Multi-bump solutions in a neural field model with external inputs

"Available online 3 March 2016"We study the conditions for the formation of multiple regions of high activity or “bumps” in a one-dimensional, homogeneous neural field with localized inputs. Stable multi-bump solutions of the integro-differential equation have been proposed as a model of a neural population representation of remembered external stimuli. We apply a class of oscillatory coupling functions and first derive criteria to the input width and distance, which relate to the synaptic couplings that guarantee the existence and stability of one and two regions of high activity. These input-induced patterns are attracted by the corresponding stable one-bump and two-bump solutions when the input is removed. We then extend our analytical and numerical investigation to NN-bump solutions showing that the constraints on the input shape derived for the two-bump case can be exploited to generate a memory of N>2N>2 localized inputs. We discuss the pattern formation process when either the conditions on the input shape are violated or when the spatial ranges of the excitatory and inhibitory connections are changed. An important aspect for applications is that the theoretical findings allow us to determine for a given coupling function the maximum number of localized inputs that can be stored in a given finite interval.The work received financial support from FCT through a PhD grant (SFRH/BD/41179/2007) and from the EU-FP7 ITN project NETT: Neural Engineering Transformative Technologies (nr. 289146)

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.The work was funded by FCT - Fundacao para a Ciencia e Tecnologia, through the PhD Grants SFRH/BD/48529/2008 and SFRH/BD/41179/2007 and Project NETT: Neural Engineering Transformative Technologies, EU-FP7 ITN (nr. 289146) and the FCT-Research Center CMAT (PEst-OE/MAT/UI0013/2014)

Towards temporal cognition for robots: a neurodynamics approach

If we want robots to engage effectively with humans in service applications or in collaborative work scenarios they have be endowed with the capacity to perceive the passage of time and control the timing of their actions. Here we report result of a robotics experiment in which we test a computational model of action timing based on processing principles of neurodynamics. A key assumption is that elapsed time is encoded in the consistent buildup of persistent population activity representing the memory of sensory or motor events. The stored information can be recalled using a ramp-to-threshold dynamics to guide actions in time. For the experiment we adopt an assembly paradigm from our previous work on natural human-robot interactions. The robot first watches a human executing a sequence of assembly steps. Subsequently, it has to execute the steps from memory in the correct order and in synchrony with an external timing signal. We show that the robot is able to efficiently adapt its motor timing and to store this information in memory using the temporal mismatch between the neural processing of the sensory feedback about executed actions and the external cue.FCT - Fundació Catalana de Trasplantament(PD/BD/128183/2016)This research was supported by the Marie Curie Network for Initial Training NETT, FCT through the PhD fellowship PD/BD/128183/2016, the FCT-Research Center CMAT (PEstOE/MAT/UI0013/2014), and FCT - Algoritmi research Centre (COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project ˆ Scope: UID/CEC/00319/2013

Adaptive timing in a dynamic field architecture for natural human–robot interactions

A close temporal coordination of actions and goals is crucial for natural and fluent human–robot interactions in collaborative tasks. How to endow an autonomous robot with a basic temporal cognition capacity is an open question. In this paper, we present a neurodynamics approach based on the theoretical framework of dynamic neural fields (DNF) which assumes that timing processes are closely integrated with other cognitive computations. The continuous evolution of neural population activity towards an attractor state provides an implicit sensation of the passage of time. Highly flexible sensorimotor timing can be achieved through manipulations of inputs or initial conditions that affect the speed with which the neural trajectory evolves. We test a DNF-based control architecture in an assembly paradigm in which an assistant hands over a series of pieces which the operator uses among others in the assembly process. By watching two experts, the robot first learns the serial order and relative timing of object transfers to subsequently substitute the assistant in the collaborative task. A dynamic adaptation rule exploiting a perceived temporal mismatch between the expected and the realized transfer timing allows the robot to quickly adapt its proactive motor timing to the pace of the operator even when an additional assembly step delays a handover. Moreover, the self-stabilizing properties of the population dynamics support the fast internal simulation of acquired task knowledge allowing the robot to anticipate serial order errorsThis work is financed by national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., within the scope of the projects ‘‘NEUROFIELD’’ (Ref PTDC/MAT-APL/31393/2017), ‘‘I-CATER – Intelligent Robotic Coworker Assistant for Industrial Tasks with an Ergonomics Rationale’’ (Ref PTDC/EEI-ROB/3488/2021) and R&D Units Project Scope: UIDB/00319/2020 – ALGORITMI Research Centre

