically exhaustively lesioned small artificial network (ANN) playing classic arcade game. We determined functional all nodes links, contrasting results sequential single-element simultaneous multiple elements. found that lesioning individual one at time, produced biased results. By contrast, multi-site lesion captured crucial details were missed by single-site lesions. conclude even seemingly simple ANNs show surprising complexity needs be addressed multi-lesioning coherent characterization.

Язык: Английский

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