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Feng Z, Ji H, Daković M, Cui X, Zhu M, Stanković L. Cluster-CAM: Cluster-weighted visual interpretation of CNNs' decision in image classification. Neural Netw 2024; 178:106473. [PMID: 38941740 DOI: 10.1016/j.neunet.2024.106473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 06/13/2024] [Accepted: 06/16/2024] [Indexed: 06/30/2024]
Abstract
Despite the tremendous success of convolutional neural networks (CNNs) in computer vision, the mechanism of CNNs still lacks clear interpretation. Currently, class activation mapping (CAM), a famous visualization technique to interpret CNN's decision, has drawn increasing attention. Gradient-based CAMs are efficient, while the performance is heavily affected by gradient vanishing and exploding. In contrast, gradient-free CAMs can avoid computing gradients to produce more understandable results. However, they are quite time-consuming because hundreds of forward interference per image are required. In this paper, we proposed Cluster-CAM, an effective and efficient gradient-free CNN interpretation algorithm. Cluster-CAM can significantly reduce the times of forward propagation by splitting the feature maps into clusters. Furthermore, we propose an artful strategy to forge a cognition-base map and cognition-scissors from clustered feature maps. The final salience heatmap will be produced by merging the above cognition maps. Qualitative results conspicuously show that Cluster-CAM can produce heatmaps where the highlighted regions match the human's cognition more precisely than existing CAMs. The quantitative evaluation further demonstrates the superiority of Cluster-CAM in both effectiveness and efficiency.
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Affiliation(s)
- Zhenpeng Feng
- School of Electronic Engineering, Xidian University, Xi'an, China
| | - Hongbing Ji
- School of Electronic Engineering, Xidian University, Xi'an, China.
| | - Miloš Daković
- Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro
| | - Xiyang Cui
- School of Electronic Engineering, Xidian University, Xi'an, China
| | - Mingzhe Zhu
- School of Electronic Engineering, Xidian University, Xi'an, China; Kunshan Innovation Institute of Xidian University, School of Electronic Engineering, Xidian University, China
| | - Ljubiša Stanković
- Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro
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2
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Wang R, Chen ZS. Large-scale foundation models and generative AI for BigData neuroscience. Neurosci Res 2024:S0168-0102(24)00075-0. [PMID: 38897235 DOI: 10.1016/j.neures.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 04/15/2024] [Accepted: 05/15/2024] [Indexed: 06/21/2024]
Abstract
Recent advances in machine learning have led to revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-like intelligence thanks to BigData. With the help of self-supervised learning (SSL) and transfer learning, these models may potentially reshape the landscapes of neuroscience research and make a significant impact on the future. Here we present a mini-review on recent advances in foundation models and generative AI models as well as their applications in neuroscience, including natural language and speech, semantic memory, brain-machine interfaces (BMIs), and data augmentation. We argue that this paradigm-shift framework will open new avenues for many neuroscience research directions and discuss the accompanying challenges and opportunities.
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Affiliation(s)
- Ran Wang
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience and Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA.
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3
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Rolls ET. The memory systems of the human brain and generative artificial intelligence. Heliyon 2024; 10:e31965. [PMID: 38841455 PMCID: PMC11152951 DOI: 10.1016/j.heliyon.2024.e31965] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 05/11/2024] [Accepted: 05/24/2024] [Indexed: 06/07/2024] Open
Abstract
Generative Artificial Intelligence foundation models (for example Generative Pre-trained Transformer - GPT - models) can generate the next token given a sequence of tokens. How can this 'generative AI' be compared with the 'real' intelligence of the human brain, when for example a human generates a whole memory in response to an incomplete retrieval cue, and then generates further prospective thoughts? Here these two types of generative intelligence, artificial in machines and real in the human brain are compared, and it is shown how when whole memories are generated by hippocampal recall in response to an incomplete retrieval cue, what the human brain computes, and how it computes it, are very different from generative AI. Key differences are the use of local associative learning rules in the hippocampal memory system, and of non-local backpropagation of error learning in AI. Indeed, it is argued that the whole operation of the human brain is performed computationally very differently to what is implemented in generative AI. Moreover, it is emphasized that the primate including human hippocampal system includes computations about spatial view and where objects and people are in scenes, whereas in rodents the emphasis is on place cells and path integration by movements between places. This comparison with generative memory and processing in the human brain has interesting implications for the further development of generative AI and for neuroscience research.