Objective graphical clustering of spatiotemporal gait pattern in patients with Parkinsonism

The goal of this study was grouping patients with parkinsonism that share similar gait characteristics based on principal component analysis (PCA). Spatiotemporal gait data during self-selected walking were obtained from 15 patients with Vascular Parkinsonism, 15 patients with Idiopathic Parkinson's Disease and 15 Controls. PCA was used to reduce the dimensionality of 12 gait characteristics for the 45 subjects. Fuzzy C-mean cluster analysis was performed plotting the first two principal components, which accounted for 84.1% of the total variability. Results indicates that it is possible to quantitatively differentiate different gait types in patients with parkinsonism using PCA. Objective graphical classification of gait patterns could assist in clinical evaluation as well as aid treatment planning.POCI-01-0145-FEDER-006961National Funds through the FCT as part of project UID/EEA/50014/201

Rapid learning of complex sequences with time constraints: A dynamic neural field model

Many of our sequential activities require that behaviors must be both precisely timed and put in the proper order. This paper presents a neuro-computational model based on the theoretical framework of Dynamic Neural Fields that supports the rapid learning and flexible adaptation of coupled order-timing representations of sequential events. A key assumption is that elapsed time is encoded in the monotonic buildup of self-stabilized neural population activity representing event memory. A stable activation gradient over subpopulations carries the information of an entire sequence. With robotics applications in mind, we test the model in simulations of a learning by observation paradigm, in which the cognitive agent first memorizes the order and relative timing of observed events and, subsequently, recalls the information from memory taking potential speed constraints into account. Model robustness is tested by systematically varying sequence complexity along the temporal and the ordinal dimension. Furthermore, an adaptation rule is proposed that allows the agent to adjust in a single trial a learned timing pattern to a changing temporal context. The simulation results are discussed with respect to our goal to endow autonomous robots with the capacity to efficiently learn complex sequences with time constraints, supporting more natural human-robot interactions.FCT (Portuguese Foundation for Science and Technology) through the PhD fellowship PD/BD/128183/2016, European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) and national funds, through the FCT projects PTDC/MAT-APL/31393/2017 (NEUROFIELD) and POCI-01-0247-FEDER-039334, and RD Units Project Scope: UIDB/00319/2020 and UIDB/00013/202

A multivariate randomized controlled experiment about the effects of mindfulness priming on EEG neurofeedback self-regulation serious games

Neurofeedback training (NFT) is a technique often proposed to train brain activity SR with promising results. However, some criticism has been raised due to the lack of evaluation, reliability, and validation of its learning effects. The current work evaluates the hypothesis that SR learning may be improved by priming the subject before NFT with guided mindfulness meditation (MM). The proposed framework was tested in a two-way parallel-group randomized controlled intervention with a single session alpha NFT, in a simplistic serious game design. Sixty-two healthy naïve subjects, aged between 18 and 43 years, were divided into MM priming and no-priming groups. Although both the EG and CG successfully attained the up-regulation of alpha rhythms (F(1,59) = 20.67, p ηp2 = 0.26), the EG showed a significantly enhanced ability (t(29) = 4.38, p t(29) = 1.18, p > 0.1). Furthermore, EG superior performance on NFT seems to be explained by the subject’s lack of awareness at pre-intervention, less vigour at post-intervention, increased task engagement, and a relaxed non-judgemental attitude towards the NFT tasks. This study is a preliminary validation of the proposed assisted priming framework, advancing some implicit and explicit metrics about its efficacy on NFT performance, and a promising tool for improving naïve “users” self-regulation ability.This work is co-financed by the ERDF—European Regional Development Fund through the Operational Program for Competitiveness and Internationalisation—COMPETE 2020 (ref.: POCI01-0145-FEDER-007043; ref: POCI-01-0145-FEDER-007038), the North Portugal Regional Operational Program—NORTE 2020 (ref.: NORTE-01-0145-FEDER-000045) and by the Portuguese Foundation for Science and Technology – FCT under MIT Portugal (author Ph.D. grant ref.: PD/BD/114033/2015) and within the R&D Units Project Scope (ref.: UIDB/00319/2020)

