New neural activity patterns emerge with long-term learning.
Ontology highlight
ABSTRACT:
SUBMITTER: Oby ER
PROVIDER: S-EPMC6660765 | biostudies-literature | 2019 Jul
REPOSITORIES: biostudies-literature
Publications
New neural activity patterns emerge with long-term learning.
Oby Emily R ER Golub Matthew D MD Hennig Jay A JA Degenhart Alan D AD Tyler-Kabara Elizabeth C EC Yu Byron M BM Chase Steven M SM Batista Aaron P AP
Proceedings of the National Academy of Sciences of the United States of America 20190610 30
Learning has been associated with changes in the brain at every level of organization. However, it remains difficult to establish a causal link between specific changes in the brain and new behavioral abilities. We establish that new neural activity patterns emerge with learning. We demonstrate that these new neural activity patterns cause the new behavior. Thus, the formation of new patterns of neural population activity can underlie the learning of new skills. ...[more]
Similar Datasets
Project description:What are the neural mechanisms of skill acquisition? Many studies find that long-term practice is associated with a functional reorganization of cortical neural activity. However, the link between these changes in neural activity and the behavioral improvements that occur is not well understood, especially for long-term learning that takes place over several weeks. To probe this link in detail, we leveraged a brain-computer interface (BCI) paradigm in which rhesus monkeys learned to master nonintuitive mappings between neural spiking in primary motor cortex and computer cursor movement. Critically, these BCI mappings were designed to disambiguate several different possible types of neural reorganization. We found that during the initial phase of learning, lasting minutes to hours, rapid changes in neural activity common to all neurons led to a fast suppression of motor error. In parallel, local changes to individual neurons gradually accrued over several weeks of training. This slower timescale cortical reorganization persisted long after the movement errors had decreased to asymptote and was associated with more efficient control of movement. We conclude that long-term practice evokes two distinct neural reorganization processes with vastly different timescales, leading to different aspects of improvement in motor behavior. NEW & NOTEWORTHY We leveraged a brain-computer interface learning paradigm to track the neural reorganization occurring throughout the full time course of motor skill learning lasting several weeks. We report on two distinct types of neural reorganization that mirror distinct phases of behavioral improvement: a fast phase, in which global reorganization of neural recruitment leads to a quick suppression of motor error, and a slow phase, in which local changes in individual tuning lead to improvements in movement efficiency.
| S-EPMC6485743 | biostudies-literature
Project description:Past experience provides a rich source of predictive information about the world that could be used to guide and optimize ongoing perception. However, the neural mechanisms that integrate information coded in long-term memory (LTM) with ongoing perceptual processing remain unknown. Here, we explore how the contents of LTM optimize perception by modulating anticipatory brain states. By using a paradigm that integrates LTM and attentional orienting, we first demonstrate that the contents of LTM sharpen perceptual sensitivity for targets presented at memory-predicted spatial locations. Next, we examine oscillations in EEG to show that memory-guided attention is associated with spatially specific desynchronization of alpha-band activity over visual cortex. Additionally, we use functional MRI to confirm that target-predictive spatial information stored in LTM triggers spatiotopic modulation of preparatory activity in extrastriate visual cortex. Finally, functional MRI results also implicate an integrated cortical network, including the hippocampus and a dorsal frontoparietal circuit, as a likely candidate for organizing preparatory states in visual cortex according to the contents of LTM.
| S-EPMC3277524 | biostudies-literature
Project description:Mammalian cortex has both local and cross-area connections, suggesting vital roles for both local and cross-area neural population dynamics in cortically-dependent tasks, like movement learning. Prior studies of movement learning have focused on how single-area population dynamics change during short-term adaptation. It is unclear how cross-area dynamics contribute to movement learning, particularly long-term learning and skill acquisition. Using simultaneous recordings of rodent motor (M1) and premotor (M2) cortex and computational methods, we show how cross-area activity patterns evolve during reach-to-grasp learning in rats. The emergence of reach-related modulation in cross-area activity correlates with skill acquisition, and single-trial modulation in cross-area activity predicts reaction time and reach duration. Local M2 neural activity precedes local M1 activity, supporting top-down hierarchy between the regions. M2 inactivation preferentially affects cross-area dynamics and behavior, with minimal disruption of local M1 dynamics. Together, these results indicate that cross-area population dynamics are necessary for learned motor skills.
