Department of Computer Science & Engineering

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IIT Jodhpur

School of Artificial Intelligence and Data Science (AIDE)

We present a cortico-basal ganglia model to study the neural mechanisms behind reaching movements in normal and in Parkinson’s disease conditions. The model consists of the following components: a two-joint arm model (AM), a layer of motor neurons in the spinal cord (MN), the proprioceptive cortex (PC), the motor cortex (MC), the prefrontal cortex (PFC), and the basal ganglia (BG). The model thus has an outer sensory-motor cortical loop and an inner cortico-basal ganglia loop to drive learning of reaching behavior. Sensory and motor maps are formed by the PC and MC which represent the space of arm configurations. The BG sends control signals to the MC following a stochastic gradient ascent policy applied to the value function defined over the arm configuration space. The trainable connections from PFC to MC can directly activate the motor cortex, thereby producing rapid movement avoiding the slow search conducted by the BG. The model captures the two main stages of motor learning, i.e., slow movements dominated by the BG during early stages and cortically driven fast movements with smoother trajectories at later stages. The model explains PD performance in stationary and pursuit reaching tasks. The model also shows that PD symptoms like tremor and rigidity could be attributed to synchronized oscillations in STN–GPe. The model is in line with closed-loop control and with neural representations for all the nuclei which explains Parkinsonian reaching. By virtue of its ability to capture the role of cortico-basal ganglia systems in controlling a wide range of features of reaching, the proposed model can potentially serve as a benchmark to test various motor pathologies of the BG. © Springer Nature Singapore Pte Ltd. 2018.

World University root node

Cognitive Science and Technology

A cortico-basal ganglia model to understand the neural dynamics of targeted reaching in normal and parkinson’s conditions

The compensation capabilities of Choquet integral are augmented to consider the complex attitudinal character of a decision maker. The resulting operator is termed as attitudinal Choquet integral (ACI). The proposed ACI is further extended as induced ACI. The special cases of ACI are investigated. The usefulness of ACI is shown through a case study. © 2018 Wiley Periodicals, Inc.

International Journal of Intelligent Systems

Attitudinal Choquet integrals and applications in decision making

A challenging problem in cognitive neuroscience is to relate the structural connectivity (SC) to the functional connectivity (FC) to better understand how large-scale network dynamics underlying human cognition emerges from the relatively fixed SC architecture. Recent modeling attempts point to the possibility of a single diffusion kernel giving a good estimate of the FC. We highlight the shortcomings of the single-diffusion-kernel model (SDK) and propose a multi-scale diffusion scheme. Our multi-scale model is formulated as a reaction-diffusion system giving rise to spatio-temporal patterns on a fixed topology. We hypothesize the presence of inter-regional co-activations (latent parameters) that combine diffusion kernels at multiple scales to characterize how FC could arise from SC. We formulated a multiple kernel learning (MKL) scheme to estimate the latent parameters from training data. Our model is analytically tractable and complex enough to capture the details of the underlying biological phenomena. The parameters learned by the MKL model lead to highly accurate predictions of subject-specific FCs from test datasets at a rate of 71%, surpassing the performance of the existing linear and non-linear models. We provide an example of how these latent parameters could be used to characterize age-specific reorganization in the brain structure and function. © 2018 The Author(s).

Fulltext

Scientific Reports

Multiple Kernel Learning Model for Relating Structural and Functional Connectivity in the Brain

Alzheimer's disease (AD) is a neurodegenerative disorder contributing to rapid decline in cognitive function and ultimately dementia. Most cases of AD occur in elderly and later years. There is a growing need for understanding the relationship between aging and AD to identify shared and unique hallmarks associated with the disease in a region and cell-type specific manner. Although genomic studies on AD have been performed extensively, the molecular mechanism of disease progression is still not clear. The major objective of our study is to obtain a higher-order network-level understanding of aging and AD, and their relationship using the hippocampal gene expression profiles of young (20-50 years), aging (70-99 years), and AD (70-99 years). The hippocampus is vulnerable to damage at early stages of AD and altered neurogenesis in the hippocampus is linked to the onset of AD. We combined the weighted gene co-expression network and weighted protein-protein interaction network-level approaches to study the transition from young to aging to AD. The network analysis revealed the organization of co-expression network into functional modules that are cell-type specific in aging and AD. We found that modules associated with astrocytes, endothelial cells and microglial cells are upregulated and significantly correlate with both aging and AD. The modules associated with neurons, mitochondria and endoplasmic reticulum are downregulated and significantly correlate with AD than aging. The oligodendrocytes module does not show significant correlation with neither aging nor disease. Further, we identified aging- and AD-specific interactions/subnetworks by integrating the gene expression with a human protein-protein interaction network. We found dysregulation of genes encoding protein kinases (FYN, SYK, SRC, PKC, MAPK1, ephrin receptors) and transcription factors (FOS, STAT3, CEBPB, MYC, NFKβ, and EGR1) in AD. Further, we found genes that encode proteins with neuroprotective function (14-3-3 proteins, PIN1, ATXN1, BDNF, VEGFA) to be part of the downregulated AD subnetwork. Our study highlights that simultaneously analyzing aging and AD will help to understand the pre-clinical and clinical phase of AD and aid in developing the treatment strategies. © 2018 Lanke, Moolamalla, Roy and Vinod.

