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Vasudevan V, Murthy A, Padhi R. Modeling kinematic variability reveals displacement and velocity based dual control of saccadic eye movements. Exp Brain Res 2024; 242:2159-2176. [PMID: 38980340 DOI: 10.1007/s00221-024-06870-3] [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: 12/14/2023] [Accepted: 06/01/2024] [Indexed: 07/10/2024]
Abstract
Noise is a ubiquitous component of motor systems that leads to behavioral variability of all types of movements. Nonetheless, systems-based models investigating human movements are generally deterministic and explain only the central tendencies like mean trajectories. In this paper, a novel approach to modeling kinematic variability of movements is presented and tested on the oculomotor system. This approach reconciles the two prominent philosophies of saccade control: displacement-based control versus velocity-based control. This was achieved by quantifying the variability in saccadic eye movements and developing a stochastic model of its control. The proposed stochastic dual model generated significantly better fits of inter-trial variances of the saccade trajectories compared to existing models. These results suggest that the saccadic system can flexibly use the information of both desired displacement and velocity for its control. This study presents a potential framework for investigating computational principles of motor control in the presence of noise utilizing stochastic modeling of kinematic variability.
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Affiliation(s)
- Varsha Vasudevan
- Department of Bioengineering, Indian Institute of Science, Bangalore, 560012, India.
| | - Aditya Murthy
- Department of Bioengineering, Indian Institute of Science, Bangalore, 560012, India
- Centre for Neuroscience, Indian Institute of Science, Bangalore, 560012, India
| | - Radhakant Padhi
- Department of Bioengineering, Indian Institute of Science, Bangalore, 560012, India
- Department of Aerospace Engineering, Indian Institute of Science, Bangalore, 560012, India
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2
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Barjuan L, Soriano J, Serrano MÁ. Optimal navigability of weighted human brain connectomes in physical space. Neuroimage 2024; 297:120703. [PMID: 38936648 DOI: 10.1016/j.neuroimage.2024.120703] [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: 04/11/2024] [Revised: 06/17/2024] [Accepted: 06/21/2024] [Indexed: 06/29/2024] Open
Abstract
Communication protocols in the brain connectome describe how to transfer information from one region to another. Typically, these protocols hinge on either the spatial distances between brain regions or the intensity of their connections. Yet, none of them combine both factors to achieve optimal efficiency. Here, we introduce a continuous spectrum of decentralized routing strategies that integrates link weights and the spatial embedding of connectomes to route signal transmission. We implemented the protocols on connectomes from individuals in two cohorts and on group-representative connectomes designed to capture weighted connectivity properties. We identified an intermediate domain of routing strategies, a sweet spot, where navigation achieves maximum communication efficiency at low transmission cost. This phenomenon is robust and independent of the particular configuration of weights. Our findings suggest an interplay between the intensity of neural connections and their topology and geometry that amplifies communicability, where weights play the role of noise in a stochastic resonance phenomenon. Such enhancement may support more effective responses to external and internal stimuli, underscoring the intricate diversity of brain functions.
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Affiliation(s)
- Laia Barjuan
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain; Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Martí i Franquès 1, E-08028, Barcelona, Spain
| | - Jordi Soriano
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain; Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Martí i Franquès 1, E-08028, Barcelona, Spain
| | - M Ángeles Serrano
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain; Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Martí i Franquès 1, E-08028, Barcelona, Spain; ICREA, Pg. Lluís Companys 23, E-08010 Barcelona, Spain.
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Li Y, Zhu X, Qi Y, Wang Y. Revealing unexpected complex encoding but simple decoding mechanisms in motor cortex via separating behaviorally relevant neural signals. eLife 2024; 12:RP87881. [PMID: 39120996 PMCID: PMC11315449 DOI: 10.7554/elife.87881] [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: 08/11/2024] Open
Abstract
In motor cortex, behaviorally relevant neural responses are entangled with irrelevant signals, which complicates the study of encoding and decoding mechanisms. It remains unclear whether behaviorally irrelevant signals could conceal some critical truth. One solution is to accurately separate behaviorally relevant and irrelevant signals at both single-neuron and single-trial levels, but this approach remains elusive due to the unknown ground truth of behaviorally relevant signals. Therefore, we propose a framework to define, extract, and validate behaviorally relevant signals. Analyzing separated signals in three monkeys performing different reaching tasks, we found neural responses previously considered to contain little information actually encode rich behavioral information in complex nonlinear ways. These responses are critical for neuronal redundancy and reveal movement behaviors occupy a higher-dimensional neural space than previously expected. Surprisingly, when incorporating often-ignored neural dimensions, behaviorally relevant signals can be decoded linearly with comparable performance to nonlinear decoding, suggesting linear readout may be performed in motor cortex. Our findings prompt that separating behaviorally relevant signals may help uncover more hidden cortical mechanisms.
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Affiliation(s)
- Yangang Li
- Qiushi Academy for Advanced Studies, Zhejiang UniversityHangzhouChina
- Nanhu Brain-Computer Interface InstituteHangzhouChina
- College of Computer Science and Technology, Zhejiang UniversityHangzhouChina
- The State Key Lab of Brain-Machine Intelligence, Zhejiang UniversityHangzhouChina
| | - Xinyun Zhu
- Qiushi Academy for Advanced Studies, Zhejiang UniversityHangzhouChina
- Nanhu Brain-Computer Interface InstituteHangzhouChina
- College of Computer Science and Technology, Zhejiang UniversityHangzhouChina
- The State Key Lab of Brain-Machine Intelligence, Zhejiang UniversityHangzhouChina
| | - Yu Qi
- Nanhu Brain-Computer Interface InstituteHangzhouChina
- College of Computer Science and Technology, Zhejiang UniversityHangzhouChina
- The State Key Lab of Brain-Machine Intelligence, Zhejiang UniversityHangzhouChina
- Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital and the MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of MedicineHangzhouChina
| | - Yueming Wang
- Qiushi Academy for Advanced Studies, Zhejiang UniversityHangzhouChina
- Nanhu Brain-Computer Interface InstituteHangzhouChina
- College of Computer Science and Technology, Zhejiang UniversityHangzhouChina
- The State Key Lab of Brain-Machine Intelligence, Zhejiang UniversityHangzhouChina
- Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital and the MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of MedicineHangzhouChina
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Gao C, Huang H, Zhan J, Li W, Li Y, Li J, Zhou J, Wang Y, Jiang Z, Chen W, Zhu Y, Zhuo Y, Wu K. Adaptive Changes in Neurovascular Properties With Binocular Accommodation Functions in Myopic Participants by 3D Visual Training: An EEG and fNIRS Study. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2749-2758. [PMID: 39074027 DOI: 10.1109/tnsre.2024.3434492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
Although three-dimensional visual training (3DVT) has been used for myopia intervention, its neural mechanisms remain largely unknown. In this study, visual function was examined before and after 3DVT, while resting-state EEG-fNIRS signals were recorded from 38 myopic participants. A graph theoretical analysis was applied to compute the neurovascular properties, including static brain networks (SBNs), dynamic brain networks (DBNs), and dynamic neurovascular coupling (DNC). Correlations between the changes in neurovascular properties and the changes in visual functions were calculated. After 3DVT, the local efficiency and node efficiency in the frontal lobes increased in the SBNs constructed from EEG δ -band; the global efficiency and node efficiency in the frontal-parietal lobes decreased in the DBNs variability constructed from EEG δ -band. For the DNC constructed with EEG α -band and oxyhemoglobin (HbO), the local efficiency decreased, for EEG α -band and deoxyhemoglobin (HbR), the node efficiency in the frontal-occipital lobes decreased. For the SBNs constructed from HbO, the functional connectivity (FC) between the frontal-occipital lobes increased. The DNC constructed between the FC of the frontal-parietal lobes from EEG β -band and the FC of the frontal-occipital lobes from HbO increased, and between the FC of the frontal-occipital lobes from EEG β -band and the FC of the inter-frontal lobes from HbR increased. The neurovascular properties were significantly correlated with the amplitude of accommodation and accommodative facility. The result indicated the positive effects of 3DVT on myopic participants, including improved efficiency of brain networks, increased FC of SBNs and DNC, and enhanced binocular accommodation functions.
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Seo S, Bharmauria V, Schütz A, Yan X, Wang H, Crawford JD. Multiunit Frontal Eye Field Activity Codes the Visuomotor Transformation, But Not Gaze Prediction or Retrospective Target Memory, in a Delayed Saccade Task. eNeuro 2024; 11:ENEURO.0413-23.2024. [PMID: 39054056 DOI: 10.1523/eneuro.0413-23.2024] [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: 10/13/2023] [Revised: 07/16/2024] [Accepted: 07/18/2024] [Indexed: 07/27/2024] Open
Abstract
Single-unit (SU) activity-action potentials isolated from one neuron-has traditionally been employed to relate neuronal activity to behavior. However, recent investigations have shown that multiunit (MU) activity-ensemble neural activity recorded within the vicinity of one microelectrode-may also contain accurate estimations of task-related neural population dynamics. Here, using an established model-fitting approach, we compared the spatial codes of SU response fields with corresponding MU response fields recorded from the frontal eye fields (FEFs) in head-unrestrained monkeys (Macaca mulatta) during a memory-guided saccade task. Overall, both SU and MU populations showed a simple visuomotor transformation: the visual response coded target-in-eye coordinates, transitioning progressively during the delay toward a future gaze-in-eye code in the saccade motor response. However, the SU population showed additional secondary codes, including a predictive gaze code in the visual response and retention of a target code in the motor response. Further, when SUs were separated into regular/fast spiking neurons, these cell types showed different spatial code progressions during the late delay period, only converging toward gaze coding during the final saccade motor response. Finally, reconstructing MU populations (by summing SU data within the same sites) failed to replicate either the SU or MU pattern. These results confirm the theoretical and practical potential of MU activity recordings as a biomarker for fundamental sensorimotor transformations (e.g., target-to-gaze coding in the oculomotor system), while also highlighting the importance of SU activity for coding more subtle (e.g., predictive/memory) aspects of sensorimotor behavior.