| S-EPMC7426952 | biostudies-literature
Project description:Animals retain some but not all experiences in long-term memory (LTM). Sleep supports LTM retention across animal species. It is well established that learning experiences enhance post-learning sleep. However, the underlying mechanisms of how learning mediates sleep for memory retention are not clear. Drosophila males display increased amounts of sleep after courtship learning. Courtship learning depends on Mushroom Body (MB) neurons, and post-learning sleep is mediated by the sleep-promoting ventral Fan-Shaped Body neurons (vFBs). We show that post-learning sleep is regulated by two opposing output neurons (MBONs) from the MB, which encode a measure of learning. Excitatory MBONs-γ2α'1 becomes increasingly active upon increasing time of learning, whereas inhibitory MBONs-β'2mp is activated only by a short learning experience. These MB outputs are integrated by SFS neurons, which excite vFBs to promote sleep after prolonged but not short training. This circuit may ensure that only longer or more intense learning experiences induce sleep and are thereby consolidated into LTM.
| S-EPMC8807839 | biostudies-literature
Project description:Humans are sophisticated social beings. Social cues from others are exceptionally salient, particularly during adolescence. Understanding how adolescents interpret and learn from variable social signals can provide insight into the observed shift in social sensitivity during this period. The present study tested 120 participants between the ages of 8 and 25 years on a social reinforcement learning task where the probability of receiving positive social feedback was parametrically manipulated. Seventy-eight of these participants completed the task during fMRI scanning. Modeling trial-by-trial learning, children and adults showed higher positive learning rates than did adolescents, suggesting that adolescents demonstrated less differentiation in their reaction times for peers who provided more positive feedback. Forming expectations about receiving positive social reinforcement correlated with neural activity within the medial prefrontal cortex and ventral striatum across age. Adolescents, unlike children and adults, showed greater insular activity during positive prediction error learning and increased activity in the supplementary motor cortex and the putamen when receiving positive social feedback regardless of the expected outcome, suggesting that peer approval may motivate adolescents toward action. While different amounts of positive social reinforcement enhanced learning in children and adults, all positive social reinforcement equally motivated adolescents. Together, these findings indicate that sensitivity to peer approval during adolescence goes beyond simple reinforcement theory accounts and suggest possible explanations for how peers may motivate adolescent behavior.
| S-EPMC4127887 | biostudies-literature
Project description:The platypus is a semi-aquatic mammal, endemic to freshwater habitats of eastern Australia. There are gaps in the understanding of platypus movement behaviour within river systems, including spatial and temporal organization of individuals. We tracked movements of 12 platypuses on the regulated Snowy and Mitta Mitta Rivers for up to 12-months, the longest continuous tracking of platypus using acoustic telemetry. Platypuses remained relatively localized, occupying 0.73-8.45 km of river over 12 months, consistent with previous tracking studies over shorter periods. Males moved further than females, and larger males had higher cumulative movements, suggesting a possible relationship to metabolic requirements. Platypuses moved greater distances on the Mitta Mitta River, possibly associated with impacts of altered flow regimes to their macroinvertebrate diet. Increased movements and diurnal activity during winter were primarily driven by males, possibly attributable to breeding behaviours, rather than increased costs of winter foraging. Evidence for relatively small movements has implications for declining populations, given areas of localised declines are unlikely to be supplemented by migrating platypuses, especially when dispersal is restricted by dam walls. Understanding platypus movement behaviour is pertinent for their conservation, as water resource development and habitat modification continue to reduce connectivity between populations across their distribution.