Frontiers in Aging Neuroscience

Integrative analysis of hippocampus gene expression profiles identifies network alterations in aging and Alzheimer's disease

Head direction (HD) cells, grid cells, and place cells, often dubbed spatial cells, are neural correlates of spatial navigation. We propose a computational model to study the influence of multisensory modalities, especially vision, and proprioception on responses of these cells. A virtual animal was made to navigate within a square box along a synthetic trajectory. Visual information was obtained via a cue card placed at a specific location in the environment, while proprioceptive information was derived from curvature-modulated limb oscillations associated with the gait of the virtual animal. A self-organizing layer was used to encode HD information from both sensory streams. The sensory integration (SI) of HD from both modalities was performed using a continuous attractor network with local connectivity, followed by oscillatory path integration and lateral anti-Hebbian network, where spatial cell responses were observed. The model captured experimental findings which investigated the role of visual manipulation (cue card removal and cue card rotation) on these spatial cells. The model showed a more stable formation of spatial representations via the visual pathway compared to the proprioceptive pathway, emphasizing the role of visual input as an anchor for HD, grid, and place responses. The model suggests the need for SI at the HD level for formation of such stable representations of space essential for effective navigation. © 2016 IEEE.

IEEE Transactions on Cognitive and Developmental Systems

A Model of Multisensory Integration and Its Influence on Hippocampal Spatial Cell Responses

The spares distribution centre is an important member of a manufacturing supply chain where the automobile components are packaged and distributed to customers and retail units. Though the components do not undergo processing in a spare parts centre, various other operations are performed during distribution that contributes to component failures. The Six Sigma DMAIC framework is used for the analysis of the failures effectively. The different causes of rejections are generated using the cause and effect diagram and each of these causes are validated with the help of experiments and other tools. The root cause of this problem arising in the distribution centre is identified to be the packing method. An economic solution to the existing cause of the problem is implemented with minor variations in the current packing method and the results are discussed. A structured follow up plan for continuous improvement is also suggested. © Copyright 2018 Inderscience Enterprises Ltd.

International Journal of Services and Operations Management

Failure analysis of automobile spares in a manufacturing supply chain distribution centre using Six Sigma DMAIC framework

The concept of information sets is inspired by the way we perceive information source values. However, they lack a provision to represent the hesitancy inevitably associated with the perceived values. To this end, recently the concept of hesitant information set (HIS) is proposed in the literature. In contrast to an information set that maps an information source value to a single perceived information value, HIS gives a set of multiple perceived information values for a single information source value. This paper explores HIS further for its properties. © 2018 Copyright is held by the owner/author(s).

ACM International Conference Proceeding Series

Properties for hesitant information sets

The brain during healthy aging exhibits gradual deterioration of structure but maintains a high level of cognitive ability. These structural changes are often accompanied by reorganization of functional brain networks. Existing neurocognitive theories of aging have argued that such changes are either beneficial or detrimental. Despite numerous empirical investigations, the field lacks a coherent account of the dynamic processes that occur over our lifespan. Taking advantage of the recent developments in whole-brain computational modeling approaches, we hypothesize that the continuous process of aging can be explained by the concepts of metastability − a theoretical framework that gives a systematic account of the variability of the brain. This hypothesis can bridge the gap between existing theories and the empirical findings on age-related changes. © 2017 Elsevier Ltd

Trends in Cognitive Sciences

Metastability in Senescence

The Hilltop Review

The Effect of Inflation Targeting Policies on Dollarization-A Cross-Country Analysis

Vehicle routing problems (VRPs) whose typical objective is to minimise total travel costs over a tour have evolved over the years with objectives ranging from minimising travel times and distances to minimising pollution and fuel consumption. However, driver behaviour continues to be neglected while planning for vehicle routes. Factors such as traffic congestion levels, monotonous drives and fatigue have an impact on the behaviour of drivers, which in turn might affect their speed-choice and route-choice behaviours. The behaviour of drivers and their subsequent decision-making owing to these factors impact the revenue of transport companies and could lead to huge losses in extreme cases. There have been studies on the behaviour of drivers in isolation, without inclusion of the objectives and constraints of the traditional routing problem. This paper presents a review of existing models of VRP, planner behaviour models in the VRP context and driver behaviour models and provides a motivation to integrate these models in a stochastic traffic environment to produce practical, economic and driver-friendly logistics solutions. The paper provides valuable insights on the relevance of behavioural issues in logistics and highlights the modelling implications of incorporating planner and driver behaviour in the framework of routing problems. © 2016 Informa UK Limited, trading as Taylor & Francis Group.