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We present a successful replication of Ebbinghaus' classic forgetting curve from 1880 based on the method of savings. One subject spent 70 hours learning lists and relearning them after 20 min, 1 hour, 9 hours, 1 day, 2 days, or 31 days. The results are similar to Ebbinghaus' original data. We analyze the effects of serial position on forgetting and investigate what mathematical equations present a good fit to the Ebbinghaus forgetting curve and its replications. We conclude that the Ebbinghaus forgetting curve has indeed been replicated and that it is not completely smooth but most probably shows a jump upwards starting at the 24 hour data point.

Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapting to new data. Memory-augmented neural networks enhance neural networks with an explicit memory to overcome these issues. Access to this explicit memory, however, occurs via soft read and write operations involving every individual memory entry, resulting in a bottleneck when implemented using the conventional von Neumann computer architecture. To overcome this bottleneck, we propose a robust architecture that employs a computational memory unit as the explicit memory performing analog in-memory computation on high-dimensional (HD) vectors, while closely matching 32-bit software-equivalent accuracy. This is achieved by a content-based attention mechanism that represents unrelated items in the computational memory with uncorrelated HD vectors, whose real-valued components can be readily approximated by binary, or bipolar components. Experimental results demonstrate the efficacy of our approach on few-shot image classification tasks on the Omniglot dataset using more than 256,000 phase-change memory devices. Our approach effectively merges the richness of deep neural network representations with HD computing that paves the way for robust vector-symbolic manipulations applicable in reasoning, fusion, and compression. The implementation of memory-augmented neural networks using conventional computer architectures is challenging due to a large number of read and write operations. Here, Karunaratne, Schmuck et al. propose an architecture that enables analog in-memory computing on high-dimensional vectors at accuracy matching 32-bit software equivalent.

Bilateral rehabilitation systems with bilateral or unilateral assistive robots have been developed for hemiplegia patients to recover their one-side paralysis. However, the compliant robotic assistance to promote bilateral inter-limb coordination remains a challenge that should be addressed. In this paper, a biomimetic variable stiffness modulation strategy for the Variable Stiffness Actuator (VSA) integrated robotic is proposed to improve bilateral limb coordination and promote bilateral motor skills relearning. An Electromyography (EMG)-driven synergy reference stiffness estimation model of the upper limb elbow joint is developed to reproduce the muscle synergy effect on the affected side limb by independent real-time stiffness control. Additionally, the bilateral impedance control is incorporated for realizing compliant patient–robot interaction. Preliminary experiments were carried out to evaluate the tracking performance and investigate the multiple task intensities’ influence on bilateral motor skills relearning. Experimental results evidence the proposed method could enable bilateral motor task skills relearning with wide-range task intensities and further promote bilateral inter-limb coordination.

Brain–computer interfaces (BCIs) enable control of assistive devices in individuals with severe motor impairments. A limitation of BCIs that has hindered real-world adoption is poor long-term reliability and lengthy daily recalibration times. To develop methods that allow stable performance without recalibration, we used a 128-channel chronic electrocorticography (ECoG) implant in a paralyzed individual, which allowed stable monitoring of signals. We show that long-term closed-loop decoder adaptation, in which decoder weights are carried across sessions over multiple days, results in consolidation of a neural map and ‘plug-and-play’ control. In contrast, daily reinitialization led to degradation of performance with variable relearning. Consolidation also allowed the addition of control features over days, that is, long-term stacking of dimensions. Our results offer an approach for reliable, stable BCI control by leveraging the stability of ECoG interfaces and neural plasticity. A paralyzed individual controls a neuroprosthetic without daily recalibration.

