Department of Computer Science & Engineering

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IIT Jodhpur

School of Artificial Intelligence and Data Science (AIDE)

Grid cells are a special class of spatial cells found in the medial entorhinal cortex (MEC) characterized by their strikingly regular hexagonal firing fields. This spatially periodic firing pattern is originally considered to be independent of the geometric properties of the environment. However, this notion was contested by examining the grid cell periodicity in environments with different polarity (Krupic et al., 2015) and in connected environments (Carpenter et al., 2015). Aforementioned experimental results demonstrated the dependence of grid cell activity on environmental geometry. Analysis of grid cell periodicity on practically infinite variations of environmental geometry imposes a limitation on the experimental study. Hence we analyze the dependence of grid cell periodicity on the environmental geometry purely from a computational point of view. We use a hierarchical oscillatory network model where velocity inputs are presented to a layer of Head Direction cells, outputs of which are projected to a Path Integration layer. The Lateral Anti-Hebbian Network (LAHN) is used to perform feature extraction from the Path Integration neurons thereby producing a spectrum of spatial cell responses. We simulated the model in five types of environmental geometries such as: (1) connected environments, (2) convex shapes, (3) concave shapes, (4) regular polygons with varying number of sides, and (5) transforming environment. Simulation results point to a greater function for grid cells than what was believed hitherto. Grid cells in the model encode not just the local position but also more global information like the shape of the environment. Furthermore, the model is able to capture the invariant attributes of the physical space ingrained in its LAHN layer, thereby revealing its ability to classify an environment using this information. The proposed model is interesting not only because it is able to capture the experimental results but, more importantly, it is able to make many important predictions on the effect of the environmental geometry on the grid cell periodicity and suggesting the possibility of grid cells encoding the invariant properties of an environment. © 2019 Jayakumar, Narayanamurthy, Ramesh, Soman, Muralidharan and Chakravarthy.

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Fulltext

Frontiers in Neural Circuits

Modeling the effect of environmental geometries on grid cell representations

How do we prepare to stop ourselves in the future? Here, we used scalp EEG to test the hypothesis that people prepare to stop by putting parts of their motor system (specifically, here, sensorimotor cortex) into a suppressed state ahead of time. On each trial, participants were cued to prepare to stop one hand and then initiated a bimanual movement. On a minority of trials, participants were instructed to stop the cued hand while continuing quickly with the other. We used a guided multivariate source separation method to examine oscillatory power changes in presumed sensorimotor cortical areas. We observed that, when people prepare to stop a hand, there were above-baseline beta band power increases (12–24 Hz) in contralateral cortex up to a second earlier. This increase in beta band power in the proactive period was functionally relevant because it predicted, trial by trial, the degree of selectivity with which participants subsequently stopped a response but did not relate to movement per se. Thus, preparing to stop particular response channels corresponds to increased beta power from contralateral (sensorimotor) cortex, and this relates specifically to subsequent stopping. These results provide a high temporal resolution and frequency-specific electrophysiological signature of the preparing-to-stop state that is pertinent to future studies of mitigating provocation, including in clinical disorders. The results also highlight the utility of guided multivariate source separation for revealing the cortical dynamics underlying both movement and response suppression. © 2019 Massachusetts Institute of Technology.

Journal of Cognitive Neuroscience

Preparing to stop action increases beta band power in contralateral sensorimotor cortex

To facilitate the selection of an optimal therapy for a stroke patient with upper extremity hemiparesis, we propose a cortico-basal ganglia model capable of performing reaching tasks under normal and stroke conditions. The model contains two hemispherical systems, each organized into an outer sensory-motor cortical loop and an inner basal ganglia (BG) loop, controlling their respective hands. The model is trained to simulate two therapeutic approaches: the constraint induced movement therapy (CIMT) in which the intact is arrested, and Bimanual Reaching in which the movements of the intact arm are found to aid the affected arm. Which of these apparently mutually conflicting approaches is right for a given patient? Based on our study on the effect of lesion size on arm performance, we hypothesize that the choice of the therapy depends on the lesion size. Whereas bimanual reaching is more suitable for smaller lesion size, CIMT is preferred in case of larger lesion sizes. By virtue of the model’s ability to capture the experimental results effectively, we believe that it can serve as a benchmark for the development and testing of various rehabilitation strategies for stroke. © 2019, The Author(s).

Scientific Reports

A Cortico- Basal Ganglia Model for choosing an optimal rehabilitation strategy in Hemiparetic Stroke

In the real-world multicriteria decision making, the evaluations of the various criteria are often vague (or not crisp). The existing choice models are difficult to apply in such situations. In this paper, we introduce an intuitionistic fuzzy variant of the multinomial logit model, which helps us to suggest a decision-maker's likely choices with vague evaluations. The applicability of the proposed model is shown through a real multicriteria decision-making application. © 2017 IEEE.

