An introduction to time-resolved decoding analysis for M/EEG

The human brain is constantly processing and integrating information in order to make decisions and interact with the world, for tasks from recognizing a familiar face to playing a game of tennis. These complex cognitive processes require communication between large populations of neurons. The non-invasive neuroimaging methods of electroencephalography (EEG) and magnetoencephalography (MEG) provide population measures of neural activity with millisecond precision that allow us to study the temporal dynamics of cognitive processes. However, multi-sensor M/EEG data is inherently high dimensional, making it difficult to parse important signal from noise. Multivariate pattern analysis (MVPA) or "decoding" methods offer vast potential for understanding high-dimensional M/EEG neural data. MVPA can be used to distinguish between different conditions and map the time courses of various neural processes, from basic sensory processing to high-level cognitive processes. In this chapter, we discuss the practical aspects of performing decoding analyses on M/EEG data as well as the limitations of the method, and then we discuss some applications for understanding representational dynamics in the human brain

Closed loop interactions between spiking neural network and robotic simulators based on MUSIC and ROS

In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC). This enables any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations. This work extends the toolset available for researchers in both neurorobotics and computational neuroscience, and creates the opportunity to perform closed-loop experiments of arbitrary complexity to address questions in multiple areas, including embodiment, agency, and reinforcement learning

Integer Echo State Networks: Hyperdimensional Reservoir Computing

We propose an approximation of Echo State Networks (ESN) that can be efficiently implemented on digital hardware based on the mathematics of hyperdimensional computing. The reservoir of the proposed Integer Echo State Network (intESN) is a vector containing only n-bits integers (where n<8 is normally sufficient for a satisfactory performance). The recurrent matrix multiplication is replaced with an efficient cyclic shift operation. The intESN architecture is verified with typical tasks in reservoir computing: memorizing of a sequence of inputs; classifying time-series; learning dynamic processes. Such an architecture results in dramatic improvements in memory footprint and computational efficiency, with minimal performance loss.Comment: 10 pages, 10 figures, 1 tabl

What is the functional role of adult neurogenesis in the hippocampus?

The dentate gyrus is part of the hippocampal memory system and special in that it generates new neurons throughout life. Here we discuss the question of what the functional role of these new neurons might be. Our hypothesis is that they help the dentate gyrus to avoid the problem of catastrophic interference when adapting to new environments. We assume that old neurons are rather stable and preserve an optimal encoding learned for known environments while new neurons are plastic to adapt to those features that are qualitatively new in a new environment. A simple network simulation demonstrates that adding new plastic neurons is indeed a successful strategy for adaptation without catastrophic interference

Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives

In this paper we review the works related to muscle synergies that have been carried-out in neuroscience and control engineering. In particular, we refer to the hypothesis that the central nervous system (CNS) generates desired muscle contractions by combining a small number of predefined modules, called muscle synergies. We provide an overview of the methods that have been employed to test the validity of this scheme, and we show how the concept of muscle synergy has been generalized for the control of artificial agents. The comparison between these two lines of research, in particular their different goals and approaches, is instrumental to explain the computational implications of the hypothesized modular organization. Moreover, it clarifies the importance of assessing the functional role of muscle synergies: although these basic modules are defined at the level of muscle activations (input-space), they should result in the effective accomplishment of the desired task. This requirement is not always explicitly considered in experimental neuroscience, as muscle synergies are often estimated solely by analyzing recorded muscle activities. We suggest that synergy extraction methods should explicitly take into account task execution variables, thus moving from a perspective purely based on input-space to one grounded on task-space as well

Reading Comprehension in L2 Italian: Connecting Psycholinguistic Research and Pedagogical Practice