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Affiliation(s)
- Serah Seo
- Centre for Vision Research and Centre for Integrative and Applied Neuroscience, York University, Toronto, Ontario M3J 1P3, Canada
| | - Vishal Bharmauria
- Centre for Vision Research and Centre for Integrative and Applied Neuroscience, York University, Toronto, Ontario M3J 1P3, Canada
- Department of Neurosurgery and Brain Repair, Morsani College of Medicine, University of South Florida, Tampa, Florida 33606
| | - Adrian Schütz
- Department of Neurophysics, Philipps-Universität Marburg, 35032 Marburg, Germany
- Center for Mind, Brain, and Behavior - CMBB, Philipps-Universität Marburg, 35032 Marburg, and Justus-Liebig-Universität Giessen, Giessen, Germany
| | - Xiaogang Yan
- Centre for Vision Research and Centre for Integrative and Applied Neuroscience, York University, Toronto, Ontario M3J 1P3, Canada
| | - Hongying Wang
- Centre for Vision Research and Centre for Integrative and Applied Neuroscience, York University, Toronto, Ontario M3J 1P3, Canada
| | - J Douglas Crawford
- Centre for Vision Research and Centre for Integrative and Applied Neuroscience, York University, Toronto, Ontario M3J 1P3, Canada
- Departments of Psychology, Biology, Kinesiology & Health Sciences, York University, Toronto, Ontario M3J 1P3, Canada
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Nigg JT, Bruton A, Kozlowski MB, Johnstone JM, Karalunas SL. Systematic Review and Meta-Analysis: Do White Noise or Pink Noise Help With Task Performance in Youth With Attention-Deficit/Hyperactivity Disorder or With Elevated Attention Problems? J Am Acad Child Adolesc Psychiatry 2024; 63:778-788. [PMID: 38428577 PMCID: PMC11283987 DOI: 10.1016/j.jaac.2023.12.014] [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: 06/01/2023] [Revised: 11/22/2023] [Accepted: 02/21/2024] [Indexed: 03/03/2024]
Abstract
OBJECTIVE Public interest in the potential benefits of white, pink, and brown noise for attention-deficit/hyperactivity disorder (ADHD) has recently mushroomed. White noise contains all frequencies of noise and sounds like static; pink or brown noise has more power in the lower frequencies and may sound, respectively, like rain or a waterfall. This meta-analysis evaluated effects on laboratory tasks in individuals with ADHD or elevated ADHD symptoms. METHOD Eligible studies reported on participants with diagnosis of ADHD or elevated symptoms of ADHD who were assessed in a randomized trial using laboratory tasks intended to measure aspects of attention or academic work involving attention or executive function while exposed to white, pink, and brown noise and compared with a low/no noise condition. Two authors independently reviewed and screened studies for eligibility. A random-effects meta-analysis was conducted with preplanned moderator analyses of age, diagnostic status, and task type. Publication bias was evaluated. The GRADE tool was used to assess certainty of the evidence. Sensitivity analyses were conducted to evaluate robustness. RESULTS Studies of children and college-age young adults with ADHD or ADHD symptoms (k = 13, N = 335) yielded a small but statistically significant benefit of white and pink noise on task performance (g = 0.249, 95% CI [0.135, 0.363], p < .0001). No studies of brown noise were identified. Heterogeneity was minimal, and moderators were nonsignificant; results survived sensitivity tests, and no publication bias was identified. In non-ADHD comparison groups (k = 11, N = 335), white and pink noise had a negative effect (g = -0.212, 95% CI [-0.355, -0.069], p = .0036). CONCLUSION White and pink noise provide a small benefit on laboratory attention tasks for individuals with ADHD or high ADHD symptoms, but not for non-ADHD individuals. This article addresses theoretical implications, cautions, risks, and limitations. PLAIN LANGUAGE SUMMARY Public interest in the potential benefits of white, pink, and brown noise exposure for enhancing task performance for individuals with attention-deficit/hyperactivity disorder (ADHD) has increased substantially. This systematic review and meta-analysis included 13 studies with 335 participants and found that white/pink noise improved cognitive performance for children and young adults with ADHD or significant ADHD symptoms. In contrast, white/pink noise impaired cognitive performance for individuals without ADHD. Positive effects of noise were small, but these results point to a possible low-cost, low-risk intervention that may benefit youth with ADHD. Additional studies are needed to confirm effects and identify safe and appropriate decibel levels. The potential detrimental effects for individuals without ADHD also requires further study. STUDY PREREGISTRATION INFORMATION White Noise for ADHD: A Systematic Review And Meta-analysis; https://www.crd.york.ac.uk/prospero; CRD42023393992.
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Affiliation(s)
- Joel T Nigg
- Oregon Health & Science University, Portland, Oregon.
| | - Alisha Bruton
- Oregon Health & Science University, Portland, Oregon
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Kaufmann P, Koller W, Wallnöfer E, Goncalves B, Baca A, Kainz H. Increased trial-to-trial similarity and reduced temporal overlap of muscle synergy activation coefficients manifest during learning and with increasing movement proficiency. Sci Rep 2024; 14:17638. [PMID: 39085397 PMCID: PMC11291506 DOI: 10.1038/s41598-024-68515-3] [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: 11/08/2023] [Accepted: 07/23/2024] [Indexed: 08/02/2024] Open
Abstract
Muscle synergy analyses are used to enhance our understanding of motor control. Spatially fixed synergy weights coordinate multiple co-active muscles through activation commands, known as activation coefficients. To gain a more comprehensive understanding of motor learning, it is essential to understand how activation coefficients vary during a learning task and at different levels of movement proficiency. Participants walked on a line, a beam, and learned to walk on a tightrope-tasks that represent different levels of proficiency. Muscle synergies were extracted from electromyography signals across all conditions and the number of synergies was determined by the knee-point of the total variance accounted for (tVAF) curve. The results indicated that the tVAF of one synergy decreased with task proficiency, with the tightrope task resulting in the highest tVAF compared to the line and beam tasks. Furthermore, with increasing proficiency and after a learning process, trial-to-trial similarity increased and temporal overlap of synergy activation coefficients decreased. Consequently, we propose that precise adjustment and refinement of synergy activation coefficients play a pivotal role in motor learning.
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Affiliation(s)
- Paul Kaufmann
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a (USZ ||), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a, 1150, Vienna, Austria
| | - Willi Koller
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a (USZ ||), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a, 1150, Vienna, Austria
| | - Elias Wallnöfer
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a (USZ ||), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a, 1150, Vienna, Austria
| | - Basilio Goncalves
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a (USZ ||), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a, 1150, Vienna, Austria
| | - Arnold Baca
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a (USZ ||), 1150, Vienna, Austria
| | - Hans Kainz
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a (USZ ||), 1150, Vienna, Austria.
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a, 1150, Vienna, Austria.
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Coolen IEJI, van Langen J, Hofman S, van Aagten FE, Schaaf JV, Michel L, Aristodemou M, Judd N, van Hout ATB, Meeussen E, Kievit RA. Protocol and preregistration for the CODEC project: measuring, modelling and mechanistically understanding the nature of cognitive variability in early childhood. BMC Psychol 2024; 12:407. [PMID: 39060934 PMCID: PMC11282758 DOI: 10.1186/s40359-024-01904-5] [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] [Received: 07/03/2024] [Accepted: 07/13/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND Children's cognitive performance fluctuates across multiple timescales. However, fluctuations have often been neglected in favour of research into average cognitive performance, limiting the unique insights into cognitive abilities and development that cognitive variability may afford. Preliminary evidence suggests that greater variability is associated with increased symptoms of neurodevelopmental disorders, and differences in behavioural and neural functioning. The relative dearth of empirical work on variability, historically limited due to a lack of suitable data and quantitative methodology, has left crucial questions unanswered, which the CODEC (COgnitive Dynamics in Early Childhood) study aims to address. METHOD The CODEC cohort is an accelerated 3-year longitudinal study which encompasses 600 7-to-10-year-old children. Each year includes a 'burst' week (3 times per day, 5 days per week) of cognitive measurements on five cognitive domains (reasoning, working memory, processing speed, vocabulary, exploration), conducted both in classrooms and at home through experience sampling assessments. We also measure academic outcomes and external factors hypothesised to predict cognitive variability, including sleep, mood, motivation and background noise. A subset of 200 children (CODEC-MRI) are invited for two deep phenotyping sessions (in year 1 and year 3 of the study), including structural and functional magnetic resonance imaging, eye-tracking, parental measurements and questionnaire-based demographic and psychosocial measures. We will quantify developmental differences and changes in variability using Dynamic Structural Equation Modelling, allowing us to simultaneously capture variability and the multilevel structure of trials nested in sessions, days, children and classrooms. DISCUSSION CODEC's unique design allows us to measure variability across a range of different cognitive domains, ages, and temporal resolutions. The deep-phenotyping arm allows us to test hypotheses concerning variability, including the role of mind wandering, strategy exploration, mood, sleep, and brain structure. Due to CODEC's longitudinal nature, we are able to quantify which measures of variability at baseline predict long-term outcomes. In summary, the CODEC study is a unique longitudinal study combining experience sampling, an accelerated longitudinal 'burst' design, deep phenotyping, and cutting-edge statistical methodologies to better understand the nature, causes, and consequences of cognitive variability in children. TRIAL REGISTRATION ClinicalTrials.gov - NCT06330090.
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Affiliation(s)
- Ilse E J I Coolen
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Trigon Building, Kapittelweg 29, Nijmegen, 6525 EN, the Netherlands
| | - Jordy van Langen
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Trigon Building, Kapittelweg 29, Nijmegen, 6525 EN, the Netherlands
| | - Sophie Hofman
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Trigon Building, Kapittelweg 29, Nijmegen, 6525 EN, the Netherlands
| | - Fréderique E van Aagten
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Trigon Building, Kapittelweg 29, Nijmegen, 6525 EN, the Netherlands
| | - Jessica V Schaaf
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Trigon Building, Kapittelweg 29, Nijmegen, 6525 EN, the Netherlands
| | - Lea Michel
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Trigon Building, Kapittelweg 29, Nijmegen, 6525 EN, the Netherlands
| | - Michael Aristodemou
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Trigon Building, Kapittelweg 29, Nijmegen, 6525 EN, the Netherlands
| | - Nicholas Judd
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Trigon Building, Kapittelweg 29, Nijmegen, 6525 EN, the Netherlands
| | - Aran T B van Hout
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Trigon Building, Kapittelweg 29, Nijmegen, 6525 EN, the Netherlands
| | - Emma Meeussen
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Trigon Building, Kapittelweg 29, Nijmegen, 6525 EN, the Netherlands
| | - Rogier A Kievit
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Trigon Building, Kapittelweg 29, Nijmegen, 6525 EN, the Netherlands.
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Yan C, Mercaldo V, Jacob AD, Kramer E, Mocle A, Ramsaran AI, Tran L, Rashid AJ, Park S, Insel N, Redish AD, Frankland PW, Josselyn SA. Higher-order interactions between hippocampal CA1 neurons are disrupted in amnestic mice. Nat Neurosci 2024:10.1038/s41593-024-01713-4. [PMID: 39030342 DOI: 10.1038/s41593-024-01713-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 06/18/2024] [Indexed: 07/21/2024]
Abstract
Across systems, higher-order interactions between components govern emergent dynamics. Here we tested whether contextual threat memory retrieval in mice relies on higher-order interactions between dorsal CA1 hippocampal neurons requiring learning-induced dendritic spine plasticity. We compared population-level Ca2+ transients as wild-type mice (with intact learning-induced spine plasticity and memory) and amnestic mice (TgCRND8 mice with high levels of amyloid-β and deficits in learning-induced spine plasticity and memory) were tested for memory. Using machine-learning classifiers with different capacities to use input data with complex interactions, our findings indicate complex neuronal interactions in the memory representation of wild-type, but not amnestic, mice. Moreover, a peptide that partially restored learning-induced spine plasticity also restored the statistical complexity of the memory representation and memory behavior in Tg mice. These findings provide a previously missing bridge between levels of analysis in memory research, linking receptors, spines, higher-order neuronal dynamics and behavior.
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Affiliation(s)
- Chen Yan
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- DeepMind, London, UK
| | - Valentina Mercaldo
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland
| | - Alexander D Jacob
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Dept. of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Emily Kramer
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Andrew Mocle
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Dept. of Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Adam I Ramsaran
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Dept. of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Lina Tran
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Asim J Rashid
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Sungmo Park
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Nathan Insel
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Dept. of Psychology, University of Montana, Missoula, MT, USA
- Department of Psychology, Wilfrid Laurier University, Waterloo, Ontario, Canada
| | - A David Redish
- Dept. of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Paul W Frankland
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- Dept. of Psychology, University of Toronto, Toronto, Ontario, Canada
- Dept. of Physiology, University of Toronto, Toronto, Ontario, Canada
- Child & Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario, Canada
| | - Sheena A Josselyn
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada.
- Dept. of Psychology, University of Toronto, Toronto, Ontario, Canada.
- Dept. of Physiology, University of Toronto, Toronto, Ontario, Canada.