Gait classification of patients with Fabry's disease based on normalized gait features obtained using multiple regression models

Diagnosis of Fabry disease (FD) remains a challenge mostly due to its rare occurrence and phenotipical variability, with considerable delay between onset and clinical diagnosis. It is then of extreme importance to explore biomarkers capable of assisting the earlier diagnosis of FD. There is growing evidence supporting the use of gait assessment in the diagnosis and management of several neurological diseases. In fact, gait abnormalities have previously been observed in FD, justifying further investigation. The aim of this study is to evaluate the effectiveness of different machine learning strategies when distinguishing patients with FD from healthy controls based on normalized gait features. Gait features of an individual are affected by physical characteristics including age, height, weight, and gender, as well as walking speed or stride length. Therefore, in order to reduce bias due to inter-subject variations a multiple regression (MR) normalization approach for gait data was performed. Four different machine learning strategies - Support Vector Machines (SVM), Random Forest (RF), Multiple Layer Perceptrons (MLPs), and Deep Belief Networks (DBNs) - were employed on raw and normalized gait data. Wearable sensors positioned on both feet were used to acquire the gait data from 36 patients with FD and 34 healthy subjects. Gait normalization using MR revealed significant differences in percentage of stance phase spent in foot flat and pushing (p < 0.05), with FD presenting lower percentages in foot flat and higher in pushing. No significant differences were observed before gait normalization. Support Vector Machine was the superior classifier achieving an FD classification accuracy of 78.21% after gait normalization, compared to 71.96% using raw gait data. Gait normalization improved the performance of all classifiers. To the best of our knowledge, this is the first study on gait classification that includes patients with FD, and our results support the use of gait assessment on the clinical assessment of FD.This work was partially supported by the projects NORTE-01-0145- FEDER- 000026 (DeM-Deus Ex Machina) financed by NORTE2020 and FEDER, and the Pluriannual Funding Programs of the research centres CMAT and Algoritm

Towards collaborative robots as intelligent co-workers in human-robot joint tasks: what to do and who does it?

Recently there has been an increasing demand for collaborative robots able to interact and cooperate with people in several human environments, sharing physical space, and working closely with humans in joint tasks. Endowing robots with learning and cognitive capabilities is a key for natural and efficient cooperation with the human co-worker. In particular, these abilities improve and facilitate the use of collaborative robots in the joint assembly task, especially in smart manufacturing contexts. In this paper, we report the results of the implementation of a neuro-inspired model – based on Dynamic Neural Fields - for action selection in a Human-Robot join action scenario. We test the model in a real construction scenario where the robot Sawyer selects and verbalizes, at each step, the next part to be mounted and outputs an appropriate action to insert it, together with its human partner. The two-dimensional Action Execution Layer allows the representation of the components object and action in the same field. The results reveal that the robot can compute valid decisions for different workspace layouts and for situations where there are missing pieces.Project “PRODUTECH SIF – Soluções para a Indústria do Futuro” reference POCI-01-0247-FEDER-024541,Fundação para a Ciência e a Tecnologia (FCT), Portugal within project ‘Neurofield’, ref. PTDC/MAT-APL/31393/2017