The brain’s remarkable ability to learn and execute various motor behaviours harnesses the capacity of neural populations to generate a variety of activity patterns. Here we explore systematic changes in preparatory activity in motor cortex that accompany motor learning. We trained rhesus monkeys to learn an arm-reaching task 1 in a curl force field that elicited new muscle forces for some, but not all, movement directions 2 , 3 . We found that in a neural subspace predictive of hand forces, changes in preparatory activity tracked the learned behavioural modifications and reassociated 4 existing activity patterns with updated movements. Along a neural population dimension orthogonal to the force-predictive subspace, we discovered that preparatory activity shifted uniformly for all movement directions, including those unaltered by learning. During a washout period when the curl field was removed, preparatory activity gradually reverted in the force-predictive subspace, but the uniform shift persisted. These persistent preparatory activity patterns may retain a motor memory of the learned field 5 , 6 and support accelerated relearning of the same curl field. When a set of distinct curl fields was learned in sequence, we observed a corresponding set of field-specific uniform shifts which separated the associated motor memories in the neural state space 7 – 9 . The precise geometry of these uniform shifts in preparatory activity could serve to index motor memories, facilitating the acquisition, retention and retrieval of a broad motor repertoire. In rhesus monkeys, learning of a motor task is accompanied by uniform changes in preparatory activity in motor cortex that are orthogonal to the force-predictive neural state subspace.

The trafficking of AMPA receptors to and from the surface of postsynaptic membranes regulates synaptic strength and underlies learning and memory. Awasthi et al. found that the integral membrane protein synaptotagmin-3 (Syt3) is predominantly found on postsynaptic endocytic zones of neurons, where it promotes AMPA receptor internalization (see the Perspective by Mandelberg and Tsien). In Syt3 overexpressing or knockdown neurons, synaptic transmission and short-term plasticity were unchanged. However, in neurons from Syt3 knockout mice, synaptic long-term depression was abolished and decaying long-term potentiation endured. In Syt3 knockout mice, spatial learning was unaltered; however, these animals showed signs of impaired forgetting and relearning during the water maze spatial memory task. Science , this issue p. eaav1483 ; see also p. 31 In mice, the neuronal membrane trafficking protein synaptotagmin-3 is involved in learning processes that require forgetting. Forgetting is important. Without it, the relative importance of acquired memories in a changing environment is lost. We discovered that synaptotagmin-3 (Syt3) localizes to postsynaptic endocytic zones and removes AMPA receptors from synaptic plasma membranes in response to stimulation. AMPA receptor internalization, long-term depression (LTD), and decay of long-term potentiation (LTP) of synaptic strength required calcium-sensing by Syt3 and were abolished through Syt3 knockout. In spatial memory tasks, mice in which Syt3 was knocked out learned normally but exhibited a lack of forgetting. Disrupting Syt3:GluA2 binding in a wild-type background mimicked the lack of LTP decay and lack of forgetting, and these effects were occluded in the Syt3 knockout background. Our findings provide evidence for a molecular mechanism in which Syt3 internalizes AMPA receptors to depress synaptic strength and promote forgetting.

The motor system demonstrates an exquisite ability to adapt to changes in the environment and to quickly reset when these changes prove transient. If similar environmental changes are encountered in the future, learning may be faster, a phenomenon known as savings. In studies of sensorimotor learning, a central component of savings is attributed to the explicit recall of the task structure and appropriate compensatory strategies. Whether implicit adaptation also contributes to savings remains subject to debate. We tackled this question by measuring, in parallel, explicit and implicit adaptive responses in a visuomotor rotation task, employing a protocol that typically elicits savings. While the initial rate of learning was faster in the second exposure to the perturbation, an analysis decomposing the 2 processes showed the benefit to be solely associated with explicit re-aiming. Surprisingly, we found a significant decrease after relearning in aftereffect magnitudes during no-feedback trials, a direct measure of implicit adaptation. In a second experiment, we isolated implicit adaptation using clamped visual feedback, a method known to eliminate the contribution of explicit learning processes. Consistent with the results of the first experiment, participants exhibited a marked reduction in the adaptation function, as well as an attenuated aftereffect when relearning from the clamped feedback. Motivated by these results, we reanalyzed data from prior studies and observed a consistent, yet unappreciated pattern of attenuation of implicit adaptation during relearning. These results indicate that explicit and implicit sensorimotor processes exhibit opposite effects upon relearning: Explicit learning shows savings, while implicit adaptation becomes attenuated