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Yano S, Nakamura A, Suzuki Y, Smith CE, Nomura T. Smartphone usage during walking decreases the positive persistency in gait cycle variability. Sci Rep 2024; 14:16410. [PMID: 39013927 PMCID: PMC11252135 DOI: 10.1038/s41598-024-66727-1] [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] [Received: 01/11/2024] [Accepted: 07/03/2024] [Indexed: 07/18/2024] Open
Abstract
Gait cycle variability during steady walking, described by the stride interval time series, has been used as a gait-stability-related measure. In particular, the positive persistency in the stride intervals with 1/f-like fluctuation and reduction of the persistency are the well-documented metrics that can characterize gait patterns of healthy young adults and elderly including patients with neurological diseases, respectively. Here, we examined effects of a dual task on gait cycle variability in healthy young adults, based on the mean and standard deviation statistics as well as the positive persistency of the stride intervals during steady walking on a treadmill. Specifically, three gait conditions were examined: control condition, non-cognitive task with holding a smartphone in front of the chest using their dominant hand and looking fixedly at a blank screen of the smartphone, and cognitive motor task with holding a smartphone as in the non-cognitive task and playing a puzzle game displayed on the smartphone by one-thumb operation. We showed that only the positive persistency, not the mean and standard deviation statistics, was affected by the cognitive and motor load of smartphone usage in the cognitive condition. More specifically, the positive persistency exhibited in the control and the non-cognitive conditions was significantly reduced in the cognitive condition. Our results suggest that the decrease in the positive persistency during the cognitive task, which might represent the deterioration of healthy gait pattern, is caused endogenously by the cognitive and motor load, not necessarily by the reduction of visual field as often hypothesized.
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Affiliation(s)
- Shunpei Yano
- Department of Mechanical Science and Bioengineering, Osaka University, Osaka, 5608531, Japan
| | - Akihiro Nakamura
- Department of Mechanical Science and Bioengineering, Osaka University, Osaka, 5608531, Japan
| | - Yasuyuki Suzuki
- Department of Mechanical Science and Bioengineering, Osaka University, Osaka, 5608531, Japan
| | - Charles E Smith
- Department of Statistics, North Carolina State University, Raleigh, NC, 27695-8203, USA
| | - Taishin Nomura
- Department of Mechanical Science and Bioengineering, Osaka University, Osaka, 5608531, Japan.
- Department of Informatics, Kyoto University, Kyoto, 606-8501, Japan.
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11
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Mobille Z, Sikandar UB, Sponberg S, Choi H. Temporal resolution of spike coding in feedforward networks with signal convergence and divergence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.08.602598. [PMID: 39026834 PMCID: PMC11257569 DOI: 10.1101/2024.07.08.602598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Convergent and divergent structures in the networks that make up biological brains are found universally across many species and brain regions at various scales. Neurons in these networks fire action potentials, or "spikes", whose precise timing is becoming increasingly appreciated as large sources of information about both sensory input and motor output. While previous theories on coding in convergent and divergent networks have largely neglected the role of precise spike timing, our model and analyses place this aspect at the forefront. For a suite of stimuli with different timescales, we demonstrate that structural bottlenecks (small groups of neurons) post-synaptic to network convergence have a stronger preference for spike timing codes than expansion layers created by structural divergence. Additionally, we found that a simple network model with similar convergence and divergence ratios to those found experimentally can reproduce the relative contribution of spike timing information about motor output in the hawkmoth Manduca sexta. Our simulations and analyses suggest a relationship between the level of convergent/divergent structure present in a feedforward network and the loss of stimulus information encoded by its population spike trains as their temporal resolution decreases, which could be confirmed experimentally across diverse neural systems in future studies. We further show that this relationship can be generalized across different models and measures, implying a potentially fundamental link between network structure and coding strategy using spikes.
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Affiliation(s)
- Zach Mobille
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, GA 30332
| | - Usama Bin Sikandar
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
| | - Simon Sponberg
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, GA 30332
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
| | - Hannah Choi
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, GA 30332
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12
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Fernández JG, Keemink S, van Gerven M. Gradient-free training of recurrent neural networks using random perturbations. Front Neurosci 2024; 18:1439155. [PMID: 39050673 PMCID: PMC11267880 DOI: 10.3389/fnins.2024.1439155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 06/25/2024] [Indexed: 07/27/2024] Open
Abstract
Recurrent neural networks (RNNs) hold immense potential for computations due to their Turing completeness and sequential processing capabilities, yet existing methods for their training encounter efficiency challenges. Backpropagation through time (BPTT), the prevailing method, extends the backpropagation (BP) algorithm by unrolling the RNN over time. However, this approach suffers from significant drawbacks, including the need to interleave forward and backward phases and store exact gradient information. Furthermore, BPTT has been shown to struggle to propagate gradient information for long sequences, leading to vanishing gradients. An alternative strategy to using gradient-based methods like BPTT involves stochastically approximating gradients through perturbation-based methods. This learning approach is exceptionally simple, necessitating only forward passes in the network and a global reinforcement signal as feedback. Despite its simplicity, the random nature of its updates typically leads to inefficient optimization, limiting its effectiveness in training neural networks. In this study, we present a new approach to perturbation-based learning in RNNs whose performance is competitive with BPTT, while maintaining the inherent advantages over gradient-based learning. To this end, we extend the recently introduced activity-based node perturbation (ANP) method to operate in the time domain, leading to more efficient learning and generalization. We subsequently conduct a range of experiments to validate our approach. Our results show similar performance, convergence time and scalability when compared to BPTT, strongly outperforming standard node perturbation and weight perturbation methods. These findings suggest that perturbation-based learning methods offer a versatile alternative to gradient-based methods for training RNNs which can be ideally suited for neuromorphic computing applications.
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Affiliation(s)
- Jesús García Fernández
- Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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13
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Patil NS, Dingwell JB, Cusumano JP. A model of task-level human stepping regulation yields semistable walking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583616. [PMID: 38979349 PMCID: PMC11230222 DOI: 10.1101/2024.03.05.583616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
A simple lateral dynamic walker, with swing leg dynamics and three adjustable input parameters, is used to study how motor regulation affects frontal plane stepping. Motivated by experimental observations and phenomenological models, we imposed task-level multiobjective regulation targeting the walker's optimal lateral foot placement at each step. The regulator prioritizes achieving step width and lateral body position goals to varying degrees by choosing a mixture parameter. Our model thus integrates a lateral mechanical template, which captures fundamental mechanics of frontal-plane walking, with a lateral motor regulation template, an empirically verified model of how humans manipulate lateral foot placements in a goal-directed manner. The model captures experimentally observed stepping fluctuation statistics and demonstrates how linear empirical models of stepping dynamics can emerge from first-principles nonlinear mechanics. We find that task-level regulation gives rise to a goal equivalent manifold in the system's extended state space of mechanical states and inputs, a subset of which contains a continuum of period-1 gaits forming a semistable set: perturbations off of any of its gaits result in transients that return to the set, though typically to different gaits.
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Affiliation(s)
- Navendu S. Patil
- Department of Kinesiology, Pennsylvania State University, University Park, PA 16802, USA
- Department of Engineering Science & Mechanics, Pennsylvania State University, University Park, PA 16802, USA
| | - Jonathan B. Dingwell
- Department of Kinesiology, Pennsylvania State University, University Park, PA 16802, USA
| | - Joseph P. Cusumano
- Department of Engineering Science & Mechanics, Pennsylvania State University, University Park, PA 16802, USA
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14
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Negrón A, Getz MP, Handy G, Doiron B. The mechanics of correlated variability in segregated cortical excitatory subnetworks. Proc Natl Acad Sci U S A 2024; 121:e2306800121. [PMID: 38959037 PMCID: PMC11252788 DOI: 10.1073/pnas.2306800121] [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] [Received: 04/25/2023] [Accepted: 04/03/2024] [Indexed: 07/04/2024] Open
Abstract
Understanding the genesis of shared trial-to-trial variability in neuronal population activity within the sensory cortex is critical to uncovering the biological basis of information processing in the brain. Shared variability is often a reflection of the structure of cortical connectivity since it likely arises, in part, from local circuit inputs. A series of experiments from segregated networks of (excitatory) pyramidal neurons in the mouse primary visual cortex challenge this view. Specifically, the across-network correlations were found to be larger than predicted given the known weak cross-network connectivity. We aim to uncover the circuit mechanisms responsible for these enhanced correlations through biologically motivated cortical circuit models. Our central finding is that coupling each excitatory subpopulation with a specific inhibitory subpopulation provides the most robust network-intrinsic solution in shaping these enhanced correlations. This result argues for the existence of excitatory-inhibitory functional assemblies in early sensory areas which mirror not just response properties but also connectivity between pyramidal cells. Furthermore, our findings provide theoretical support for recent experimental observations showing that cortical inhibition forms structural and functional subnetworks with excitatory cells, in contrast to the classical view that inhibition is a nonspecific blanket suppression of local excitation.
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Affiliation(s)
- Alex Negrón
- Department of Applied Mathematics, Illinois Institute of Technology, Chicago, IL60616
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL60637
| | - Matthew P. Getz
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL60637
- Department of Neurobiology, University of Chicago, Chicago, IL60637
- Department of Statistics, University of Chicago, Chicago, IL60637
| | - Gregory Handy
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL60637
- Department of Neurobiology, University of Chicago, Chicago, IL60637
- Department of Statistics, University of Chicago, Chicago, IL60637
| | - Brent Doiron
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL60637
- Department of Neurobiology, University of Chicago, Chicago, IL60637
- Department of Statistics, University of Chicago, Chicago, IL60637
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15
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Tomić I, Adamcová D, Fehér M, Bays PM. Dissecting the components of error in analogue report tasks : Error in analogue report tasks. Behav Res Methods 2024:10.3758/s13428-024-02453-w. [PMID: 38977610 DOI: 10.3758/s13428-024-02453-w] [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] [Accepted: 06/05/2024] [Indexed: 07/10/2024]
Abstract
Over the last two decades, the analogue report task has become a standard method for measuring the fidelity of visual representations across research domains including perception, attention, and memory. Despite its widespread use, there has been no methodical investigation of the different task parameters that might contribute to response variability. To address this gap, we conducted two experiments manipulating components of a typical analogue report test of memory for colour hue. We found that human response errors were independently affected by changes in storage and maintenance requirements of the task, demonstrated by a strong effect of set size even in the absence of a memory delay. In contrast, response variability remained unaffected by physical size of the colour wheel, implying negligible contribution of motor noise to task performance, or by its chroma radius, highlighting non-uniformity of the standard colour space. Comparing analogue report to a matched forced-choice task, we found variation in adjustment criterion made a limited contribution to analogue report variability, becoming meaningful only with low representational noise. Our findings validate the analogue report task as a robust measure of representational fidelity for most purposes, while also quantifying non-representational sources of noise that would limit its reliability in specialized settings.
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Affiliation(s)
- Ivan Tomić
- Department of Psychology, University of Cambridge, Cambridge, England.
- Department of Psychology, Faculty of Humanities and Social Sciences, University of Zagreb, Ivana Lucica 3, 10000, Zagreb, Croatia.
| | - Dagmar Adamcová
- Department of Psychology, University of Cambridge, Cambridge, England
| | - Máté Fehér
- Faculty of Biology, University of Cambridge, Cambridge, England
| | - Paul M Bays
- Department of Psychology, University of Cambridge, Cambridge, England
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16
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Adl Zarrabi A, Jeulin M, Bardet P, Commère P, Naccache L, Aucouturier JJ, Ponsot E, Villain M. A simple psychophysical procedure separates representational and noise components in impairments of speech prosody perception after right-hemisphere stroke. Sci Rep 2024; 14:15194. [PMID: 38956187 PMCID: PMC11219855 DOI: 10.1038/s41598-024-64295-y] [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] [Received: 12/19/2023] [Accepted: 06/06/2024] [Indexed: 07/04/2024] Open
Abstract
After a right hemisphere stroke, more than half of the patients are impaired in their capacity to produce or comprehend speech prosody. Yet, and despite its social-cognitive consequences for patients, aprosodia following stroke has received scant attention. In this report, we introduce a novel, simple psychophysical procedure which, by combining systematic digital manipulations of speech stimuli and reverse-correlation analysis, allows estimating the internal sensory representations that subtend how individual patients perceive speech prosody, and the level of internal noise that govern behavioral variability in how patients apply these representations. Tested on a sample of N = 22 right-hemisphere stroke survivors and N = 21 age-matched controls, the representation + noise model provides a promising alternative to the clinical gold standard for evaluating aprosodia (MEC): both parameters strongly associate with receptive, and not expressive, aprosodia measured by MEC within the patient group; they have better sensitivity than MEC for separating high-functioning patients from controls; and have good specificity with respect to non-prosody-related impairments of auditory attention and processing. Taken together, individual differences in either internal representation, internal noise, or both, paint a potent portrait of the variety of sensory/cognitive mechanisms that can explain impairments of prosody processing after stroke.