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Affiliation(s)
- Edmund T. Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, 200403, China
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4
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Kausel L, Zamorano F, Billeke P, Sutherland ME, Alliende MI, Larrain‐Valenzuela J, Soto‐Icaza P, Aboitiz F. Theta and alpha oscillations may underlie improved attention and working memory in musically trained children. Brain Behav 2024; 14:e3517. [PMID: 38702896 PMCID: PMC11069029 DOI: 10.1002/brb3.3517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 04/10/2024] [Accepted: 04/13/2024] [Indexed: 05/06/2024] Open
Abstract
INTRODUCTION Attention and working memory are key cognitive functions that allow us to select and maintain information in our mind for a short time, being essential for our daily life and, in particular, for learning and academic performance. It has been shown that musical training can improve working memory performance, but it is still unclear if and how the neural mechanisms of working memory and particularly attention are implicated in this process. In this work, we aimed to identify the oscillatory signature of bimodal attention and working memory that contributes to improved working memory in musically trained children. MATERIALS AND METHODS We recruited children with and without musical training and asked them to complete a bimodal (auditory/visual) attention and working memory task, whereas their brain activity was measured using electroencephalography. Behavioral, time-frequency, and source reconstruction analyses were made. RESULTS Results showed that, overall, musically trained children performed better on the task than children without musical training. When comparing musically trained children with children without musical training, we found modulations in the alpha band pre-stimuli onset and the beginning of stimuli onset in the frontal and parietal regions. These correlated with correct responses to the attended modality. Moreover, during the end phase of stimuli presentation, we found modulations correlating with correct responses independent of attention condition in the theta and alpha bands, in the left frontal and right parietal regions. CONCLUSIONS These results suggest that musically trained children have improved neuronal mechanisms for both attention allocation and memory encoding. Our results can be important for developing interventions for people with attention and working memory difficulties.
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Affiliation(s)
- Leonie Kausel
- Centro de Estudios en Neurociencia Humana y Neuropsicología, Facultad de PsicologíaUniversidad Diego PortalesSantiagoChile
- Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (CICS), Facultad de GobiernoUniversidad del DesarrolloSantiagoChile
- Centro Interdisciplinario de NeurocienciasPontificia Universidad Católica de ChileSantiagoChile
| | - F. Zamorano
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de ImágenesClínica Alemanade SantiagoSantiagoChile
- Facultad de Ciencias para el Cuidado de la SaludUniversidad San SebastiánSantiagoChile
- Laboratorio de Psiquiatría TraslacionalDepartamento de PsiquiatríaFacultad de MedicinaUniversidad de ChileSantiagoChile
| | - P. Billeke
- Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (CICS), Facultad de GobiernoUniversidad del DesarrolloSantiagoChile
| | - M. E. Sutherland
- Centro Interdisciplinario de NeurocienciasPontificia Universidad Católica de ChileSantiagoChile
| | - M. I. Alliende
- Centro Interdisciplinario de NeurocienciasPontificia Universidad Católica de ChileSantiagoChile
| | - J. Larrain‐Valenzuela
- Centro de Investigación en Complejidad Social (CICS), Facultad de GobiernoUniversidad del DesarrolloSantiagoChile
| | - P. Soto‐Icaza
- Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (CICS), Facultad de GobiernoUniversidad del DesarrolloSantiagoChile
| | - F. Aboitiz
- Centro Interdisciplinario de NeurocienciasPontificia Universidad Católica de ChileSantiagoChile
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Mencattini A, Daprati E, Della-Morte D, Guadagni F, Sangiuolo F, Martinelli E. Assembloid learning: opportunities and challenges for personalized approaches to brain functioning in health and disease. Front Artif Intell 2024; 7:1385871. [PMID: 38708094 PMCID: PMC11066156 DOI: 10.3389/frai.2024.1385871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 04/08/2024] [Indexed: 05/07/2024] Open
Affiliation(s)
- Arianna Mencattini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
- Interdisciplinary Center of Advanced Study of Organ-on-Chip and Lab-on-Chip Applications (IC-LOC), University of Rome Tor Vergata, Rome, Italy
| | - Elena Daprati
- Department of System Medicine and Centro di Biomedicina Spaziale (CBMS), University of Rome Tor Vergata, Rome, Italy
| | - David Della-Morte
- Interdisciplinary Center of Advanced Study of Organ-on-Chip and Lab-on-Chip Applications (IC-LOC), University of Rome Tor Vergata, Rome, Italy
- San Raffaele Rome University, Rome, Italy
| | - Fiorella Guadagni
- San Raffaele Rome University, Rome, Italy
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele, Rome, Italy
| | - Federica Sangiuolo
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Eugenio Martinelli
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
- Interdisciplinary Center of Advanced Study of Organ-on-Chip and Lab-on-Chip Applications (IC-LOC), University of Rome Tor Vergata, Rome, Italy
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Gershman SJ. What have we learned about artificial intelligence from studying the brain? BIOLOGICAL CYBERNETICS 2024; 118:1-5. [PMID: 38337064 DOI: 10.1007/s00422-024-00983-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 01/11/2024] [Indexed: 02/12/2024]
Abstract
Neuroscience and artificial intelligence (AI) share a long, intertwined history. It has been argued that discoveries in neuroscience were (and continue to be) instrumental in driving the development of new AI technology. Scrutinizing these historical claims yields a more nuanced story, where AI researchers were loosely inspired by the brain, but ideas flowed mostly in the other direction.