| S-EPMC7878892 | biostudies-literature
Project description:PURPOSE:To examine the effect of a long-term structured physical activity (PA) intervention on accelerometer-derived metrics of activity pattern changes in mobility-impaired older adults. METHODS:Participants were randomized to either a PA or health education (HE) program. The PA intervention included a walking regimen with strength, flexibility, and balance training. The HE program featured health-related discussions and a brief upper body stretching routine. Participants (n = 1341) wore a hip-worn accelerometer for ?10 h·d for ?3 d at baseline and again at 6, 12, and 24 months postrandomization. Total PA (TPA)-defined as movements registering 100+ counts per minute-was segmented into the following intensities: low-light PA (LLPA; 100-759 counts per minute), high light PA (HLPA; 760-1040 counts per minute), low moderate PA (LMPA; 1041-2019 counts per minute), and high moderate and greater PA (HMPA; 2020+ counts per minute). Patterns of activity were characterized as bouts (defined as the consecutive minutes within an intensity). RESULTS:Across groups, TPA decreased an average of 74 min·wk annually. The PA intervention attenuated this effect (PA = -68 vs HE: -112 min·wk, P = 0.002). This attenuation shifted TPA composition by increasing time in LLPA (10+ bouts increased 6 min·wk), HLPA (1+, 2+, 5+, and 10+ bouts increased 6, 3, 2, and 1 min·wk, respectively), LMPA (1+, 2+, 5+, and 10+ bouts increased: 19, 17,16, and 8 min·wk, respectively), and HMPA (1+, 2+, 5+, and 10+ bouts increased 23, 21, 17, and 14 min·wk, respectively). CONCLUSIONS:The PA intervention increased PA by shifting the composition of activity toward higher-intensity activity in longer-duration bouts. However, a long-term structured PA intervention did not completely eliminate overall declines in total daily activity experienced by mobility-impaired older adults.
| S-EPMC5653284 | biostudies-literature
Project description:Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.
| S-EPMC5619984 | biostudies-literature
Project description:BackgroundThe accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.MethodsFirst, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager-Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis.ResultsThe proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR.ConclusionsThe presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/distinction of muscle activity from background noise in sEMG signals.
| S-EPMC8532313 | biostudies-literature
Project description:BackgroundBrain-computer interfaces (BCIs) are systems capable of translating human brain patterns, measured through electroencephalography (EEG), into commands for an external device. Despite the great advances in machine learning solutions to enhance the performance of BCI decoders, the translational impact of this technology remains elusive. The reliability of BCIs is often unsatisfactory for end-users, limiting their application outside a laboratory environment.MethodsWe present the analysis on the data acquired from an end-user during the preparation for two Cybathlon competitions, where our pilot won the gold medal twice in a row. These data are of particular interest given the mutual learning approach adopted during the longitudinal training phase (8 months), the long training break in between the two events (1 year) and the demanding evaluation scenario. A multifaceted perspective on long-term user learning is proposed: we enriched the information gathered through conventional metrics (e.g., accuracy, application performances) by investigating novel neural correlates of learning in different neural domains.ResultsFirst, we showed that by focusing the training on user learning, the pilot was capable of significantly improving his performance over time even with infrequent decoder re-calibrations. Second, we revealed that the analysis of the within-class modifications of the pilot's neural patterns in the Riemannian domain is more effective in tracking the acquisition and the stabilization of BCI skills, especially after the 1-year break. These results further confirmed the key role of mutual learning in the acquisition of BCI skills, and particularly highlighted the importance of user learning as a key to enhance BCI reliability.ConclusionWe firmly believe that our work may open new perspectives and fuel discussions in the BCI field to shift the focus of future research: not only to the machine learning of the decoder, but also in investigating novel training procedures to boost the user learning and the stability of the BCI skills in the long-term. To this end, the analyses and the metrics proposed could be used to monitor the user learning during training and provide a marker guiding the decoder re-calibration to maximize the mutual adaptation of the user to the BCI system.
| S-EPMC9254548 | biostudies-literature