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Affiliation(s)
- Aynaz Adl Zarrabi
- Université de Franche-Comté, SUPMICROTECH, CNRS, Institut FEMTO-ST, 25000, Besançon, France
| | - Mélissa Jeulin
- Department of Physical Medicine & Rehabilitation, APHP/Hôpital Pitié-Salpêtrière, 75013, Paris, France
| | - Pauline Bardet
- Department of Physical Medicine & Rehabilitation, APHP/Hôpital Pitié-Salpêtrière, 75013, Paris, France
| | - Pauline Commère
- Department of Physical Medicine & Rehabilitation, APHP/Hôpital Pitié-Salpêtrière, 75013, Paris, France
| | - Lionel Naccache
- Department of Physical Medicine & Rehabilitation, APHP/Hôpital Pitié-Salpêtrière, 75013, Paris, France
- Paris Brain Institute (ICM), Inserm, CNRS, PICNIC-Lab, 75013, Paris, France
| | | | - Emmanuel Ponsot
- Science & Technology of Music and Sound, IRCAM/CNRS/Sorbonne Université, 75004, Paris, France
| | - Marie Villain
- Department of Physical Medicine & Rehabilitation, APHP/Hôpital Pitié-Salpêtrière, 75013, Paris, France.
- Paris Brain Institute (ICM), Inserm, CNRS, PICNIC-Lab, 75013, Paris, France.
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17
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Wu N, Valera I, Sinz F, Ecker A, Euler T, Qiu Y. Probabilistic neural transfer function estimation with Bayesian system identification. PLoS Comput Biol 2024; 20:e1012354. [PMID: 39083559 DOI: 10.1371/journal.pcbi.1012354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 08/12/2024] [Accepted: 07/22/2024] [Indexed: 08/02/2024] Open
Abstract
Neural population responses in sensory systems are driven by external physical stimuli. This stimulus-response relationship is typically characterized by receptive fields, which have been estimated by neural system identification approaches. Such models usually require a large amount of training data, yet, the recording time for animal experiments is limited, giving rise to epistemic uncertainty for the learned neural transfer functions. While deep neural network models have demonstrated excellent power on neural prediction, they usually do not provide the uncertainty of the resulting neural representations and derived statistics, such as most exciting inputs (MEIs), from in silico experiments. Here, we present a Bayesian system identification approach to predict neural responses to visual stimuli, and explore whether explicitly modeling network weight variability can be beneficial for identifying neural response properties. To this end, we use variational inference to estimate the posterior distribution of each model weight given the training data. Tests with different neural datasets demonstrate that this method can achieve higher or comparable performance on neural prediction, with a much higher data efficiency compared to Monte Carlo dropout methods and traditional models using point estimates of the model parameters. At the same time, our variational method provides us with an effectively infinite ensemble, avoiding the idiosyncrasy of any single model, to generate MEIs. This allows us to estimate the uncertainty of stimulus-response function, which we have found to be negatively correlated with the predictive performance at model level and may serve to evaluate models. Furthermore, our approach enables us to identify response properties with credible intervals and to determine whether the inferred features are meaningful by performing statistical tests on MEIs. Finally, in silico experiments show that our model generates stimuli driving neuronal activity significantly better than traditional models in the limited-data regime.
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Affiliation(s)
- Nan Wu
- Department of Computer Science, Saarland University, Saarbrücken, Germany
- Institute for Ophthalmic Research and Centre for Integrative Neuroscience (CIN), Tübingen University, Tübingen, Germany
| | - Isabel Valera
- Department of Computer Science, Saarland University, Saarbrücken, Germany
| | - Fabian Sinz
- Department of Computer Science and Campus Institute Data Science (CIDAS), Göttingen University, Göttingen, Germany
| | - Alexander Ecker
- Department of Computer Science and Campus Institute Data Science (CIDAS), Göttingen University, Göttingen, Germany
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Thomas Euler
- Institute for Ophthalmic Research and Centre for Integrative Neuroscience (CIN), Tübingen University, Tübingen, Germany
| | - Yongrong Qiu
- Institute for Ophthalmic Research and Centre for Integrative Neuroscience (CIN), Tübingen University, Tübingen, Germany
- Department of Computer Science and Campus Institute Data Science (CIDAS), Göttingen University, Göttingen, Germany
- Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, California, United State of America
- Stanford Bio-X, Stanford University, Stanford, California, United State of America
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, California, United State of America
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18
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Fukushima M, Leibnitz K. Effects of packetization on communication dynamics in brain networks. Netw Neurosci 2024; 8:418-436. [PMID: 38952819 PMCID: PMC11142457 DOI: 10.1162/netn_a_00360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 01/18/2024] [Indexed: 07/03/2024] Open
Abstract
Computational studies in network neuroscience build models of communication dynamics in the connectome that help us understand the structure-function relationships of the brain. In these models, the dynamics of cortical signal transmission in brain networks are approximated with simple propagation strategies such as random walks and shortest path routing. Furthermore, the signal transmission dynamics in brain networks can be associated with the switching architectures of engineered communication systems (e.g., message switching and packet switching). However, it has been unclear how propagation strategies and switching architectures are related in models of brain network communication. Here, we investigate the effects of the difference between packet switching and message switching (i.e., whether signals are packetized or not) on the transmission completion time of propagation strategies when simulating signal propagation in mammalian brain networks. The results show that packetization in the connectome with hubs increases the time of the random walk strategy and does not change that of the shortest path strategy, but decreases that of more plausible strategies for brain networks that balance between communication speed and information requirements. This finding suggests an advantage of packet-switched communication in the connectome and provides new insights into modeling the communication dynamics in brain networks.
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Affiliation(s)
- Makoto Fukushima
- Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima, Japan
| | - Kenji Leibnitz
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka, Japan
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
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19
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Yacoubi B, Christou EA. Motor Output Variability in Movement Disorders: Insights From Essential Tremor. Exerc Sport Sci Rev 2024; 52:95-101. [PMID: 38445865 DOI: 10.1249/jes.0000000000000338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
Findings on individuals with essential tremor suggest that tremor (within-trial movement unsteadiness) and inconsistency (trial-to-trial movement variance) stem from distinct pathologies and affect function uniquely. Nonetheless, the intricacies of inconsistency in movement disorders remain largely unexplored, as exemplified in ataxia where inconsistency below healthy levels is associated with greater pathology. We advocate for clinical assessments that quantify both tremor and inconsistency.
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20
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Yu P, Dong R, Wang X, Tang Y, Liu Y, Wang C, Zhao L. Neuroimaging of motor recovery after ischemic stroke - functional reorganization of motor network. Neuroimage Clin 2024; 43:103636. [PMID: 38950504 PMCID: PMC11267109 DOI: 10.1016/j.nicl.2024.103636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 06/01/2024] [Accepted: 06/27/2024] [Indexed: 07/03/2024]
Abstract
The long-term motor outcome of acute stroke patients may be correlated to the reorganization of brain motor network. Abundant neuroimaging studies contribute to understand the pathological changes and recovery of motor networks after stroke. In this review, we summarized how current neuroimaging studies have increased understanding of reorganization and plasticity in post stroke motor recovery. Firstly, we discussed the changes in the motor network over time during the motor-activation and resting states, as well as the overall functional integration trend of the motor network. These studies indicate that the motor network undergoes dynamic bilateral hemispheric functional reorganization, as well as a trend towards network randomization. In the second part, we summarized the current study progress in the application of neuroimaging technology to early predict the post-stroke motor outcome. In the third part, we discuss the neuroimaging techniques commonly used in the post-stroke recovery. These methods provide direct or indirect visualization patterns to understand the neural mechanisms of post-stroke motor recovery, opening up new avenues for studying spontaneous and treatment-induced recovery and plasticity after stroke.
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Affiliation(s)
- Pei Yu
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Ruoyu Dong
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Xiao Wang
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yuqi Tang
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yaning Liu
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Can Wang
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Ling Zhao
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
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21
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Caballero C, Barbado D, Peláez M, Moreno FJ. Applying different levels of practice variability for motor learning: More is not better. PeerJ 2024; 12:e17575. [PMID: 38948206 PMCID: PMC11212619 DOI: 10.7717/peerj.17575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 05/24/2024] [Indexed: 07/02/2024] Open
Abstract
Background Variable practice is a broadly used tool to improve motor learning processes. However, controversial results can be found in literature about the success of this type of practice compared to constant practice. This study explored one potential reason for this controversy: the manipulation of variable practice load applied during practice and its effects according to the initial performance level and the initial intrinsic variability of the learner. Method Sixty-five participants were grouped into four practice schedules to learn a serial throwing task, in which the training load of variable practice was manipulated: one constant practice group and three groups with different variable practice loads applied. After a pre-test, participants trained for 2 weeks. A post-test and three retests (96 h, 2 weeks and 1 month) were carried out after training. The participants' throwing accuracy was assessed through error parameters and their initial intrinsic motor variability was assessed by the autocorrelation coefficient of the error. Results The four groups improved their throwing performance. Pairwise comparisons and effect sizes showed larger error reduction in the low variability group. Different loads of variable practice seem to induce different performance improvements in a throwing task. The modulation of the variable practice load seems to be a step forward to clarify the controversy about its benefits, but it has to be guided by the individuals' features, mainly by the initial intrinsic variability of the learner.
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Affiliation(s)
- Carla Caballero
- Sport Sciences Department, Sport Research Centre, Universiad Miguel Hernández de Elche, Elche, Alicante, Spain
- Neurosciences Research Group, Alicante Institute for Health and Biomedical Research (ISABIAL), Spain, Alicante, Spain
| | - David Barbado
- Sport Sciences Department, Sport Research Centre, Universiad Miguel Hernández de Elche, Elche, Alicante, Spain
- Neurosciences Research Group, Alicante Institute for Health and Biomedical Research (ISABIAL), Spain, Alicante, Spain
| | - Manuel Peláez
- Sport Sciences Department, Sport Research Centre, Universiad Miguel Hernández de Elche, Elche, Alicante, Spain
| | - Francisco J. Moreno
- Sport Sciences Department, Sport Research Centre, Universiad Miguel Hernández de Elche, Elche, Alicante, Spain
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22
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Ilan Y. Free Will as Defined by the Constrained Disorder Principle: a Restricted, Mandatory, Personalized, Regulated Process for Decision-Making. Integr Psychol Behav Sci 2024:10.1007/s12124-024-09853-9. [PMID: 38900370 DOI: 10.1007/s12124-024-09853-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/08/2024] [Indexed: 06/21/2024]
Abstract
The concept of free will has challenged physicists, biologists, philosophers, and other professionals for decades. The constrained disorder principle (CDP) is a fundamental law that defines systems according to their inherent variability. It provides mechanisms for adapting to dynamic environments. This work examines the CDP's perspective of free will concerning various free will theories. Per the CDP, systems lack intentions, and the "freedom" to select and act is built into their design. The "freedom" is embedded within the response range determined by the boundaries of the systems' variability. This built-in and self-generating mechanism enables systems to cope with perturbations. According to the CDP, neither dualism nor an unknown metaphysical apparatus dictates choices. Brain variability facilitates cognitive adaptation to complex, unpredictable situations across various environments. Human behaviors and decisions reflect an underlying physical variability in the brain and other organs for dealing with unpredictable noises. Choices are not predetermined but reflect the ongoing adaptation processes to dynamic prssu½res. Malfunctions and disease states are characterized by inappropriate variability, reflecting an inability to respond adequately to perturbations. Incorporating CDP-based interventions can overcome malfunctions and disease states and improve decision processes. CDP-based second-generation artificial intelligence platforms improve interventions and are being evaluated to augment personal development, wellness, and health.
