Search results for "Sequence learning"
showing 10 items of 25 documents
Effects of low-gamma tACS on primary motor cortex in implicit motor learning
2019
Abstract In the primary motor cortex (M1), rhythmic activity in the gamma frequency band has been found during movement planning, onset and execution. Although the role of high-gamma oscillatory activity in M1 is well established, the contribution of low-gamma activity is still unexplored. In this study, transcranial alternating current stimulation (tACS) was used with the aim to specifically modulate low-gamma frequency band in M1, during an implicit motor learning task. A 40 Hz-tACS was applied over the left M1 while participants performed a serial reaction time task (SRTT) using their right hand. The task required the repetitive execution of sequential movements in response to sequences …
A Matrixulation Method for Mapping an E-learning Platform Designer’s Conception of Learning: A Pilot Study
2004
This paper presents a method for analyzing human conceptions related to e-learning, based on positioning data on what is called here a learning matrix. The set of dimensions comprising the matrix distinguish between emphasis on individuality and sociality in learning, between viewing learning as knowledge adoption and as knowledge construction, and between viewing learning as subjective and as objective to time. The learning matrix is used to visualize and compare conceptions of learning extracted from literature and from individual perceptions of learning, revealed through interviews. This study supports the development of e-learning environments and casts light on different conceptions of…
On the impact of forgetting on learning machines
1995
People tend not to have perfect memories when it comes to learning, or to anything else for that matter. Most formal studies of learning, however, assume a perfect memory. Some approaches have restricted the number of items that could be retained. We introduce a complexity theoretic accounting of memory utilization by learning machines. In our new model, memory is measured in bits as a function of the size of the input. There is a hierarchy of learnability based on increasing memory allotment. The lower bound results are proved using an unusual combination of pumping and mutual recursion theorem arguments. For technical reasons, it was necessary to consider two types of memory : long and sh…
Anodal Transcranial Direct Current Stimulation Over Prefrontal Cortex Slows Sequence Learning in Older Adults
2022
Aging is associated with declines in sensorimotor function. Several studies have demonstrated that transcranial direct current stimulation (tDCS), a form of non-invasive brain stimulation, can be combined with training to mitigate age-related cognitive and motor declines. However, in some cases, the application of tDCS disrupts performance and learning. Here, we applied anodal tDCS either over the left prefrontal cortex (PFC), right PFC, supplementary motor complex (SMC), the left M1, or in a sham condition while older adults (n = 63) practiced a Discrete Sequence Production (DSP), an explicit motor sequence, task across 3 days. We hypothesized that stimulation to either the right or left P…
A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning
2016
Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies n…
Modelling the insect Mushroom Bodies: Application to sequence learning
2015
Learning and reproducing temporal sequences is a fundamental ability used by living beings to adapt behaviour repertoire to environmental constraints. This paper is focused on the description of a model based on spiking neurons, able to learn and autonomously generate a sequence of events. The neural architecture is inspired by the insect Mushroom Bodies (MBs) that are a crucial centre for multimodal sensory integration and behaviour modulation. The sequence learning capability coexists, within the insect brain computational model, with all the other features already addressed like attention, expectation, learning classification and others. This is a clear example that a unique neural struc…
Sequence Learning in a Single Trial: A Spiking Neurons Model Based on Hippocampal Circuitry.
2020
ABSTRACTIn contrast with our everyday experience using brain circuits, it can take a prohibitively long time to train a computational system to produce the correct sequence of outputs in the presence of a series of inputs. This suggests that something important is missing in the way in which models are trying to reproduce basic cognitive functions. In this work, we introduce a new neuronal network architecture that is able to learn, in a single trial, an arbitrary long sequence of any known objects. The key point of the model is the explicit use of mechanisms and circuitry observed in the hippocampus, which allow the model to reach a level of efficiency and accuracy that, to the best of our…
Sequential Learning with LS-SVM for Large-Scale Data Sets
2006
We present a subspace-based variant of LS-SVMs (i.e. regularization networks) that sequentially processes the data and is hence especially suited for online learning tasks. The algorithm works by selecting from the data set a small subset of basis functions that is subsequently used to approximate the full kernel on arbitrary points. This subset is identified online from the data stream. We improve upon existing approaches (esp. the kernel recursive least squares algorithm) by proposing a new, supervised criterion for the selection of the relevant basis functions that takes into account the approximation error incurred from approximating the kernel as well as the reduction of the cost in th…
Adaptive variable structure fuzzy neural identification and control for a class of MIMO nonlinear system
2013
This paper presents a novel adaptive variable structure (AVS) method to design a fuzzy neural network (FNN). This AVS-FNN is based on radial basis function (RBF) neurons, which have center and width vectors. The network performs sequential learning through sliding data window reflecting system dynamic changes, and dynamic growing-and-pruning structure of FNN. The salient characteristics of the AVS-FNN are as follows: (1) Structure-learning and parameters estimation are performed automatically and simultaneously without partitioning input space and selecting initial parameters a priori. The structure-learning approach relies on the contribution of the size of the output. (2) A set of fuzzy r…
Learning spatio-temporal behavioural sequences
2018
Living beings are able to adapt their behaviour repertoire to environmental constraints. Among the capabilities needed for such improvement, the ability to store and retrieve temporal sequences is of particular importance. This chapter focuses on the description of an architecture based on spiking neurons, able to learn and autonomously generate a sequence of generic objects or events. The neural architecture is inspired by the insect mushroom bodies already taken into account in the previous chapters as a crucial centre for multimodal sensory integration and behaviour modulation in insects. Sequence learning is only one among a variety of functionalities that coexist within the insect brai…