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Affiliation(s)
- Samuel J Gershman
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, USA, Cambridge, USA.
- Center for Brains, Minds, and Machines,MIT, Cambridge, USA.
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7
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Li W, Li J, Chu C, Cao D, Shi W, Zhang Y, Jiang T. Common Sequential Organization of Face Processing in the Human Brain and Convolutional Neural Networks. Neuroscience 2024; 541:1-13. [PMID: 38266906 DOI: 10.1016/j.neuroscience.2024.01.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 01/26/2024]
Abstract
Face processing includes two crucial processing levels - face detection and face recognition. However, it remains unclear how human brains organize the two processing levels sequentially. While some studies found that faces are recognized as fast as they are detected, others have reported that faces are detected first, followed by recognition. We discriminated the two processing levels on a fine time scale by combining human intracranial EEG (two females, three males, and three subjects without reported sex information) and representation similarity analysis. Our results demonstrate that the human brain exhibits a "detection-first, recognition-later" pattern during face processing. In addition, we used convolutional neural networks to test the hypothesis that the sequential organization of the two face processing levels in the brain reflects computational optimization. Our findings showed that the networks trained on face recognition also exhibited the "detection-first, recognition-later" pattern. Moreover, this sequential organization mechanism developed gradually during the training of the networks and was observed only for correctly predicted images. These findings collectively support the computational account as to why the brain organizes them in this way.
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Affiliation(s)
- Wenlu Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jin Li
- School of Psychology, Capital Normal University, Beijing 100048, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Dan Cao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yu Zhang
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, Hunan Province, China.
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8
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Buchman AS. Untangling a taxonomy of living from the science of the continuum of life. Curr Opin Behav Sci 2024; 55:101345. [PMID: 38223539 PMCID: PMC10783655 DOI: 10.1016/j.cobeha.2023.101345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Medical innovation and technologic advances enrich daily living and occur within our normative worlds, that are socially constructed. These advances confront society with critical questions about the nature of human life, laying bare the inadequacies of extant norms and boundaries. Yet, society has been unable to develop consensus about when life ends. Scientific studies highlight that life is best characterized by continua without natural boundaries. Thus, scientific information alone cannot be employed to justify the socially constructed health categories required for setting norms and boundaries. An iterative process that integrates a broad range of non-scientific data with advancing scientific information is needed to facilitate consensus for updating social norms and boundaries. This can lead to a new taxonomy of living across the measurable continuum of life and align our normative worlds with the dizzying pace of medical innovation and advances in technologies transforming the world in which we live.
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Affiliation(s)
- Aron S Buchman
- Rush Alzheimer's Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois
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9
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Giansanti D. An Umbrella Review of the Fusion of fMRI and AI in Autism. Diagnostics (Basel) 2023; 13:3552. [PMID: 38066793 PMCID: PMC10706112 DOI: 10.3390/diagnostics13233552] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 11/22/2023] [Accepted: 11/25/2023] [Indexed: 04/05/2024] Open
Abstract
The role of functional magnetic resonance imaging (fMRI) is assuming an increasingly central role in autism diagnosis. The integration of Artificial Intelligence (AI) into the realm of applications further contributes to its development. This study's objective is to analyze emerging themes in this domain through an umbrella review, encompassing systematic reviews. The research methodology was based on a structured process for conducting a literature narrative review, using an umbrella review in PubMed and Scopus. Rigorous criteria, a standard checklist, and a qualification process were meticulously applied. The findings include 20 systematic reviews that underscore key themes in autism research, particularly emphasizing the significance of technological integration, including the pivotal roles of fMRI and AI. This study also highlights the enigmatic role of oxytocin. While acknowledging the immense potential in this field, the outcome does not evade acknowledging the significant challenges and limitations. Intriguingly, there is a growing emphasis on research and innovation in AI, whereas aspects related to the integration of healthcare processes, such as regulation, acceptance, informed consent, and data security, receive comparatively less attention. Additionally, the integration of these findings into Personalized Medicine (PM) represents a promising yet relatively unexplored area within autism research. This study concludes by encouraging scholars to focus on the critical themes of health domain integration, vital for the routine implementation of these applications.
