Long-Term Visual Memory and Its Role in Learning Suppression

Long-term memory is a core aspect of human learning that permits a wide range of skills and behaviors often important for survival. While this core ability has been broadly observed for procedural and declarative memory, whether similar mechanisms subserve basic sensory or perceptual processes remains unclear. Here, we use a visual learning paradigm to show that training humans to search for common visual features in the environment leads to a persistent improvement in performance over consecutive days but, surprisingly, suppresses the subsequent ability to learn similar visual features. This suppression is reversed if the memory is prevented from consolidating, while still permitting the ability to learn multiple visual features simultaneously. These findings reveal a memory mechanism that may enable salient sensory patterns to persist in memory over prolonged durations, but which also functions to prevent false-positive detection by proactively suppressing new learning

Unveiling Theory of Mind in Large Language Models: A Parallel to Single Neurons in the Human Brain

With their recent development, large language models (LLMs) have been found to exhibit a certain level of Theory of Mind (ToM), a complex cognitive capacity that is related to our conscious mind and that allows us to infer another's beliefs and perspective. While human ToM capabilities are believed to derive from the neural activity of a broadly interconnected brain network, including that of dorsal medial prefrontal cortex (dmPFC) neurons, the precise processes underlying LLM's capacity for ToM or their similarities with that of humans remains largely unknown. In this study, we drew inspiration from the dmPFC neurons subserving human ToM and employed a similar methodology to examine whether LLMs exhibit comparable characteristics. Surprisingly, our analysis revealed a striking resemblance between the two, as hidden embeddings (artificial neurons) within LLMs started to exhibit significant responsiveness to either true- or false-belief trials, suggesting their ability to represent another's perspective. These artificial embedding responses were closely correlated with the LLMs' performance during the ToM tasks, a property that was dependent on the size of the models. Further, the other's beliefs could be accurately decoded using the entire embeddings, indicating the presence of the embeddings' ToM capability at the population level. Together, our findings revealed an emergent property of LLMs' embeddings that modified their activities in response to ToM features, offering initial evidence of a parallel between the artificial model and neurons in the human brain

A cortical-spinal prosthesis for targeted limb movement in paralyzed primate avatars

Motor paralysis is among the most disabling aspects of injury to the central nervous system. Here we develop and test a target-based cortical-spinal neural prosthesis that employs neural activity recorded from pre-motor neurons to control limb movements in functionally paralyzed primate avatars. Given the complexity by which muscle contractions are naturally controlled, we approach the problem of eliciting goal-directed limb movement in paralyzed animals by focusing on the intended targets of movement rather than their intermediate trajectories. We then match this information in real-time with spinal cord and muscle stimulation parameters that produce free planar limb movements to those intended target locations. We demonstrate that both the decoded activities of pre-motor populations and their adaptive responses can be used, after brief training, to effectively direct an avatar’s limb to distinct targets variably displayed on a screen. These findings advance the future possibility of reconstituting targeted limb movement in paralyzed subjects

Prediction of Maximal Heart Rate in Children and Adolescents.

OBJECTIVE: To identify a method to predict the maximal heart rate (MHR) in children and adolescents, as available prediction equations developed for adults have a low accuracy in children. We hypothesized that MHR may be influenced by resting heart rate, anthropometric factors, or fitness level. DESIGN: Cross-sectional study. SETTING: Sports medicine center in primary care. PARTICIPANTS: Data from 627 treadmill maximal exercise tests performed by 433 pediatric athletes (age 13.7 ± 2.1 years, 70% males) were analyzed. INDEPENDENT VARIABLES: Age, sex, sport type, stature, body mass, BMI, body fat, fitness level, resting, and MHR were recorded. MAIN OUTCOME MEASURES: To develop a prediction equation for MHR in youth, using stepwise multivariate linear regression and linear mixed model. To determine correlations between existing prediction equations and pediatric MHR. RESULTS: Observed MHR was 197 ± 8.6 b·min. Regression analysis revealed that resting heart rate, fitness, body mass, and fat percent were predictors of MHR (R = 0.25, P < 0.001), whereas age was not. Resting heart rate explained 15.6% of MHR variance, body mass added 5.7%, fat percent added 2.4%, and fitness added 1.2%. Existing adult equations had low correlations with observed MHR in children and adolescents (r = -0.03-0.34). CONCLUSIONS: A new equation to predict MHR in children and adolescents was developed, but was found to have low predictive ability, a finding similar to adult equations applied to children. CLINICAL RELEVANCE: Considering the narrow range of MHR in youth, we propose using 197 b·min as the mean MHR in children and adolescents, with 180 b·min the minimal threshold value (-2 standard deviations)

Neural population partitioning and a concurrent brain-machine interface for sequential motor function

