Biology subjects

Chakravarthy, S.

Publications and source records attributed to Chakravarthy, S..

4 recordsLinked to original sources

bioRxiv2018

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

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. In addition to constraint induced movement therapy (CIMT), the model performs both unimanual and bimanual reaching tasks and the simulation results are in congruence with the experiment conducted by Rose et al (2004). Based on our study on the effect of lesion size on arm performance, we hypothesize that the effectiveness of a therapy could greatly depend on this factor. By virtue of the models 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.

neuroscience↗

bioRxiv2018

A Hierarchical Anti-Hebbian Network Model for the Formation of Spatial Cells in Three-Dimensional Space

Three dimensional (3D) spatial cells in the mammalian hippocampalformationare believed to support the existence of 3D cognitive maps. Modeling studies are crucial to comprehend the neural principles governing the formation of these maps, yet to date very few have addressed this topic in 3D space. Here, we present a hierarchical network model for the formation of 3D spatial cells using anti-hebbian network. Built on empirical data, the model accounts for the natural emergence of 3D place, border and grid-cells as well as a new type of previously undescribed spatial cell type which we call plane cells. It further explains the plausible reason behind the place and grid-cell anisotropic coding that has been observed in rodents and the potential discrepancy with the predicted periodic coding during 3D volumetric navigation. Lastly, it provides evidence for the importance of unsupervised learning rules in guiding the formation of higher dimensional cognitive maps.

neuroscience↗

bioRxiv2017

Bipolar oscillations between positive and negative mood states in a computational model of Basal Ganglia

Bipolar disorder is characterized by mood swings - oscillations between manic and depressive states. The swings (oscillations) mark the length of an episode in a patients mood cycle (period), and can vary from hours to years. The proposed modeling study uses decision making framework to investigate the role of basal ganglia network in generating bipolar oscillations. In this model, the basal ganglia system performs a two-arm bandit task in which one of the arms leads to a positive outcome, while the other leads to a negative outcome. In healthy conditions, the model chooses positive action and avoids negative one, whereas under bipolar conditions, the model exhibits slow oscillations in its choice of positive or negative outcomes, reminiscent of bipolar oscillations. The model is cast at three levels of abstraction: 1) a two-dimensional dynamical system model, 2) a phenomenological basal ganglia model, 3) a detailed network model of basal ganglia. Phase-plane analyses on the simple reduced dynamical system with two variables reveal the essential parameters that generate pathological bipolar-like oscillations. Phenomenological and network models of the basal ganglia extend that logic, and interpret bipolar oscillations in terms of the activity of dopaminergic and serotonergic projections on the cortico-basal ganglia network dynamics. The networks dysfunction, specifically in terms of reward and risk sensitivity, is shown to be responsible for the pathological bipolar oscillations. The study proposes a computational model that explores the effects of impaired serotonergic neuromodulation on the dynamics of the cortico basal ganglia network, and relates this impairment to abstract mood states (manic and depressive episodes) and oscillations of bipolar disorder.

neuroscience↗

bioRxiv2017

A computational model that explores the effect of environmental geometries on grid cell representations

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 was originally considered to be invariant to 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 grid cell periodicity from a computational point of view using a model that was successful in generating a wide range of spatial cells, including grid cells, place cells, head direction cells and border cells. We simulated the model in four types of environmental geometries such as: 1) connected environments, 2) convex shapes, 3) concave shapes and 4) regular polygons with varying number of sides. Simulation results point to a greater function for grid cells than what was believed hitherto. Grid cells in the model code not just for local position but also for more global information like the shape of the environment. The proposed model is interesting not only because it was able to capture the aforementioned experimental results but, more importantly, it was able to make many important predictions on the effect of the environmental geometry on the grid cell periodicity.

neuroscience↗