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Affiliation(s)
- Daniele Giansanti
- Centro Nazionale TISP, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Roma, Italy
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10
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Rothschild AJ. Artificial Intelligence and the Journal of Clinical Psychopharmacology. J Clin Psychopharmacol 2023; 43:397-398. [PMID: 37683224 DOI: 10.1097/jcp.0000000000001752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Affiliation(s)
- Anthony J Rothschild
- From the Department of Psychiatry, University of Massachusetts Chan Medical School, UMass Memorial Health Care, Worcester, MA
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11
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Thölke P, Mantilla-Ramos YJ, Abdelhedi H, Maschke C, Dehgan A, Harel Y, Kemtur A, Mekki Berrada L, Sahraoui M, Young T, Bellemare Pépin A, El Khantour C, Landry M, Pascarella A, Hadid V, Combrisson E, O'Byrne J, Jerbi K. Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data. Neuroimage 2023:120253. [PMID: 37385392 DOI: 10.1016/j.neuroimage.2023.120253] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/05/2023] [Accepted: 06/26/2023] [Indexed: 07/01/2023] Open
Abstract
Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of ML requires a sound understanding of its subtleties and limitations. Training ML models on datasets with imbalanced classes is a particularly common problem, and it can have severe consequences if not adequately addressed. With the neuroscience ML user in mind, this paper provides a didactic assessment of the class imbalance problem and illustrates its impact through systematic manipulation of data imbalance ratios in (i) simulated data and (ii) brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). Our results illustrate how the widely-used Accuracy (Acc) metric, which measures the overall proportion of successful predictions, yields misleadingly high performances, as class imbalance increases. Because Acc weights the per-class ratios of correct predictions proportionally to class size, it largely disregards the performance on the minority class. A binary classification model that learns to systematically vote for the majority class will yield an artificially high decoding accuracy that directly reflects the imbalance between the two classes, rather than any genuine generalizable ability to discriminate between them. We show that other evaluation metrics such as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), and the less common Balanced Accuracy (BAcc) metric - defined as the arithmetic mean between sensitivity and specificity, provide more reliable performance evaluations for imbalanced data. Our findings also highlight the robustness of Random Forest (RF), and the benefits of using stratified cross-validation and hyperprameter optimization to tackle data imbalance. Critically, for neuroscience ML applications that seek to minimize overall classification error, we recommend the routine use of BAcc, which in the specific case of balanced data is equivalent to using standard Acc, and readily extends to multi-class settings. Importantly, we present a list of recommendations for dealing with imbalanced data, as well as open-source code to allow the neuroscience community to replicate and extend our observations and explore alternative approaches to coping with imbalanced data.
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Affiliation(s)
- Philipp Thölke
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada; Institute of Cognitive Science, Osnabrück University, Neuer Graben 29/Schloss, Osnabrück, 49074, Lower Saxony, Germany.
| | - Yorguin-Jose Mantilla-Ramos
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada; Neuropsychology and Behavior Group (GRUNECO), Faculty of Medicine, Universidad de Antioquia,53-108, Medellin, Aranjuez, Medellin, 050010, Colombia
| | - Hamza Abdelhedi
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada
| | - Charlotte Maschke
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada; Integrated Program in Neuroscience, McGill University, 1033 Pine Ave,Montreal, H3A 0G4, Canada
| | - Arthur Dehgan
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada; Institut de Neurosciences de la Timone (INT), CNRS, Aix Marseille University,Marseille, 13005, France
| | - Yann Harel
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada
| | - Anirudha Kemtur
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada
| | - Loubna Mekki Berrada
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada
| | - Myriam Sahraoui
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada
| | - Tammy Young
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada; Department of Computing Science, University of Alberta, 116 St & 85 Ave, Edmonton, T6G 2R3, AB, Canada
| | - Antoine Bellemare Pépin
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada; Department of Music, Concordia University, 1550 De Maisonneuve Blvd. W., Montreal, H3H 1G8, QC, Canada
| | - Clara El Khantour
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada
| | - Mathieu Landry
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada
| | - Annalisa Pascarella
- Institute for Applied Mathematics Mauro Picone, National Research Council, Roma, Italy, Roma, Italy
| | - Vanessa Hadid
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada
| | - Etienne Combrisson