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Affiliation(s)
- Yaron Ilan
- Faculty of Medicine, Hebrew University and Department of Medicine, Hadassah Medical Center, Jerusalem, Israel.
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23
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Schrøder Jakobsen L, Samani A, Desbrosses K, de Zee M, Madeleine P. In-Field Training of a Passive Back Exoskeleton Changes the Biomechanics of Logistic Workers. IISE Trans Occup Ergon Hum Factors 2024:1-13. [PMID: 38869954 DOI: 10.1080/24725838.2024.2359371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/21/2024] [Indexed: 06/15/2024]
Abstract
OCCUPATIONAL APPLICATIONSOccupational exoskeletons receive rising interest in industry as these devices diminish the biomechanical load during manual materials handling. Still, we have limited knowledge when it comes to in-field use. This gap often contributes to failure in the implementation of exoskeleton in industry. In this study, we investigated how a training protocol consisting of in-field use of a passive back exoskeleton affected the biomechanics of logistic workers. More specifically, we focused on how the variation of the muscular and kinematic patterns of the user was altered after exoskeleton training. We found that training had a positive effect on exoskeleton use, as a relative decrease of 6-9% in peak back muscle activity was observed post-training. Additionally, training decreased knee flexion by 6°-16°, indicating a more stoop lifting technique. The findings point at the potential benefits of applying a training approach when implementing a back-supporting exoskeleton in logistics.
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Affiliation(s)
- Lasse Schrøder Jakobsen
- ExerciseTech, Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
| | - Afshin Samani
- ExerciseTech, Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
| | - Kevin Desbrosses
- INRS, French National Research and Safety Institute for the Prevention of Occupational Accidents and Diseases, Nancy, France
| | - Mark de Zee
- ExerciseTech, Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
| | - Pascal Madeleine
- ExerciseTech, Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
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24
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Aldarondo D, Merel J, Marshall JD, Hasenclever L, Klibaite U, Gellis A, Tassa Y, Wayne G, Botvinick M, Ölveczky BP. A virtual rodent predicts the structure of neural activity across behaviours. Nature 2024:10.1038/s41586-024-07633-4. [PMID: 38862024 DOI: 10.1038/s41586-024-07633-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 05/30/2024] [Indexed: 06/13/2024]
Abstract
Animals have exquisite control of their bodies, allowing them to perform a diverse range of behaviours. How such control is implemented by the brain, however, remains unclear. Advancing our understanding requires models that can relate principles of control to the structure of neural activity in behaving animals. Here, to facilitate this, we built a 'virtual rodent', in which an artificial neural network actuates a biomechanically realistic model of the rat1 in a physics simulator2. We used deep reinforcement learning3-5 to train the virtual agent to imitate the behaviour of freely moving rats, thus allowing us to compare neural activity recorded in real rats to the network activity of a virtual rodent mimicking their behaviour. We found that neural activity in the sensorimotor striatum and motor cortex was better predicted by the virtual rodent's network activity than by any features of the real rat's movements, consistent with both regions implementing inverse dynamics6. Furthermore, the network's latent variability predicted the structure of neural variability across behaviours and afforded robustness in a way consistent with the minimal intervention principle of optimal feedback control7. These results demonstrate how physical simulation of biomechanically realistic virtual animals can help interpret the structure of neural activity across behaviour and relate it to theoretical principles of motor control.
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Affiliation(s)
- Diego Aldarondo
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA.
- Fauna Robotics, New York, NY, USA.
| | - Josh Merel
- DeepMind, Google, London, UK
- Fauna Robotics, New York, NY, USA
| | - Jesse D Marshall
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Reality Labs, Meta, New York, NY, USA
| | | | - Ugne Klibaite
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Amanda Gellis
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | | | | | - Matthew Botvinick
- DeepMind, Google, London, UK
- Gatsby Computational Neuroscience Unit, University College London, London, UK
| | - Bence P Ölveczky
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA.
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25
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Russo S, Stanley GB, Najafi F. Spike Reliability is Cell-Type Specific and Shapes Excitation and Inhibition in the Cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.05.597657. [PMID: 38895401 PMCID: PMC11185694 DOI: 10.1101/2024.06.05.597657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Neurons encode information in the highly variable spiking activity of neuronal populations, so that different repetitions of the same stimulus can generate action potentials that vary significantly in terms of the count and timing. How does spiking variability originate, and does it have a functional purpose? Leveraging the Allen Institute cell types dataset, we relate the spiking reliability of cortical neurons in-vitro during the intracellular injection of current resembling synaptic inputs to their morphologic, electrophysiologic, and transcriptomic classes. Our findings demonstrate that parvalbumin+ (PV) interneurons, a subclass of inhibitory neurons, show high reliability compared to other neuronal subclasses, particularly excitatory neurons. Through computational modeling, we predict that the high reliability of PV interneurons allows for strong and precise inhibition in downstream neurons, while the lower reliability of excitatory neurons allows for integrating multiple synaptic inputs leading to a spiking rate code. These findings illuminate how spiking variability in different neuronal classes affect information propagation in the brain, leading to precise inhibition and spiking rate codes.
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Affiliation(s)
- S. Russo
- Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, US
- Allen Institute, Brain and Consciousness Program, Seattle, WA, US
| | - G. B. Stanley
- Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, US
| | - F. Najafi
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, US
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26
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Bredenberg C, Savin C. Desiderata for Normative Models of Synaptic Plasticity. Neural Comput 2024; 36:1245-1285. [PMID: 38776950 DOI: 10.1162/neco_a_01671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 02/06/2024] [Indexed: 05/25/2024]
Abstract
Normative models of synaptic plasticity use computational rationales to arrive at predictions of behavioral and network-level adaptive phenomena. In recent years, there has been an explosion of theoretical work in this realm, but experimental confirmation remains limited. In this review, we organize work on normative plasticity models in terms of a set of desiderata that, when satisfied, are designed to ensure that a given model demonstrates a clear link between plasticity and adaptive behavior, is consistent with known biological evidence about neural plasticity and yields specific testable predictions. As a prototype, we include a detailed analysis of the REINFORCE algorithm. We also discuss how new models have begun to improve on the identified criteria and suggest avenues for further development. Overall, we provide a conceptual guide to help develop neural learning theories that are precise, powerful, and experimentally testable.
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Affiliation(s)
- Colin Bredenberg
- Center for Neural Science, New York University, New York, NY 10003, U.S.A
- Mila-Quebec AI Institute, Montréal, QC H2S 3H1, Canada
| | - Cristina Savin
- Center for Neural Science, New York University, New York, NY 10003, U.S.A
- Center for Data Science, New York University, New York, NY 10011, U.S.A.
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27
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Boundy-Singer ZM, Ziemba CM, Hénaff OJ, Goris RLT. How does V1 population activity inform perceptual certainty? J Vis 2024; 24:12. [PMID: 38884544 PMCID: PMC11185272 DOI: 10.1167/jov.24.6.12] [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] [Received: 02/15/2024] [Accepted: 05/06/2024] [Indexed: 06/18/2024] Open
Abstract
Neural population activity in sensory cortex informs our perceptual interpretation of the environment. Oftentimes, this population activity will support multiple alternative interpretations. The larger the spread of probability over different alternatives, the more uncertain the selected perceptual interpretation. We test the hypothesis that the reliability of perceptual interpretations can be revealed through simple transformations of sensory population activity. We recorded V1 population activity in fixating macaques while presenting oriented stimuli under different levels of nuisance variability and signal strength. We developed a decoding procedure to infer from V1 activity the most likely stimulus orientation as well as the certainty of this estimate. Our analysis shows that response magnitude, response dispersion, and variability in response gain all offer useful proxies for orientation certainty. Of these three metrics, the last one has the strongest association with the decoder's uncertainty estimates. These results clarify that the nature of neural population activity in sensory cortex provides downstream circuits with multiple options to assess the reliability of perceptual interpretations.
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Affiliation(s)
- Zoe M Boundy-Singer
- Center for Perceptual Systems, University of Texas at Austin, Austin, TX, USA
| | - Corey M Ziemba
- Center for Perceptual Systems, University of Texas at Austin, Austin, TX, USA
| | | | - Robbe L T Goris
- Center for Perceptual Systems, University of Texas at Austin, Austin, TX, USA
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28
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Kurvits L, Stenner MP, Guo S, Neumann WJ, Haggard P, Ganos C. Rapid Compensation for Noisy Voluntary Movements in Adults with Primary Tic Disorders. Mov Disord 2024; 39:955-964. [PMID: 38661451 DOI: 10.1002/mds.29775] [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] [Received: 10/21/2023] [Revised: 01/17/2024] [Accepted: 02/22/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND It has been proposed that tics and premonitory urges in primary tic disorders (PTD), like Tourette syndrome, are a manifestation of sensorimotor noise. However, patients with tics show no obvious movement imprecision in everyday life. One reason could be that patients have strategies to compensate for noise that disrupts performance (ie, noise that is task-relevant). OBJECTIVES Our goal was to unmask effects of elevated sensorimotor noise on the variability of voluntary movements in patients with PTD. METHODS We tested 30 adult patients with PTD (23 male) and 30 matched controls in a reaching task designed to unmask latent noise. Subjects reached to targets whose shape allowed for variability either in movement direction or extent. This enabled us to decompose variability into task-relevant versus less task-relevant components, where the latter should be less affected by compensatory strategies than the former. In alternating blocks, the task-relevant target dimension switched, allowing us to explore the temporal dynamics with which participants adjusted movement variability to changes in task demands. RESULTS Both groups accurately reached to targets, and adjusted movement precision based on target shape. However, when task-relevant dimensions of the target changed, patients initially produced movements that were more variable than controls, before regaining precision after several reaches. This effect persisted across repeated changes in the task-relevant dimension across the experiment, and therefore did not reflect an effect of novelty, or differences in learning. CONCLUSIONS Our results suggest that patients with PTD generate noisier voluntary movements compared with controls, but rapidly compensate according to current task demands. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Lille Kurvits
- Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
- Department of Neurology, Charité University Hospital, Berlin, Germany
| | - Max-Philipp Stenner
- Department of Neurology, University Hospital Magdeburg, Magdeburg, Germany
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health, Jena-Magdeburg-Halle, Germany
| | - Siqi Guo
- Department of Neurology, Charité University Hospital, Berlin, Germany
| | | | - Patrick Haggard
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Christos Ganos
- Department of Neurology, Charité University Hospital, Berlin, Germany
- Movement Disorder Clinic, Edmond J. Safra Program in Parkinson's Disease, Division of Neurology, University of Toronto, Toronto Western Hospital, Toronto, Ontario, Canada
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29
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Pietzonka P, Coghi F. Thermodynamic cost for precision of general counting observables. Phys Rev E 2024; 109:064128. [PMID: 39020906 DOI: 10.1103/physreve.109.064128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 05/13/2024] [Indexed: 07/20/2024]
Abstract
We analytically derive universal bounds that describe the tradeoff between thermodynamic cost and precision in a sequence of events related to some internal changes of an otherwise hidden physical system. The precision is quantified by the fluctuations in either the number of events counted over time or the waiting times between successive events. Our results are valid for the same broad class of nonequilibrium driven systems considered by the thermodynamic uncertainty relation, but they extend to both time-symmetric and asymmetric observables. We show how optimal precision saturating the bounds can be achieved. For waiting-time fluctuations of asymmetric observables, a phase transition in the optimal configuration arises, where higher precision can be achieved by combining several signals.
