Effects of mirror-box therapy on modulation of sensorimotor EEG oscillatory rhythms: a single-case longitudinal study.

We provide direct electrophysiological evidence that mirror therapy (MT) can change brain activity and aid in the recovery of motor function after stroke. In this longitudinal single-case study, the subject was a 58-yr-old man with right-hand hemiplegia due to ischemic stroke. Over a 9-mo period we treated him with MT twice a week and measured electroencephalograms (EEG) before, during, and after each therapy session. Using advanced signal processing methods, we identified five distinct movement-related oscillatory EEG components: one slow component designated as mu rhythm and four faster components designated as sensorimotor rhythms. Results show that MT produced long-term changes of two oscillatory EEG components including the mu rhythm, which is a well-documented correlate of voluntary movement in the frequency range of 7.5-12 Hz. Specifically, MT was significantly associated with an increase in the power of mu rhythm recorded over both hemispheres and a decrease in the power of one sensorimotor component recorded over the affected hemisphere. To obtain robust, repeatable individual measures of EEG components suitable for longitudinal study, we used irregular-resampling autospectral analysis to separate fractal and oscillatory components in the EEG power spectrum and three-way parallel factor analysis to isolate oscillatory EEG components and track their activations over time. The rhythms were identified over individual days of MT training and were clearly related to the periods of event-related desynchronization and synchronization (rest, observe, and move) during MT. Our results are consistent with a model in which MT promotes recovery of motor function by altering neural activity associated with voluntary movement. NEW & NOTEWORTHY We provide novel evidence that mirror therapy (MT), which helps in the recovery of motor function after a stroke, is also associated with long-lasting changes in brain electrical activity. Using precise measurements of oscillatory EEG components over a 9-mo period in a victim of ischemic stroke, we showed that MT produced long-term increases in the mu rhythm recorded over both hemispheres and a decrease in a sensorimotor EEG component recorded over the affected hemisphere.

Ultimate Grounding of Abstract Concepts: A Graded Account.

ion, one of the hallmarks of human cognition, continues to be the topic of a strong debate. The primary disagreement concerns whether or not abstract concepts can be accounted for within the scope of embodied cognition. In this paper, we introduce the embodied approach to conceptual knowledge and distinguish between embodiment and grounding, where grounding is the general term for how concepts initially acquire their meaning. Referring to numerous pieces of empirical evidence, we emphasise that, ultimately, all concepts are acquired via interaction with the world via two main pathways: embodiment and social interaction. The first pathway is direct and primarily involves action/perception, interoception and emotions. The second pathway is indirect, being mediated by language in particular. Evidence from neuroscience, psychology and cognitive linguistics shows these pathways have different properties, roles in cognition and temporal profiles. Human development also places revealing constraints on how children develop the ability to reason more abstractly as they grow up. We recognize language as a crucial cognitive faculty with several roles enabling the acquisition of abstract concepts indirectly. Three detailed case studies on body-specificity hypothesis, abstract verbs and mathematics are used to argue that a compelling case has accumulated in favour of the ultimate grounding of abstract concepts in an agent's interaction with its world, primarily relying on the direct pathway. We consolidate the debate through multidisciplinary evidence for the idea that abstractness is a graded, rather than a binary property of concepts.

Computational analysis of memory capacity in echo state networks.

Reservoir computing became very popular due to its potential for efficient design of recurrent neural networks, exploiting the computational properties of the reservoir structure. Various approaches, ranging from appropriate reservoir initialization to its optimization by training have been proposed. In this paper, we extend our previous work and focus on short-term memory capacity, introduced by Jaeger in case of echo state networks. Memory capacity has been previously shown to peak at criticality, when the network switches from a stable regime to an unstable dynamic regime. Using computational experiments with nonlinear ESNs, we systematically analyze the memory capacity from the perspective of several parameters and their relationship, namely the input and reservoir weights scaling, reservoir size and its sparsity. We also derive and test two gradient descent based orthogonalization procedures for recurrent weights matrix, which considerably increase the memory capacity, approaching the upper bound, which is equal to the reservoir size, as proved for linear reservoirs. Orthogonalization procedures are discussed in the context of existing methods and their benefit is assessed.

Early lexical development in a self-organizing neural network.

In this paper we present a self-organizing neural network model of early lexical development called DevLex. The network consists of two self-organizing maps (a growing semantic map and a growing phonological map) that are connected via associative links trained by Hebbian learning. The model captures a number of important phenomena that occur in early lexical acquisition by children, as it allows for the representation of a dynamically changing linguistic environment in language learning. In our simulations, DevLex develops topographically organized representations for linguistic categories over time, models lexical confusion as a function of word density and semantic similarity, and shows age-of-acquisition effects in the course of learning a growing lexicon. These results match up with patterns from empirical research on lexical development, and have significant implications for models of language acquisition based on self-organizing neural networks.

Grounding the Meanings in Sensorimotor Behavior using Reinforcement Learning.

The recent outburst of interest in cognitive developmental robotics is fueled by the ambition to propose ecologically plausible mechanisms of how, among other things, a learning agent/robot could ground linguistic meanings in its sensorimotor behavior. Along this stream, we propose a model that allows the simulated iCub robot to learn the meanings of actions (point, touch, and push) oriented toward objects in robot's peripersonal space. In our experiments, the iCub learns to execute motor actions and comment on them. Architecturally, the model is composed of three neural-network-based modules that are trained in different ways. The first module, a two-layer perceptron, is trained by back-propagation to attend to the target position in the visual scene, given the low-level visual information and the feature-based target information. The second module, having the form of an actor-critic architecture, is the most distinguishing part of our model, and is trained by a continuous version of reinforcement learning to execute actions as sequences, based on a linguistic command. The third module, an echo-state network, is trained to provide the linguistic description of the executed actions. The trained model generalizes well in case of novel action-target combinations with randomized initial arm positions. It can also promptly adapt its behavior if the action/target suddenly changes during motor execution.

Dynamics and topographic organization of recursive self-organizing maps.

Recently there has been an outburst of interest in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, there is no general consensus as to how best to process sequences using topographic maps, and this topic remains an active focus of neurocomputational research. The representational capabilities and internal representations of the models are not well understood. Here, we rigorously analyze a generalization of the self-organizing map (SOM) for processing sequential data, recursive SOM(RecSOM) (Voegtlin, 2002), as a nonautonomous dynamical system consisting of a set of fixed input maps. We argue that contractive fixed-input maps are likely to produce Markovian organizations of receptive fields on the RecSOM map. We derive bounds on parameter beta (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed-input maps is guaranteed. Some generalizations of SOM contain a dynamic module responsible for processing temporal contexts as an integral part of the model. We show that Markovian topographic maps of sequential data can be produced using a simple fixed (nonadaptable) dynamic module externally feeding a standard topographic model designed to process static vectorial data of fixed dimensionality (e.g., SOM). However, by allowing trainable feedback connections, one can obtain Markovian maps with superior memory depth and topography preservation. We elaborate on the importance of non-Markovian organizations in topographic maps of sequential data.