- Institut de Neurosciences de la Timone (INT), CNRS, Aix Marseille University,Marseille, 13005, France
| | - Jordan O'Byrne
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada
| | - Karim Jerbi
- Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, H3T 1J4, Quebec, Canada; Mila (Quebec Machine Learning Institute),6666 Rue Saint-Urbain, Montreal, H2S 3H1, QC, Canada; UNIQUE Centre (Quebec Neuro-AI Research Centre), 3744 rue Jean-Brillant, Montreal,H3T 1P1,QC, Canada
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Liu XQ, Ji XY, Weng X, Zhang YF. Artificial intelligence ecosystem for computational psychiatry: Ideas to practice. World J Meta-Anal 2023; 11:79-91. [DOI: 10.13105/wjma.v11.i4.79] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/18/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms. One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable. This may help researchers develop more effective treatments and interventions for mental health problems. This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry. The artificial intelligence ecosystem for computational psychiatry includes data acquisition, preparation, modeling, application, and evaluation. This approach allows researchers to integrate data from a variety of sources, such as brain imaging, genetics, and behavioral experiments, to obtain a more complete understanding of mental health conditions. Through the process of data preprocessing, training, and testing, the data that are required for model building can be prepared. By using machine learning, neural networks, artificial intelligence, and other methods, researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors. Despite the continuous development and breakthrough of computational psychiatry, it has not yet influenced routine clinical practice and still faces many challenges, such as data availability and quality, biological risks, equity, and data protection. As we move progress in this field, it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.
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Affiliation(s)
- Xin-Qiao Liu
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xin-Yu Ji
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xing Weng
- Huzhou Educational Science & Research Center, Huzhou 313000, Zhejiang Province, China
| | - Yi-Fan Zhang
- School of Education, Tianjin University, Tianjin 300350, China
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13
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Zador A, Escola S, Richards B, Ölveczky B, Bengio Y, Boahen K, Botvinick M, Chklovskii D, Churchland A, Clopath C, DiCarlo J, Ganguli S, Hawkins J, Körding K, Koulakov A, LeCun Y, Lillicrap T, Marblestone A, Olshausen B, Pouget A, Savin C, Sejnowski T, Simoncelli E, Solla S, Sussillo D, Tolias AS, Tsao D. Catalyzing next-generation Artificial Intelligence through NeuroAI. Nat Commun 2023; 14:1597. [PMID: 36949048 PMCID: PMC10033876 DOI: 10.1038/s41467-023-37180-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 03/03/2023] [Indexed: 03/24/2023] Open
Abstract
Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities - inherited from over 500 million years of evolution - that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.
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Affiliation(s)
- Anthony Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.
| | - Sean Escola
- Department of Psychiatry, Columbia University, New York, NY, 10027, USA
| | - Blake Richards
- Mila, Montréal, QC, H2S 3H1, Canada
- School of Computer Science, McGill University, Montreal, Canada
- Montreal Neurological Institute, McGill University, Montreal, Canada
- Department of Neurology & Neurosurgery, McGill University, Montreal, Canada
- Learning in Machines and Brains Program, CIFAR, Toronto, Canada
| | - Bence Ölveczky
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
| | | | - Kwabena Boahen
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | | | | | - Anne Churchland
- Department of Neurobiology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, SW7 2BW, UK
| | - James DiCarlo
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA
| | - Surya Ganguli
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA
| | | | - Konrad Körding
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Alexei Koulakov
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Yann LeCun
- Meta, Menlo Park, CA, 94025, USA
- Department of Electrical and Computer Engineering, NYU, Brooklyn, NY, 11201, USA
| | | | | | - Bruno Olshausen
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, 94720, USA
| | - Alexandre Pouget
- Department of Basic Neurosciences, University of Geneva, Genève, 1211, Switzerland
| | - Cristina Savin
- Center for Neural Science, NYU, New York, NY, 10003, USA
| | | | - Eero Simoncelli
- Departments of Neural Science, Mathematics, and Psychology, NYU, New York, NY, 10003, USA
| | - Sara Solla
- Department of Physiology, Northwestern University, Chicago, IL, 60611, USA
| | - David Sussillo
- Meta, Menlo Park, CA, 94025, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Doris Tsao
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, 94720, USA
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14
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Surianarayanan C, Lawrence JJ, Chelliah PR, Prakash E, Hewage C. Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3062. [PMID: 36991773 PMCID: PMC10053494 DOI: 10.3390/s23063062] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.