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30
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Héroux ME, Fisher G, Axelson LH, Butler AA, Gandevia SC. How we perceive the width of grasped objects: Insights into the central processes that govern proprioceptive judgements. J Physiol 2024; 602:2899-2916. [PMID: 38734987 DOI: 10.1113/jp286322] [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] [Received: 01/22/2024] [Accepted: 04/09/2024] [Indexed: 05/13/2024] Open
Abstract
Low-level proprioceptive judgements involve a single frame of reference, whereas high-level proprioceptive judgements are made across different frames of reference. The present study systematically compared low-level (grasp → $\rightarrow$ grasp) and high-level (vision → $\rightarrow$ grasp, grasp → $\rightarrow$ vision) proprioceptive tasks, and quantified the consistency of grasp → $\rightarrow$ vision and possible reciprocal nature of related high-level proprioceptive tasks. Experiment 1 (n = 30) compared performance across vision → $\rightarrow$ grasp, a grasp → $\rightarrow$ vision and a grasp → $\rightarrow$ grasp tasks. Experiment 2 (n = 30) compared performance on the grasp → $\rightarrow$ vision task between hands and over time. Participants were accurate (mean absolute error 0.27 cm [0.20 to 0.34]; mean [95% CI]) and precise (R 2 $R^2$ = 0.95 [0.93 to 0.96]) for grasp → $\rightarrow$ grasp judgements, with a strong correlation between outcomes (r = -0.85 [-0.93 to -0.70]). Accuracy and precision decreased in the two high-level tasks (R 2 $R^2$ = 0.86 and 0.89; mean absolute error = 1.34 and 1.41 cm), with most participants overestimating perceived width for the vision → $\rightarrow$ grasp task and underestimating it for grasp → $\rightarrow$ vision task. There was minimal correlation between accuracy and precision for these two tasks. Converging evidence indicated performance was largely reciprocal (inverse) between the vision → $\rightarrow$ grasp and grasp → $\rightarrow$ vision tasks. Performance on the grasp → $\rightarrow$ vision task was consistent between dominant and non-dominant hands, and across repeated sessions a day or week apart. Overall, there are fundamental differences between low- and high-level proprioceptive judgements that reflect fundamental differences in the cortical processes that underpin these perceptions. Moreover, the central transformations that govern high-level proprioceptive judgements of grasp are personalised, stable and reciprocal for reciprocal tasks. KEY POINTS: Low-level proprioceptive judgements involve a single frame of reference (e.g. indicating the width of a grasped object by selecting from a series of objects of different width), whereas high-level proprioceptive judgements are made across different frames of reference (e.g. indicating the width of a grasped object by selecting from a series of visible lines of different length). We highlight fundamental differences in the precision and accuracy of low- and high-level proprioceptive judgements. We provide converging evidence that the neural transformations between frames of reference that govern high-level proprioceptive judgements of grasp are personalised, stable and reciprocal for reciprocal tasks. This stability is likely key to precise judgements and accurate predictions in high-level proprioception.
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Affiliation(s)
- Martin E Héroux
- Neuroscience Research Australia, Randwick, Australia
- University of New South Wales, Sydney, Australia
| | - Georgia Fisher
- Neuroscience Research Australia, Randwick, Australia
- Australian Institute of Health Innovation, Macquarie University, Macquarie Park, Australia
| | | | - Annie A Butler
- Neuroscience Research Australia, Randwick, Australia
- University of New South Wales, Sydney, Australia
| | - Simon C Gandevia
- Neuroscience Research Australia, Randwick, Australia
- University of New South Wales, Sydney, Australia
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31
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Hatton AL, Chatfield MD, Gane EM, Maharaj JN, Cattagni T, Burns J, Paton J, Rome K, Kerr G. The effects of wearing textured versus smooth shoe insoles for 4-weeks in people with diabetic peripheral neuropathy: a randomised controlled trial. Disabil Rehabil 2024:1-11. [PMID: 38819206 DOI: 10.1080/09638288.2024.2360658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 05/22/2024] [Indexed: 06/01/2024]
Abstract
PURPOSE To determine whether short-term wear of textured insoles alters balance, gait, foot sensation, physical activity, or patient-reported outcomes, in people with diabetic neuropathy. MATERIALS AND METHODS 53 adults with diabetic neuropathy were randomised to wear textured or smooth insoles for 4-weeks. At baseline and post-intervention, balance (foam/firm surface; eyes open/closed) and walking were assessed whilst barefoot, wearing shoes only, and two insoles (textured/smooth). The primary outcome was center of pressure (CoP) total sway velocity. Secondary outcomes included other CoP measures, spatiotemporal gait measures, foot sensation, physical activity, and patient-reported outcomes (foot health, falls efficacy). RESULTS Wearing textured insoles led to improvements in CoP measures when standing on foam with eyes open, relative to smooth insoles (p ≤ 0.04). The intervention group demonstrated a 5% reduction in total sway velocity, indicative of greater balance. The intervention group also showed a 9-point improvement in self-perceived vigour (p = 0.03). Adjustments for multiple comparisons were not applied. CONCLUSIONS This study provides weak statistical evidence in favour of textured insoles. Wearing textured insoles may alter measures of balance, suggestive of greater stability, in people with diabetic neuropathy. Plantar stimulation, through textured insoles, may have the capacity to modulate the perception of foot pain, leading to improved well-being.IMPLICATIONS FOR REHABILITATIONShort-term wear of textured insoles can lead to improvements in centre of pressure sway measures when standing on a compliant supporting surface.Wearing textured insoles may have the capacity to help relieve foot pain leading to enhanced self-perceived vitality in people with diabetic peripheral neuropathy.
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Affiliation(s)
- Anna L Hatton
- School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia
| | - Mark D Chatfield
- Centre for Health Sciences Research, The University of Queensland, Brisbane, Australia
| | - Elise M Gane
- School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia
| | - Jayishni N Maharaj
- School of Allied Health Sciences, Griffith University, Gold Coast, Australia
| | - Thomas Cattagni
- Laboratory Movement, Interactions, Performance EA 4334, University of Nantes, Nantes, France
| | - Joshua Burns
- Faculty of Medicine and Health & Children's Hospital at Westmead, University of Sydney School of Health Sciences, Sydney, Australia
| | - Joanne Paton
- School of Health Professions, Faculty of Health, University of Plymouth, Plymouth, UK
| | - Keith Rome
- School of Clinical Sciences, Auckland University of Technology, Auckland, New Zealand
| | - Graham Kerr
- Movement Neuroscience Group, School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Australia
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32
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Li C, Brenner J, Boesky A, Ramanathan S, Kreiman G. Neuron-level Prediction and Noise can Implement Flexible Reward-Seeking Behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595306. [PMID: 38826332 PMCID: PMC11142161 DOI: 10.1101/2024.05.22.595306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
We show that neural networks can implement reward-seeking behavior using only local predictive updates and internal noise. These networks are capable of autonomous interaction with an environment and can switch between explore and exploit behavior, which we show is governed by attractor dynamics. Networks can adapt to changes in their architectures, environments, or motor interfaces without any external control signals. When networks have a choice between different tasks, they can form preferences that depend on patterns of noise and initialization, and we show that these preferences can be biased by network architectures or by changing learning rates. Our algorithm presents a flexible, biologically plausible way of interacting with environments without requiring an explicit environmental reward function, allowing for behavior that is both highly adaptable and autonomous. Code is available at https://github.com/ccli3896/PaN.
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Affiliation(s)
- Chenguang Li
- Biophysics Program, Harvard College, Cambridge, MA 02138
| | | | | | - Sharad Ramanathan
- Department of Molecular and Cellular Biology, Harvard University Cambridge, MA 02138
| | - Gabriel Kreiman
- Boston Children's Hospital, Harvard Medical School, Boston, MA 02115
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Judd N, Aristodemou M, Klingberg T, Kievit R. Interindividual Differences in Cognitive Variability Are Ubiquitous and Distinct From Mean Performance in a Battery of Eleven Tasks. J Cogn 2024; 7:45. [PMID: 38799081 PMCID: PMC11122693 DOI: 10.5334/joc.371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024] Open
Abstract
Our performance on cognitive tasks fluctuates: the same individual completing the same task will differ in their response's moment-to-moment. For decades cognitive fluctuations have been implicitly ignored - treated as measurement error - with a focus instead on aggregates such as mean performance. Leveraging dense trial-by-trial data and novel time-series methods we explored variability as an intrinsically important phenotype. Across eleven cognitive tasks with over 7 million trials, we found highly reliable interindividual differences in cognitive variability in every task we examined. These differences are both qualitatively and quantitatively distinct from mean performance. Moreover, we found that a single dimension for variability across tasks was inadequate, demonstrating that previously posited global mechanisms for cognitive variability are at least partially incomplete. Our findings indicate that variability is a fundamental part of cognition - with the potential to offer novel insights into developmental processes.
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Affiliation(s)
- Nicholas Judd
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Michael Aristodemou
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Torkel Klingberg
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Rogier Kievit
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
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34
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Peviani VC, Miller LE, Medendorp WP. Biases in hand perception are driven by somatosensory computations, not a distorted hand model. Curr Biol 2024; 34:2238-2246.e5. [PMID: 38718799 DOI: 10.1016/j.cub.2024.04.010] [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: 12/04/2023] [Revised: 02/09/2024] [Accepted: 04/04/2024] [Indexed: 05/23/2024]
Abstract
To sense and interact with objects in the environment, we effortlessly configure our fingertips at desired locations. It is therefore reasonable to assume that the underlying control mechanisms rely on accurate knowledge about the structure and spatial dimensions of our hand and fingers. This intuition, however, is challenged by years of research showing drastic biases in the perception of finger geometry.1,2,3,4,5 This perceptual bias has been taken as evidence that the brain's internal representation of the body's geometry is distorted,6 leading to an apparent paradox regarding the skillfulness of our actions.7 Here, we propose an alternative explanation of the biases in hand perception-they are the result of the Bayesian integration of noisy, but unbiased, somatosensory signals about finger geometry and posture. To address this hypothesis, we combined Bayesian reverse engineering with behavioral experimentation on joint and fingertip localization of the index finger. We modeled the Bayesian integration either in sensory or in space-based coordinates, showing that the latter model variant led to biases in finger perception despite accurate representation of finger length. Behavioral measures of joint and fingertip localization responses showed similar biases, which were well fitted by the space-based, but not the sensory-based, model variant. The space-based model variant also outperformed a distorted hand model with built-in geometric biases. In total, our results suggest that perceptual distortions of finger geometry do not reflect a distorted hand model but originate from near-optimal Bayesian inference on somatosensory signals.
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Affiliation(s)
- Valeria C Peviani
- Donders Institute for Cognition and Behavior, Radboud University, Nijmegen 6525 GD, the Netherlands.
| | - Luke E Miller
- Donders Institute for Cognition and Behavior, Radboud University, Nijmegen 6525 GD, the Netherlands
| | - W Pieter Medendorp
- Donders Institute for Cognition and Behavior, Radboud University, Nijmegen 6525 GD, the Netherlands
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35
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Zhou R, Yu Y, Li C. Revealing neural dynamical structure of C. elegans with deep learning. iScience 2024; 27:109759. [PMID: 38711456 PMCID: PMC11070340 DOI: 10.1016/j.isci.2024.109759] [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: 10/07/2023] [Revised: 01/27/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
Caenorhabditis elegans serves as a common model for investigating neural dynamics and functions of biological neural networks. Data-driven approaches have been employed in reconstructing neural dynamics. However, challenges remain regarding the curse of high-dimensionality and stochasticity in realistic systems. In this study, we develop a deep neural network (DNN) approach to reconstruct the neural dynamics of C. elegans and study neural mechanisms for locomotion. Our model identifies two limit cycles in the neural activity space: one underpins basic pirouette behavior, essential for navigation, and the other introduces extra Ω turns. The combination of two limit cycles elucidates predominant locomotion patterns in neural imaging data. The corresponding energy landscape explains the switching strategies between two limit cycles, quantitatively, and provides testable predictions on neural functions and circuit roles. Our work provides a general approach to study neural dynamics by combining imaging data and stochastic modeling.