This dissertation explores reading comprehension abilities with a special focus on language minority bilingual children (LMBC). This population is often found to display lower scores than their monolingual peers and this gap in performance can negatively affect their future educational experience. Our goal was to shed light on the origins of these comprehension difficulties. To do so, we carried out an experimental study that involved 109 pupils attending 4th and 5th grade of primary school. The participants were 61 language minority bilingual and 48 monolingual students. We assessed their performance in reading comprehension and in a series of linguistic and non-linguistic abilities that are considered potential predictors of reading comprehension, i.e., general cognitive abilities, decoding skills, receptive vocabulary, and receptive grammar. We conducted an analysis to determine which ones were the best predictors for the two groups. The outcomes highlighted that while monolingual students relied primarily on their vocabulary knowledge during reading comprehension, for LMBC grammar knowledge, speed during decoding, and general cognitive abilities were also influencing their performance. Moreover, using three Self-Paced Reading Tasks (SPRT), we explored on-line language processing to verify whether there were qualitative or quantitative differences between groups. The analysis of reading times revealed that both groups followed similar processing patterns, but monolinguals obtained significantly higher scores in terms of accuracy. These results seem to suggest that processing complex structures in Italian is cognitively more demanding for the LMBC. The last part of the project was dedicated to the implementation of pedagogical practices that focused on teaching grammar and practicing the ability to make inferences using methods that aimed to stimulate the pupils’ metalinguistic awareness instead of using abstract rules.Questa tesi esplora le abilità di comprensione della lettura con una particolare attenzione ai bambini bilingui con background migratorio. Questa popolazione mostra spesso punteggi più bassi rispetto ai loro coetanei monolingui e questa discrepanza nei risultati può influire negativamente sulla loro esperienza scolastica. Il nostro obiettivo è quello di fare luce sulle origini di queste difficoltà di comprensione. Per farlo, abbiamo condotto uno studio sperimentale che ha coinvolto 109 alunni frequentanti il quarto e quinto anno di scuola primaria. I partecipanti includevano 61 studenti bilingui con background migratorio e 48 studenti monolingui. Abbiamo valutato le loro capacità nella comprensione della lettura e in una serie di abilità linguistiche e non linguistiche considerate potenziali predittori della comprensione della lettura (abilità cognitive generali, abilità di decodifica, vocabolario recettivo e grammatica recettiva). Inoltre, abbiamo condotto un'analisi per determinare quali fossero i migliori predittori per i due gruppi. I risultati hanno evidenziato che, mentre gli studenti monolingui si affidavano principalmente alla loro conoscenza lessicale durante la comprensione della lettura, per i bilingui anche la conoscenza grammaticale, la velocità durante la decodifica e le abilità cognitive generali influenzavano i loro punteggi. Con tre Self-Paced Reading Tasks (SPRT), abbiamo esplorato le loro capacità di processing on-line per verificare se ci fossero differenze di tipo qualitativo o quantitativo tra i gruppi. L'analisi dei tempi di lettura ha rivelato che entrambi i gruppi hanno seguito strategie di processing simili, ma i monolingui hanno ottenuto punteggi significativamente più alti in termini di accuratezza. Questi risultati sembrano suggerire che l'elaborazione di strutture complesse in italiano richieda maggiori risorse cognitive per gli studenti bilingui. La parte finale del progetto è stata dedicata all'implementazione di pratiche pedagogiche incentrate sull'insegnamento della grammatica e sulla pratica dell'abilità di fare inferenze utilizzando metodi volti a stimolare la consapevolezza metalinguistica degli alunni invece di utilizzare regole astratte

EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms

The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding. Channel attention mechanisms can be seen as a powerful evolution of spatial filters traditionally used for motor imagery decoding. This study systematically compares such mechanisms by integrating them into a lightweight architecture framework to evaluate their impact. We carefully construct a straightforward and lightweight baseline architecture designed to seamlessly integrate different channel attention mechanisms. This approach is contrary to previous works which only investigate one attention mechanism and usually build a very complex, sometimes nested architecture. Our framework allows us to evaluate and compare the impact of different attention mechanisms under the same circumstances. The easy integration of different channel attention mechanisms as well as the low computational complexity enables us to conduct a wide range of experiments on four datasets to thoroughly assess the effectiveness of the baseline model and the attention mechanisms. Our experiments demonstrate the strength and generalizability of our architecture framework as well as how channel attention mechanisms can improve the performance while maintaining the small memory footprint and low computational complexity of our baseline architecture. Our architecture emphasizes simplicity, offering easy integration of channel attention mechanisms, while maintaining a high degree of generalizability across datasets, making it a versatile and efficient solution for EEG motor imagery decoding within brain-computer interfaces.Comment: Submitted to: Journal of Neural Engineerin