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Affiliation(s)
| | | | | | - Edmond Prakash
- Research Center for Creative Arts, University for the Creative Arts (UCA), Farnham GU9 7DS, UK
| | - Chaminda Hewage
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
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15
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Naudin L. Different parameter solutions of a conductance-based model that behave identically are not necessarily degenerate. J Comput Neurosci 2023; 51:201-206. [PMID: 36905484 DOI: 10.1007/s10827-023-00848-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 02/13/2023] [Accepted: 02/22/2023] [Indexed: 03/12/2023]
Affiliation(s)
- Loïs Naudin
- Laboratoire Lorrain de Recherche en Informatique et ses Applications, CNRS, Université de Lorraine, Nancy, France. .,Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, F-75012, France.
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16
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Affiliation(s)
- Joseph Thornton
- Department of Psychiatry, University of Florida College of Medicine, Gainesville, FL, USA
| | - Russell D'Souza
- International Institute of Organizational Psychological Management, Melbourne, Australia
| | - Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI, USA.
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17
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Expression unleashed in artificial intelligence. Behav Brain Sci 2023; 46:e16. [PMID: 36799041 DOI: 10.1017/s0140525x22000814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
The problem of generating generally capable agents is an important frontier in artificial intelligence (AI) research. Such agents may demonstrate open-ended, versatile, and diverse modes of expression, similar to humans. We interpret the work of Heintz & Scott-Phillips as a minimal sufficient set of socio-cognitive biases for the emergence of generally expressive AI, separate yet complementary to existing algorithms.
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18
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Gopinath N. Artificial intelligence and neuroscience: An update on fascinating relationships. Process Biochem 2023. [DOI: 10.1016/j.procbio.2022.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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19
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Momennejad I. A rubric for human-like agents and NeuroAI. Philos Trans R Soc Lond B Biol Sci 2023; 378:20210446. [PMID: 36511409 PMCID: PMC9745874 DOI: 10.1098/rstb.2021.0446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 10/27/2022] [Indexed: 12/15/2022] Open
Abstract
Researchers across cognitive, neuro- and computer sciences increasingly reference 'human-like' artificial intelligence and 'neuroAI'. However, the scope and use of the terms are often inconsistent. Contributed research ranges widely from mimicking behaviour, to testing machine learning methods as neurally plausible hypotheses at the cellular or functional levels, or solving engineering problems. However, it cannot be assumed nor expected that progress on one of these three goals will automatically translate to progress in others. Here, a simple rubric is proposed to clarify the scope of individual contributions, grounded in their commitments to human-like behaviour, neural plausibility or benchmark/engineering/computer science goals. This is clarified using examples of weak and strong neuroAI and human-like agents, and discussing the generative, corroborate and corrective ways in which the three dimensions interact with one another. The author maintains that future progress in artificial intelligence will need strong interactions across the disciplines, with iterative feedback loops and meticulous validity tests-leading to both known and yet-unknown advances that may span decades to come. This article is part of a discussion meeting issue 'New approaches to 3D vision'.
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Affiliation(s)
- Ida Momennejad
- Microsoft Research NYC, Reinforcement Learning Station, 300 Lafayette, New York, NY 10012, USA
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20
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Makino H. Arithmetic value representation for hierarchical behavior composition. Nat Neurosci 2023; 26:140-149. [PMID: 36550292 PMCID: PMC9829535 DOI: 10.1038/s41593-022-01211-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 10/21/2022] [Indexed: 12/24/2022]
Abstract
The ability to compose new skills from a preacquired behavior repertoire is a hallmark of biological intelligence. Although artificial agents extract reusable skills from past experience and recombine them in a hierarchical manner, whether the brain similarly composes a novel behavior is largely unknown. In the present study, I show that deep reinforcement learning agents learn to solve a novel composite task by additively combining representations of prelearned action values of constituent subtasks. Learning efficacy in the composite task was further augmented by the introduction of stochasticity in behavior during pretraining. These theoretical predictions were empirically tested in mice, where subtask pretraining enhanced learning of the composite task. Cortex-wide, two-photon calcium imaging revealed analogous neural representations of combined action values, with improved learning when the behavior variability was amplified. Together, these results suggest that the brain composes a novel behavior with a simple arithmetic operation of preacquired action-value representations with stochastic policies.