The ongoing COVID-19 pandemic has caused more than 193,825 deaths during the past few months. A quick-to-be-identified cure for the disease will be a therapeutic medicine that has prior use experiences in patients in order to resolve the current pandemic situation before it could become worsening. Artificial intelligence (AI) technology is hereby applied to identify the marketed drugs with potential for treating COVID-19. An AI platform was established to identify potential old drugs with anti-coronavirus activities by using two different learning databases; one consisted of the compounds reported or proven active against SARS-CoV, SARS-CoV-2, human immunodeficiency virus, influenza virus, and the other one containing the known 3C-like protease inhibitors. All AI predicted drugs were then tested for activities against a feline coronavirus in cell-based assay. These assay results were feedbacks to the AI system for relearning and thus to generate a modified AI model to search for old drugs again. After a few runs of AI learning and prediction processes, the AI system identified 80 marketed drugs with potential. Among them, 8 drugs (bedaquiline, brequinar, celecoxib, clofazimine, conivaptan, gemcitabine, tolcapone, and vismodegib) showed activities against the proliferation of a feline infectious peritonitis (FIP) virus in Fcwf-4 cells. In addition, 5 other drugs (boceprevir, chloroquine, homoharringtonine, tilorone, and salinomycin) were also found active during the exercises of AI approaches. Having taken advantages of AI, we identified old drugs with activities against FIP coronavirus. Further studies are underway to demonstrate their activities against SARS-CoV-2 and at clinically achievable concentrations and doses. With prior use experiences in patients, these old drugs if proven active against SARS-CoV-2 can readily be applied for fighting COVID-19 pandemic.

Integration of sensory and motor information is one-step, among others, that underlies the successful production of goal-directed hand movements necessary for interacting with our environment. Disruption of sensorimotor integration is prevalent in many neurologic disorders, including stroke. In most stroke survivors, persistent paresis of the hand reduces function and overall quality of life. Current rehabilitative methods are based on neuroplastic principles to promote motor learning that focuses on regaining motor function lost due to paresis, but the sensory contributions to motor control and learning are often overlooked and currently understudied. There is a need to evaluate and understand the contribution of both sensory and motor function in the rehabilitation of skilled hand movements after stroke. Here, we will highlight the importance of integration of sensory and motor information to produce skilled hand movements in healthy individuals and individuals after stroke. We will then discuss how compromised sensorimotor integration influences relearning of skilled hand movements after stroke. Finally, we will propose an approach to target sensorimotor integration through manipulation of sensory input and motor output that may have therapeutic implications.

Purpose – The purpose of this paper is to propose a model to explore the dynamic process of knowledge management from the perspectives of organizational unlearning and organizational relearning, which promote a favorable context for knowledge management. Design/methodology/approach – The model is proposed based on extensive review of literatures. According to this model, the evolutions of organizational unlearning and organizational relearning are separately analyzed, and the interactions between them are revealed. Findings – Organizational unlearning and organizational relearning are the indispensable factors to the dynamic knowledge management. Organizational unlearning positively affects the dynamic knowledge management by discarding the outdated and useless knowledge, while organizational relearning has a positive influence on the dynamic knowledge management by acquiring the new knowledge. Organizational unlearning and organizational relearning have synergies on the dynamic knowledge management. Research limitations/implications – This paper theoretically illuminates the relationships among organizational unlearning, organizational relearning and knowledge management, and doesn't offer an empirical test. Practical implications – This paper will provide insights to practitioners to better understand the dynamic process of knowledge management. The practitioners need to provide favorable context to ensure that organizational unlearning and organizational relearning can occur. Originality/value – Most existing studies focused on the inflows of knowledge, but the outflows of knowledge still lack sufficient attention, especially the dynamic process of knowledge management. The framework provides guides in that process.