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Affiliation(s)
- Ruisong Zhou
- School of Mathematical Sciences and Shanghai Center for Mathematical Sciences, Fudan University, Shanghai 200433, China
| | - Yuguo Yu
- Research Institute of Intelligent and Complex Systems, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
| | - Chunhe Li
- School of Mathematical Sciences and Shanghai Center for Mathematical Sciences, Fudan University, Shanghai 200433, China
- Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
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36
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Chang JC, Perich MG, Miller LE, Gallego JA, Clopath C. De novo motor learning creates structure in neural activity that shapes adaptation. Nat Commun 2024; 15:4084. [PMID: 38744847 PMCID: PMC11094149 DOI: 10.1038/s41467-024-48008-7] [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] [Received: 06/26/2023] [Accepted: 04/18/2024] [Indexed: 05/16/2024] Open
Abstract
Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns that can be produced during adaptation. Here, we examined how a neural population's existing activity patterns, acquired through de novo learning, affect subsequent adaptation by modeling motor cortical neural population dynamics with recurrent neural networks. We trained networks on different motor repertoires comprising varying numbers of movements, which they acquired following various learning experiences. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization in the available population activity patterns. This structure facilitated adaptation, but only when the changes imposed by the perturbation were congruent with the organization of the inputs and the structure in neural activity acquired during de novo learning. These results highlight trade-offs in skill acquisition and demonstrate how different learning experiences can shape the geometrical properties of neural population activity and subsequent adaptation.
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Affiliation(s)
- Joanna C Chang
- Department of Bioengineering, Imperial College London, London, UK
| | - Matthew G Perich
- Département de Neurosciences, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
- Mila, Québec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Lee E Miller
- Departments of Physiology, Biomedical Engineering and Physical Medicine and Rehabilitation, Northwestern University and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Juan A Gallego
- Department of Bioengineering, Imperial College London, London, UK.
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK.
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Painchaud V, Desrosiers P, Doyon N. The Determining Role of Covariances in Large Networks of Stochastic Neurons. Neural Comput 2024; 36:1121-1162. [PMID: 38657971 DOI: 10.1162/neco_a_01656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 01/02/2024] [Indexed: 04/26/2024]
Abstract
Biological neural networks are notoriously hard to model due to their stochastic behavior and high dimensionality. We tackle this problem by constructing a dynamical model of both the expectations and covariances of the fractions of active and refractory neurons in the network's populations. We do so by describing the evolution of the states of individual neurons with a continuous-time Markov chain, from which we formally derive a low-dimensional dynamical system. This is done by solving a moment closure problem in a way that is compatible with the nonlinearity and boundedness of the activation function. Our dynamical system captures the behavior of the high-dimensional stochastic model even in cases where the mean-field approximation fails to do so. Taking into account the second-order moments modifies the solutions that would be obtained with the mean-field approximation and can lead to the appearance or disappearance of fixed points and limit cycles. We moreover perform numerical experiments where the mean-field approximation leads to periodically oscillating solutions, while the solutions of the second-order model can be interpreted as an average taken over many realizations of the stochastic model. Altogether, our results highlight the importance of including higher moments when studying stochastic networks and deepen our understanding of correlated neuronal activity.
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Affiliation(s)
- Vincent Painchaud
- Department of Mathematics and Statistics, McGill University, Montreal, Québec H3A 0B6, Canada
| | - Patrick Desrosiers
- Department of Physics, Engineering Physics, and Optics, Université Laval, Quebec City, Québec G1V 0A6, Canada
- CERVO Brain Research Center, Quebec City, Québec G1E 1T2, Canada
- Centre interdisciplinaire en modélisation mathématique de l'Université Laval, Quebec City, Québec G1V 0A6, Canada
| | - Nicolas Doyon
- Départment of Mathematics and Statistics, Université Laval, Quebec City, Québec G1V 0A6, Canada
- CERVO Brain Research Center, Quebec City, Québec G1E 1T2, Canada
- Centre interdisciplinaire en modélisation mathématique de l'Université Laval, Quebec City, Québec G1V 0A6, Canada
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Lin A, Akafia C, Dal Monte O, Fan S, Fagan N, Putnam P, Tye KM, Chang S, Ba D, Allsop AZAS. An unbiased method to partition diverse neuronal responses into functional ensembles reveals interpretable population dynamics during innate social behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593229. [PMID: 38766234 PMCID: PMC11100741 DOI: 10.1101/2024.05.08.593229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
In neuroscience, understanding how single-neuron firing contributes to distributed neural ensembles is crucial. Traditional methods of analysis have been limited to descriptions of whole population activity, or, when analyzing individual neurons, criteria for response categorization varied significantly across experiments. Current methods lack scalability for large datasets, fail to capture temporal changes and rely on parametric assumptions. There's a need for a robust, scalable, and non-parametric functional clustering approach to capture interpretable dynamics. To address this challenge, we developed a model-based, statistical framework for unsupervised clustering of multiple time series datasets that exhibit nonlinear dynamics into an a-priori-unknown number of parameterized ensembles called Functional Encoding Units (FEUs). FEU outperforms existing techniques in accuracy and benchmark scores. Here, we apply this FEU formalism to single-unit recordings collected during social behaviors in rodents and primates and demonstrate its hypothesis-generating and testing capacities. This novel pipeline serves as an analytic bridge, translating neural ensemble codes across model systems.
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Affiliation(s)
- Alexander Lin
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Cyril Akafia
- Department of Psychiatry, Yale University, New Haven, Connecticut, USA
| | - Olga Dal Monte
- Department of Psychology, Yale University, New Haven, Connecticut, USA
| | - Siqi Fan
- Department of Psychology, Yale University, New Haven, Connecticut, USA
| | - Nicholas Fagan
- Department of Psychology, Yale University, New Haven, Connecticut, USA
| | - Philip Putnam
- Department of Psychology, Yale University, New Haven, Connecticut, USA
| | - Kay M. Tye
- Salk Institute for Biological Studies, La Jolla, California, USA
- Howard Hughes Medical Institute, La Jolla, California, USA
- Kavli Institute for the Brain and Mind, La Jolla, California, USA
| | - Steve Chang
- Department of Psychology, Yale University, New Haven, Connecticut, USA
| | - Demba Ba
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
- Center for Brain Sciences, Harvard University, Cambridge, Massachusetts, USA
- Kempner Institute for the Study of Artificial and Natural Intelligence, Harvard University, Cambridge, Massachusetts, USA
| | - AZA Stephen Allsop
- Center for Collective Healing, Department of Psychiatry and Behavioral Sciences, Howard University, Washington DC, USA
- Department of Psychiatry, Yale University, New Haven, Connecticut, USA
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Lim M, Kim DJ, Nascimento TD, DaSilva AF. High-definition tDCS over primary motor cortex modulates brain signal variability and functional connectivity in episodic migraine. Clin Neurophysiol 2024; 161:101-111. [PMID: 38460220 DOI: 10.1016/j.clinph.2024.02.012] [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: 09/23/2023] [Revised: 02/08/2024] [Accepted: 02/10/2024] [Indexed: 03/11/2024]
Abstract
OBJECTIVE This study investigated how high-definition transcranial direct current stimulation (HD-tDCS) over the primary motor cortex (M1) affects brain signal variability and functional connectivity in the trigeminal pain pathway, and their association with changes in migraine attacks. METHODS Twenty-five episodic migraine patients were randomized for ten daily sessions of active or sham M1 HD-tDCS. Resting-state blood-oxygenation-level-dependent (BOLD) signal variability and seed-based functional connectivity were assessed pre- and post-treatment. A mediation analysis was performed to test whether BOLD signal variability mediates the relationship between treatment group and moderate-to-severe headache days. RESULTS The active M1 HD-tDCS group showed reduced BOLD variability in the spinal trigeminal nucleus (SpV) and thalamus, but increased variability in the rostral anterior cingulate cortex (rACC) compared to the sham group. Connectivity decreased between medial pulvinar-temporal pole, medial dorsal-precuneus, and the ventral posterior medial nucleus-SpV, but increased between the rACC-amygdala, and the periaqueductal gray-parahippocampal gyrus. Changes in medial pulvinar variability mediated the reduction in moderate-to-severe headache days at one-month post-treatment. CONCLUSIONS M1 HD-tDCS alters BOLD signal variability and connectivity in the trigeminal somatosensory and modulatory pain system, potentially alleviating migraine headache attacks. SIGNIFICANCE M1 HD-tDCS realigns brain signal variability and connectivity in migraineurs closer to healthy control levels.
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Affiliation(s)
- Manyoel Lim
- Food Processing Research Group, Korea Food Research Institute, Wanju-gun, Jeollabuk-do 55365, Republic of Korea; Department of Biologic and Materials Sciences & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI 48109, USA
| | - Dajung J Kim
- Department of Biologic and Materials Sciences & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI 48109, USA
| | - Thiago D Nascimento
- Department of Biologic and Materials Sciences & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI 48109, USA
| | - Alexandre F DaSilva
- Department of Biologic and Materials Sciences & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI 48109, USA; Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.
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Vescovo E, D'Ausilio A. The too many facets of motor output variability. Comment on "From neural noise to co-adaptability: Rethinking the multifaceted architecture of motor variability" by Casartelli, L., Maronati, C., & Cavallo, A. Phys Life Rev 2024; 50:1-3. [PMID: 38733718 DOI: 10.1016/j.plrev.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024]
Affiliation(s)
- Enrico Vescovo
- Center for Translational Neurophysiology of Speech and Communication, Italian Institute of Technology, Via Fossato di Mortara, 17-19, 44121 Ferrara, Italy; Department of Neuroscience and Rehabilitation, University of Ferrara, Via Fossato di Mortara, 17-19, 44121 Ferrara, Italy
| | - Alessandro D'Ausilio
- Center for Translational Neurophysiology of Speech and Communication, Italian Institute of Technology, Via Fossato di Mortara, 17-19, 44121 Ferrara, Italy; Department of Neuroscience and Rehabilitation, University of Ferrara, Via Fossato di Mortara, 17-19, 44121 Ferrara, Italy
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41
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Terada Y, Toyoizumi T. Chaotic neural dynamics facilitate probabilistic computations through sampling. Proc Natl Acad Sci U S A 2024; 121:e2312992121. [PMID: 38648479 PMCID: PMC11067032 DOI: 10.1073/pnas.2312992121] [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] [Received: 07/28/2023] [Accepted: 02/13/2024] [Indexed: 04/25/2024] Open
Abstract
Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.