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Affiliation(s)
- Hiroshi Makino
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
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21
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Anticevic A, Halassa MM. The thalamus in psychosis spectrum disorder. Front Neurosci 2023; 17:1163600. [PMID: 37123374 PMCID: PMC10133512 DOI: 10.3389/fnins.2023.1163600] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 03/21/2023] [Indexed: 05/02/2023] Open
Abstract
Psychosis spectrum disorder (PSD) affects 1% of the world population and results in a lifetime of chronic disability, causing devastating personal and economic consequences. Developing new treatments for PSD remains a challenge, particularly those that target its core cognitive deficits. A key barrier to progress is the tenuous link between the basic neurobiological understanding of PSD and its clinical phenomenology. In this perspective, we focus on a key opportunity that combines innovations in non-invasive human neuroimaging with basic insights into thalamic regulation of functional cortical connectivity. The thalamus is an evolutionary conserved region that forms forebrain-wide functional loops critical for the transmission of external inputs as well as the construction and update of internal models. We discuss our perspective across four lines of evidence: First, we articulate how PSD symptomatology may arise from a faulty network organization at the macroscopic circuit level with the thalamus playing a central coordinating role. Second, we discuss how recent animal work has mechanistically clarified the properties of thalamic circuits relevant to regulating cortical dynamics and cognitive function more generally. Third, we present human neuroimaging evidence in support of thalamic alterations in PSD, and propose that a similar "thalamocortical dysconnectivity" seen in pharmacological imaging (under ketamine, LSD and THC) in healthy individuals may link this circuit phenotype to the common set of symptoms in idiopathic and drug-induced psychosis. Lastly, we synthesize animal and human work, and lay out a translational path for biomarker and therapeutic development.
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Affiliation(s)
- Alan Anticevic
- School of Medicine, Yale University, New Haven, CT, United States
- *Correspondence: Alan Anticevic,
| | - Michael M. Halassa
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, United States
- Michael M. Halassa,
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22
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Bowers JS, Malhotra G, Dujmović M, Llera Montero M, Tsvetkov C, Biscione V, Puebla G, Adolfi F, Hummel JE, Heaton RF, Evans BD, Mitchell J, Blything R. Deep problems with neural network models of human vision. Behav Brain Sci 2022; 46:e385. [PMID: 36453586 DOI: 10.1017/s0140525x22002813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data.
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Affiliation(s)
- Jeffrey S Bowers
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Gaurav Malhotra
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Marin Dujmović
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Milton Llera Montero
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Christian Tsvetkov
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Valerio Biscione
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Guillermo Puebla
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Federico Adolfi
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany
| | - John E Hummel
- Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Rachel F Heaton
- Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Benjamin D Evans
- Department of Informatics, School of Engineering and Informatics, University of Sussex, Brighton, UK
| | - Jeffrey Mitchell
- Department of Informatics, School of Engineering and Informatics, University of Sussex, Brighton, UK
| | - Ryan Blything
- School of Psychology, Aston University, Birmingham, UK
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23
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Doya K, Friston K, Sugiyama M, Tenenbaum J. Neural Networks special issue on Artificial Intelligence and Brain Science. Neural Netw 2022; 155:328-329. [PMID: 36099665 DOI: 10.1016/j.neunet.2022.08.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Kenji Doya
- Okinawa Institute of Science and Technology Graduate University, Japan.
| | | | | | - Josh Tenenbaum
- Massachusetts Institute of Technology, United States of America
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24
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Hyper-flexible Convolutional Neural Networks based on Generalized Lehmer and Power Means. Neural Netw 2022; 155:177-203. [DOI: 10.1016/j.neunet.2022.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/07/2022] [Accepted: 08/15/2022] [Indexed: 11/24/2022]
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25
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Uncovering the Secrets of the Concept of Place in Cognitive Maps Aided by Artificial Intelligence. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10064-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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26
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Li Y, Zhang B, Pan X, Wang Y, Xu X, Wang R, Liu Z. Dopamine-Mediated Major Depressive Disorder in the Neural Circuit of Ventral Tegmental Area-Nucleus Accumbens-Medial Prefrontal Cortex: From Biological Evidence to Computational Models. Front Cell Neurosci 2022; 16:923039. [PMID: 35966208 PMCID: PMC9373714 DOI: 10.3389/fncel.2022.923039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 06/06/2022] [Indexed: 12/01/2022] Open
Abstract
Major depressive disorder (MDD) is a serious psychiatric disorder, with an increasing incidence in recent years. The abnormal dopaminergic pathways of the midbrain cortical and limbic system are the key pathological regions of MDD, particularly the ventral tegmental area- nucleus accumbens- medial prefrontal cortex (VTA-NAc-mPFC) neural circuit. MDD usually occurs with the dysfunction of dopaminergic neurons in VTA, which decreases the dopamine concentration and metabolic rate in NAc/mPFC brain regions. However, it has not been fully explained how abnormal dopamine concentration levels affect this neural circuit dynamically through the modulations of ion channels and synaptic activities. We used Hodgkin-Huxley and dynamical receptor binding model to establish this network, which can quantitatively explain neural activity patterns observed in MDD with different dopamine concentrations by changing the kinetics of some ion channels. The simulation replicated some important pathological patterns of MDD at the level of neurons and circuits with low dopamine concentration, such as the decreased action potential frequency in pyramidal neurons of mPFC with significantly reduced burst firing frequency. The calculation results also revealed that NaP and KS channels of mPFC pyramidal neurons played key roles in the functional regulation of this neural circuit. In addition, we analyzed the synaptic currents and local field potentials to explain the mechanism of MDD from the perspective of dysfunction of excitation-inhibition balance, especially the disinhibition effect in the network. The significance of this article is that we built the first computational model to illuminate the effect of dopamine concentrations for the NAc-mPFC-VTA circuit between MDD and normal groups, which can be used to quantitatively explain the results of existing physiological experiments, predict the results for unperformed experiments and screen possible drug targets.