While brain-machine interfaces (BMIs) have largely focused on performing single-targeted movements, many natural tasks involve planning a complete sequence of such movements before execution. For these tasks, a BMI that can concurrently decode the full planned sequence prior to its execution may also consider the higher-level goal of the task to reformulate and perform it more effectively. Here, we show that concurrent BMI decoding is possible. Using population-wide modeling, we discover two distinct subpopulations of neurons in the rhesus monkey premotor cortex that allow two planned targets of a sequential movement to be simultaneously held in working memory without degradation. Such surprising stability occurred because each subpopulation encoded either only currently held or only newly added target information irrespective of the exact sequence. Based on these findings, we develop a BMI that concurrently decodes a full motor sequence in advance of movement and then can accurately execute it as desired

Neural population partitioning and a concurrent brain-machine interface for sequential motor function

Although brain-machine interfaces (BMIs) have focused largely on performing single-targeted movements, many natural tasks involve planning a complete sequence of such movements before execution. For these tasks, a BMI that can concurrently decode the full planned sequence before its execution may also consider the higher-level goal of the task to reformulate and perform it more effectively. Using population-wide modeling, we discovered two distinct subpopulations of neurons in the rhesus monkey premotor cortex that allow two planned targets of a sequential movement to be simultaneously held in working memory without degradation. Such marked stability occurred because each subpopulation encoded either only currently held or only newly added target information irrespective of the exact sequence. On the basis of these findings, we developed a BMI that concurrently decodes a full motor sequence in advance of movement and can then accurately execute it as desired.National Institutes of Health (U.S.) (DP1 OD003646

A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design

Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.National Institutes of Health (U.S.) (NIH grant No.DP1-0D003646-01)National Institutes of Health (U.S.) (NIH grant R01-EB006385

Spine surgeon specialty differences in single-level percutaneous kyphoplasty

Background Percutaneous kyphoplasty (PKP) is a procedure performed by a spine surgeon who undergoes either orthopedic or neurosurgical training. The relationship between short-term adverse outcomes and spine specialty is presently unknown. To compare short-term adverse outcomes of single-level PKP when performed by neurosurgeons and orthopedic surgeons in order to develop more concretely preventive strategies for patients under consideration for single-level PKP. Methods We evaluated patients who underwent single-level PKP from 2012 to 2014 through the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). We used univariate analysis and multivariate logistic regression to assess the association between spine surgeon specialty and short-term adverse events, including postoperative complication and unplanned readmission, and to identify different independent risk predictors between two specialties. Results Of 2248 patients who underwent single-level PKP procedure, 1229 patients (54.7%) had their operations completed by a neurosurgeon. There were no significant differences in the development of the majority of postoperative complications and the occurrence of unplanned readmission between the neurosurgical cohort (NC) and the orthopedic cohort (OC). A difference in the postoperative blood transfusion rate (0.7% NS vs. 1.7% OC, P = 0.039) was noted and may due to the differences in comorbidities between patients. Multivariate regression analysis revealed different independent predictors of postoperative adverse events for the two spine specialties. Conclusions By comparing a large range of demographic feature, preoperative comorbidities, and intraoperative factors, we find that short-term adverse events in single-level PKP patients does not affect by spine surgeon specialty, except that the OC had higher postoperative blood transfusion rate. In addition, the different perioperative predictors of postoperative complications and unplanned readmissions were identified between the two specialties. These findings can lead to better evidence-based patient counseling and provide valuable information for medical evaluation and potentially devise methods to reduce patients’ risk

Cellular Radiosensitivity: How much better do we understand it?

Purpose: Ionizing radiation exposure gives rise to a variety of lesions in DNA that result in genetic instability and potentially tumorigenesis or cell death. Radiation extends its effects on DNA by direct interaction or by radiolysis of H2O that generates free radicals or aqueous electrons capable of interacting with and causing indirect damage to DNA. While the various lesions arising in DNA after radiation exposure can contribute to the mutagenising effects of this agent, the potentially most damaging lesion is the DNA double strand break (DSB) that contributes to genome instability and/or cell death. Thus in many cases failure to recognise and/or repair this lesion determines the radiosensitivity status of the cell. DNA repair mechanisms including homologous recombination (HR) and non-homologous end-joining (NHEJ) have evolved to protect cells against DNA DSB. Mutations in proteins that constitute these repair pathways are characterised by radiosensitivity and genome instability. Defects in a number of these proteins also give rise to genetic disorders that feature not only genetic instability but also immunodeficiency, cancer predisposition, neurodegeneration and other pathologies. Conclusions: In the past fifty years our understanding of the cellular response to radiation damage has advanced enormously with insight being gained from a wide range of approaches extending from more basic early studies to the sophisticated approaches used today. In this review we discuss our current understanding of the impact of radiation on the cell and the organism gained from the array of past and present studies and attempt to provide an explanation for what it is that determines the response to radiation