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Affiliation(s)
- Yu Terada
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Saitama351-0198, Japan
- Department of Neurobiology, University of California, San Diego, La Jolla, CA92093
- The Institute for Physics of Intelligence, The University of Tokyo, Tokyo113-0033, Japan
| | - Taro Toyoizumi
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Saitama351-0198, Japan
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo113-8656, Japan
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42
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Koren V, Malerba SB, Schwalger T, Panzeri S. Structure, dynamics, coding and optimal biophysical parameters of efficient excitatory-inhibitory spiking networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.590955. [PMID: 38712237 PMCID: PMC11071478 DOI: 10.1101/2024.04.24.590955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuro-science, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we rigorously derive the structural, coding, biophysical and dynamical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. The optimal network has biologically-plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-stimulus-specific excitatory external input regulating metabolic cost. The efficient network has excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implementing feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal biophysical parameters include 4 to 1 ratio of excitatory vs inhibitory neurons and 3 to 1 ratio of mean inhibitory-to-inhibitory vs. excitatory-to-inhibitory connectivity that closely match those of cortical sensory networks. The efficient network has biologically-plausible spiking dynamics, with a tight instantaneous E-I balance that makes them capable to achieve efficient coding of external stimuli varying over multiple time scales. Together, these results explain how efficient coding may be implemented in cortical networks and suggests that key properties of biological neural networks may be accounted for by efficient coding.
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43
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Zobaer MS, Lotfi N, Domenico CM, Hoffman C, Perotti L, Ji D, Dabaghian Y. Theta oscillons in behaving rats. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.21.590487. [PMID: 38712230 PMCID: PMC11071438 DOI: 10.1101/2024.04.21.590487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Recently discovered constituents of the brain waves-the oscillons -provide high-resolution representation of the extracellular field dynamics. Here we study the most robust, highest-amplitude oscillons that manifest in actively behaving rats and generally correspond to the traditional θ -waves. We show that the resemblances between θ -oscillons and the conventional θ -waves apply to the ballpark characteristics-mean frequencies, amplitudes, and bandwidths. In addition, both hippocampal and cortical oscillons exhibit a number of intricate, behavior-attuned, transient properties that suggest a new vantage point for understanding the θ -rhythms' structure, origins and functions. We demonstrate that oscillons are frequency-modulated waves, with speed-controlled parameters, embedded into a noise background. We also use a basic model of neuronal synchronization to contextualize and to interpret the observed phenomena. In particular, we argue that the synchronicity level in physiological networks is fairly weak and modulated by the animal's locomotion.
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44
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Mosberger AC, Sibener LJ, Chen TX, Rodrigues HFM, Hormigo R, Ingram JN, Athalye VR, Tabachnik T, Wolpert DM, Murray JM, Costa RM. Exploration biases forelimb reaching strategies. Cell Rep 2024; 43:113958. [PMID: 38520691 PMCID: PMC11097405 DOI: 10.1016/j.celrep.2024.113958] [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] [Received: 07/10/2023] [Revised: 12/05/2023] [Accepted: 02/28/2024] [Indexed: 03/25/2024] Open
Abstract
The brain can generate actions, such as reaching to a target, using different movement strategies. We investigate how such strategies are learned in a task where perched head-fixed mice learn to reach to an invisible target area from a set start position using a joystick. This can be achieved by learning to move in a specific direction or to a specific endpoint location. As mice learn to reach the target, they refine their variable joystick trajectories into controlled reaches, which depend on the sensorimotor cortex. We show that individual mice learned strategies biased to either direction- or endpoint-based movements. This endpoint/direction bias correlates with spatial directional variability with which the workspace was explored during training. Model-free reinforcement learning agents can generate both strategies with similar correlation between variability during training and learning bias. These results provide evidence that reinforcement of individual exploratory behavior during training biases the reaching strategies that mice learn.
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Affiliation(s)
- Alice C Mosberger
- Departments of Neuroscience and Neurology, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
| | - Leslie J Sibener
- Departments of Neuroscience and Neurology, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Tiffany X Chen
- Departments of Neuroscience and Neurology, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Helio F M Rodrigues
- Departments of Neuroscience and Neurology, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Allen Institute, Seattle, WA 98109, USA
| | - Richard Hormigo
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - James N Ingram
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Vivek R Athalye
- Departments of Neuroscience and Neurology, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Tanya Tabachnik
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Daniel M Wolpert
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - James M Murray
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
| | - Rui M Costa
- Departments of Neuroscience and Neurology, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Allen Institute, Seattle, WA 98109, USA.
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45
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Zobaer MS, Lotfi N, Domenico CM, Hoffman C, Perotti L, Ji D, Dabaghian Y. Theta oscillons in behaving rats. ARXIV 2024:arXiv:2404.13851v1. [PMID: 38711435 PMCID: PMC11071536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Recently discovered constituents of the brain waves-the oscillons-provide high-resolution representation of the extracellular field dynamics. Here we study the most robust, highest-amplitude oscillons that manifest in actively behaving rats and generally correspond to the traditional θ -waves. We show that the resemblances between θ -oscillons and the conventional θ -waves apply to the ballpark characteristics-mean frequencies, amplitudes, and bandwidths. In addition, both hippocampal and cortical oscillons exhibit a number of intricate, behavior-attuned, transient properties that suggest a new vantage point for understanding the θ -rhythms' structure, origins and functions. We demonstrate that oscillons are frequency-modulated waves, with speed-controlled parameters, embedded into a noise background. We also use a basic model of neuronal synchronization to contextualize and to interpret the observed phenomena. In particular, we argue that the synchronicity level in physiological networks is fairly weak and modulated by the animal's locomotion.
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Affiliation(s)
- M. S. Zobaer
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX 77030
| | - N. Lotfi
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX 77030
| | - C. M. Domenico
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030
| | - C. Hoffman
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX 77030
| | - L. Perotti
- Department of Physics, Texas Southern University, 3100 Cleburne Ave., Houston, Texas 77004
| | - D. Ji
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030
| | - Y. Dabaghian
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX 77030
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46
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Menéndez JA, Hennig JA, Golub MD, Oby ER, Sadtler PT, Batista AP, Chase SM, Yu BM, Latham PE. A theory of brain-computer interface learning via low-dimensional control. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.589952. [PMID: 38712193 PMCID: PMC11071278 DOI: 10.1101/2024.04.18.589952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
A remarkable demonstration of the flexibility of mammalian motor systems is primates' ability to learn to control brain-computer interfaces (BCIs). This constitutes a completely novel motor behavior, yet primates are capable of learning to control BCIs under a wide range of conditions. BCIs with carefully calibrated decoders, for example, can be learned with only minutes to hours of practice. With a few weeks of practice, even BCIs with randomly constructed decoders can be learned. What are the biological substrates of this learning process? Here, we develop a theory based on a re-aiming strategy, whereby learning operates within a low-dimensional subspace of task-relevant inputs driving the local population of recorded neurons. Through comprehensive numerical and formal analysis, we demonstrate that this theory can provide a unifying explanation for disparate phenomena previously reported in three different BCI learning tasks, and we derive a novel experimental prediction that we verify with previously published data. By explicitly modeling the underlying neural circuitry, the theory reveals an interpretation of these phenomena in terms of biological constraints on neural activity.
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47
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Tessari F, Hermus J, Sugimoto-Dimitrova R, Hogan N. Brownian processes in human motor control support descending neural velocity commands. Sci Rep 2024; 14:8341. [PMID: 38594312 PMCID: PMC11004188 DOI: 10.1038/s41598-024-58380-5] [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] [Received: 11/30/2023] [Accepted: 03/28/2024] [Indexed: 04/11/2024] Open
Abstract
The motor neuroscience literature suggests that the central nervous system may encode some motor commands in terms of velocity. In this work, we tackle the question: what consequences would velocity commands produce at the behavioral level? Considering the ubiquitous presence of noise in the neuromusculoskeletal system, we predict that velocity commands affected by stationary noise would produce "random walks", also known as Brownian processes, in position. Brownian motions are distinctively characterized by a linearly growing variance and a power spectral density that declines in inverse proportion to frequency. This work first shows that these Brownian processes are indeed observed in unbounded motion tasks e.g., rotating a crank. We further predict that such growing variance would still be present, but bounded, in tasks requiring a constant posture e.g., maintaining a static hand position or quietly standing. This hypothesis was also confirmed by experimental observations. A series of descriptive models are investigated to justify the observed behavior. Interestingly, one of the models capable of accounting for all the experimental results must feature forward-path velocity commands corrupted by stationary noise. The results of this work provide behavioral support for the hypothesis that humans plan the motion components of their actions in terms of velocity.
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Affiliation(s)
- Federico Tessari
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - James Hermus
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Rika Sugimoto-Dimitrova
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Neville Hogan
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
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48
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Li C, Qiu J, Huang H. Meta predictive learning model of languages in neural circuits. Phys Rev E 2024; 109:044309. [PMID: 38755909 DOI: 10.1103/physreve.109.044309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 03/18/2024] [Indexed: 05/18/2024]
Abstract
Large language models based on self-attention mechanisms have achieved astonishing performances, not only in natural language itself, but also in a variety of tasks of different nature. However, regarding processing language, our human brain may not operate using the same principle. Then, a debate is established on the connection between brain computation and artificial self-supervision adopted in large language models. One of most influential hypotheses in brain computation is the predictive coding framework, which proposes to minimize the prediction error by local learning. However, the role of predictive coding and the associated credit assignment in language processing remains unknown. Here, we propose a mean-field learning model within the predictive coding framework, assuming that the synaptic weight of each connection follows a spike and slab distribution, and only the distribution, rather than specific weights, is trained. This meta predictive learning is successfully validated on classifying handwritten digits where pixels are input to the network in sequence, and moreover, on the toy and real language corpus. Our model reveals that most of the connections become deterministic after learning, while the output connections have a higher level of variability. The performance of the resulting network ensemble changes continuously with data load, further improving with more training data, in analogy with the emergent behavior of large language models. Therefore, our model provides a starting point to investigate the connection among brain computation, next-token prediction, and general intelligence.
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Affiliation(s)
- Chan Li
- PMI Laboratory, School of Physics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China
- Department of Physics, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Junbin Qiu
- PMI Laboratory, School of Physics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China
| | - Haiping Huang
- PMI Laboratory, School of Physics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China
- Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, Sun Yat-sen University, Guangzhou 510275, People's Republic of China
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49
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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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50
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Kayser C, Heuer H. Multisensory perception depends on the reliability of the type of judgment. J Neurophysiol 2024; 131:723-737. [PMID: 38416720 DOI: 10.1152/jn.00451.2023] [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] [Received: 12/06/2023] [Revised: 02/05/2024] [Accepted: 02/24/2024] [Indexed: 03/01/2024] Open
Abstract
The brain engages the processes of multisensory integration and recalibration to deal with discrepant multisensory signals. These processes consider the reliability of each sensory input, with the more reliable modality receiving the stronger weight. Sensory reliability is typically assessed via the variability of participants' judgments, yet these can be shaped by factors both external and internal to the nervous system. For example, motor noise and participant's dexterity with the specific response method contribute to judgment variability, and different response methods applied to the same stimuli can result in different estimates of sensory reliabilities. Here we ask how such variations in reliability induced by variations in the response method affect multisensory integration and sensory recalibration, as well as motor adaptation, in a visuomotor paradigm. Participants performed center-out hand movements and were asked to judge the position of the hand or rotated visual feedback at the movement end points. We manipulated the variability, and thus the reliability, of repeated judgments by asking participants to respond using either a visual or a proprioceptive matching procedure. We find that the relative weights of visual and proprioceptive signals, and thus the asymmetry of multisensory integration and recalibration, depend on the reliability modulated by the judgment method. Motor adaptation, in contrast, was insensitive to this manipulation. Hence, the outcome of multisensory binding is shaped by the noise introduced by sensorimotor processing, in line with perception and action being intertwined.NEW & NOTEWORTHY Our brain tends to combine multisensory signals based on their respective reliability. This reliability depends on sensory noise in the environment, noise in the nervous system, and, as we show here, variability induced by the specific judgment procedure.
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Affiliation(s)
- Christoph Kayser
- Department of Cognitive Neuroscience, Universität Bielefeld, Bielefeld, Germany
| | - Herbert Heuer
- Department of Cognitive Neuroscience, Universität Bielefeld, Bielefeld, Germany
- Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany
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