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Affiliation(s)
- Yuanxi Li
- Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Bing Zhang
- Department of Anesthesiology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China
- Clinical and Translational Research Center, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiaochuan Pan
- Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China
| | - Yihong Wang
- Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China
| | - Xuying Xu
- Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China
| | - Rubin Wang
- Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China
- *Correspondence: Rubin Wang, ;
| | - Zhiqiang Liu
- Department of Anesthesiology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China
- Anesthesia and Brain Function Research Institute, Tongji University School of Medicine, Shanghai, China
- Zhiqiang Liu,
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27
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Lessons from infant learning for unsupervised machine learning. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00488-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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28
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Bolotta S, Dumas G. Social Neuro AI: Social Interaction as the “Dark Matter” of AI. FRONTIERS IN COMPUTER SCIENCE 2022. [DOI: 10.3389/fcomp.2022.846440] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
This article introduces a three-axis framework indicating how AI can be informed by biological examples of social learning mechanisms. We argue that the complex human cognitive architecture owes a large portion of its expressive power to its ability to engage in social and cultural learning. However, the field of AI has mostly embraced a solipsistic perspective on intelligence. We thus argue that social interactions not only are largely unexplored in this field but also are an essential element of advanced cognitive ability, and therefore constitute metaphorically the “dark matter” of AI. In the first section, we discuss how social learning plays a key role in the development of intelligence. We do so by discussing social and cultural learning theories and empirical findings from social neuroscience. Then, we discuss three lines of research that fall under the umbrella of Social NeuroAI and can contribute to developing socially intelligent embodied agents in complex environments. First, neuroscientific theories of cognitive architecture, such as the global workspace theory and the attention schema theory, can enhance biological plausibility and help us understand how we could bridge individual and social theories of intelligence. Second, intelligence occurs in time as opposed to over time, and this is naturally incorporated by dynamical systems. Third, embodiment has been demonstrated to provide more sophisticated array of communicative signals. To conclude, we discuss the example of active inference, which offers powerful insights for developing agents that possess biological realism, can self-organize in time, and are socially embodied.
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29
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Plessas A, Espinosa-Ramos JI, Parry D, Cowie S, Landon J. Machine learning with a snapshot of data: Spiking neural network 'predicts' reinforcement histories of pigeons' choice behavior. J Exp Anal Behav 2022; 117:301-319. [PMID: 35445745 PMCID: PMC9320819 DOI: 10.1002/jeab.759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 11/20/2022]
Abstract
An accumulated body of choice research has demonstrated that choice behavior can be understood within the context of its history of reinforcement by measuring response patterns. Traditionally, work on predicting choice behaviors has been based on the relationship between the history of reinforcement—the reinforcer arrangement used in training conditions—and choice behavior. We suggest an alternative method that treats the reinforcement history as unknown and focuses only on operant choices to accurately predict (more precisely, retrodict) reinforcement histories. We trained machine learning models known as artificial spiking neural networks (SNNs) on previously published pigeon datasets to detect patterns in choices with specific reinforcement histories—seven arranged concurrent variable‐interval schedules in effect for nine reinforcers. Notably, SNN extracted information from a small ‘window’ of observational data to predict reinforcer arrangements. The models' generalization ability was then tested with new choices of the same pigeons to predict the type of schedule used in training. We examined whether the amount of the data provided affected the prediction accuracy and our results demonstrated that choices made by the pigeons immediately after the delivery of reinforcers provided sufficient information for the model to determine the reinforcement history. These results support the idea that SNNs can process small sets of behavioral data for pattern detection, when the reinforcement history is unknown. This novel approach can influence our decisions to determine appropriate interventions; it can be a valuable addition to our toolbox, for both therapy design and research.
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Affiliation(s)
| | | | - Dave Parry
- Auckland University of Technology, New Zealand
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