IEEE Transactions on Emerging Topics in Computational Intelligence

Intuitionistic Fuzzy Logit Model of Discrete Choice

The evaluation of the alternatives against multiple criteria is of the utmost importance in a multi-criteria decision making (MCDM) problem. It is often the case that the experts have a varying degree of confidence in their evaluations. That is, an evaluation has an associated degree of credibility. To take into account this crucial piece of information in determining the best choice, we present a new data structure. More specifically, we present a confidence-based soft set. We also extend the same to the fuzzy and intuitionistic fuzzy domains. The proposed concepts are elucidated through a number of illustrative examples. We establish their usefulness in a real case-study. © 2018 Elsevier Ltd

Computers and Industrial Engineering

Confidence soft sets and applications in supplier selection

Several studies have proposed constraints under which a low-dimensional representation can be derived from large-scale real-world networks exhibiting complex nonlinear dynamics. Typically, these representations are formulated under certain assumptions, such as when solutions converge to attractor states using linear stability analysis or using projections of large-scale dynamical data into a set of lower dimensional modes that are selected heuristically. Here, we propose a generative framework for selection of lower dimensional modes onto which the entire network dynamics can be projected based on the symmetry of the input distribution for a large-scale network driven by external inputs, thus relaxing the heuristic selection of modes made in the earlier reduction approaches. The proposed mode reduction technique is tractable analytically and applied to different kinds of real-world large-scale network scenarios with nodes comprising of (a) Van der Pol oscillators (b) Hindmarsh-Rose neurons. These two demonstrations elucidate how order parameter is conserved at original and reduced descriptions thus validating our proposition. © 2019 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft.

New Journal of Physics

Generative framework for dimensionality reduction of large scale network of nonlinear dynamical systems driven by external input

A new evolutionary-learning algorithm is proposed to learn a decision maker (DM)'s best solution on a conflicting multiobjective space. Given the exemplary pairwise comparisons of solutions by a DM, we learn an ideal point (for the DM) that is used to evolve toward a better set of solutions. The process is repeated to get the DM's best solution. The comparison of solutions in pairs facilitates the process of eliciting training information for the proposed learning model. Experimental study on standard multiobjective data sets shows that the proposed method accurately identifies a DM's preferred zone in relatively a few generations and with a small number of preferences. Besides, it is found to be robust to inconsistencies in the preference statements. The results obtained are validated through a variant of the established NSGA-2 algorithm. © 2012 IEEE.

IEEE Transactions on Neural Networks and Learning Systems

Learning of a Decision-Maker's Preference Zone with an Evolutionary Approach

Resting-state functional connectivity (FC) analyses have shown atypical connectivity in autism spectrum disorder (ASD) as compared to typically developing (TD). However, this view emerges from investigating static FC overlooking the whole brain transient connectivity patterns. In our study, we investigated how age and disease influence the dynamic changes in functional connectivity of TD and ASD. We used resting-state functional magnetic resonance imaging (rs-fMRI) data stratified into three cohorts: children (7–11 years), adolescents (12–17 years), and adults (18+ years) for the analysis. The dynamic variability in the connection strength and the modular organization in terms of measures such as flexiblity, cohesion strength, and disjointness were explored for each subject to characterize the differences between ASD and TD. In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hyper-variability relates to the symptom severity in ASD. We also found that whole-brain flexibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor, and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD. © 2019 Harlalka, Bapi, Vinod and Roy.

Frontiers in Human Neuroscience

Atypical flexibility in dynamic functional connectivity quantifies the severity in autism spectrum disorder

Preferences provide a means for specifying the desires of a decision maker (DM) in a declarative way. In this paper, based on a DM’s pairwise preferences, we infer the DM’s unique decision model. We capture (a) the attitudinal character, (b) relative criteria importance, and (c) the criteria interaction, all of which are specific to the DM. We make use of the preference-learning (PL) technique to induce predictive preference models from empirical data. Because PL is emerging as a new subfield of machine learning, we could use standard machine-learning methods to accomplish our learning objective. We consider the DM’s exemplary preference information in the form of pairwise comparisons between alternatives as the training information. The DM’s decision model is captured in terms of (a), (b), and (c), through the parameters of an attitudinal Choquet integral operator. The proposed learning approach is validated through an experimental study on 16 standard data sets. The superiority of the proposed method in terms of predictive accuracy and easier interpretability is shown both theoretically as well as empirically. Copyright: © 2019 INFORMS.

INFORMS Journal on Computing

Modelling human decision behaviour with preference learning

We learn the parameters of the popular multinomial logit model to gain insights about a DM’s decision process. We accomplish this objective through the recent algorithmic advances in the emerging field of preference learning. The empirical evaluation of the proposed approach is performed on a set of 12 publicly available benchmark datasets. First experimental results suggest that our approach is not only intuitively appealing, but also competitive to state-of-the-art preference learning methods in terms of the prediction accuracy. © 2018, Springer-Verlag London Ltd., part of Springer Nature.