V. Srinivasa Chakravarthy

Impaired value estimation over space explains turn-induced freezing of gait in Parkinson's disease: A cortico-basal ganglia-inspired reinforcement learning model

Classifier-guided Deep Oscillatory Neural Networks (cDONN) for capturing both Neural Dynamics and Behavior simultaneously

This paper introduces cDONN, a neural network that generates EEG signals and classifies behavior simultaneously, showing high fidelity in synthesized EEG data.

A Mechanistic Whole Brain Model to Capture Simultaneous EEG-fMRI Data

This paper introduces a novel oscillatory network model to simulate simultaneous EEG-fMRI data, showing improved replication of functional connectivity and dynamics across spatiotemporal scales.

Electroencephalogram (EEG) Classification using a bio-inspired Deep Oscillatory Neural Network

This paper introduces two oscillatory neural networks for EEG classification and prediction tasks, incorporating brain dynamics through nonlinear oscillators.

A unified model of hippocampal spatial and object cells involving bidirectionally coupled Lateral and Medial Entorhinal Cortical layers

This paper presents a unified computational model that generates various spatial and object-sensitive neurons in the hippocampus by integrating visual and path integration information through a Graph Neural Network.

Modeling Hippocampal Spatial Cells in Rodents navigating in 3D environments

The paper models place and grid cells in 3D navigation using a deep autoencoder, showing species-specific spatial cell responses and the role of head direction tuning.

A Computational Model of Deep Brain Stimulation for Parkinsons Disease Tremor

This paper develops a computational model of deep brain stimulation (DBS) to study its effects on Parkinson's disease tremor by simulating cortico-basal ganglia circuitry in healthy and diseased states.

Modeling of whole brain Electroencephalogram (EEG) in a spatially organized oscillatory network

This study models EEG signals using a Hopf oscillator network with spatial organization, achieving accurate signal reconstruction and spectral properties.

A Basal Ganglia Model for understanding Working Memory Functions in Healthy and Parkinson's Conditions

This paper develops a unified basal ganglia model to explain working memory functions in healthy and Parkinson's disease conditions, focusing on dopamine's role.

A deep network-based model of hippocampal memory functions under normal and Alzheimer's disease conditions

The paper presents a deep network model of hippocampal memory functions, simulating normal and Alzheimer's disease conditions by altering network damage levels and observing recall behavior.

Modeling the development of cortical responses in primate dorsal (where) pathway to optic flow using hierarchical neural field models

The paper develops three hierarchical neural field models to simulate MST neuron responses to optic flow in primates, showing that deep learning models can replicate functional hierarchy in the motion pathway.

The Flip-flop neuron - A memory efficient alternative for solving challenging sequence processing and decision making problems

The paper introduces a JK flip-flop neuron model for sequence processing tasks, showing it outperforms LSTMs with fewer parameters.

The development of bi-directionally coupled self-organizing neurovascular networks captures orientation-selective neural and hemodynamic cortical responses

This paper models neurovascular networks to show that vascular feedback influences neural map organization and hemodynamic responses.

A biologically plausible dynamic deep network for recognizing structure from motion and biological motion

This paper introduces two dynamic deep networks to model structure from motion and biological motion processing, showing shared mechanisms in visual motion perception.

ASM-3D: An attentional search model fashioned after what andwhere/how pathways for target search in 3D environment

The paper introduces an attentional search model inspired by visual pathways to locate targets in 3D environments using classifier and camera motion networks.

The Influence of Neural activity and Neural Cytoarchitecture on Cerebrovascular Arborization: A Computational Model

The paper introduces a computational model, VAM, to simulate vascular arborization influenced by neural activity and cytoarchitecture, showing reduced vascular branch density in lesioned rats.

A Generalized Reinforcement Learning-Based Deep Neural Network (GRL-DNN) Agent Model for Diverse Cognitive Constructs

This paper introduces a reinforcement learning-based deep neural network model to simulate multiple cognitive functions and demonstrates its potential as a testbench for cognitive impairment research.

Memory Consolidation with Orthogonal Gradients for avoiding Catastrophic Forgetting

The paper proposes a regularization-based model to prevent catastrophic forgetting in deep networks by constraining parameter updates to directions orthogonal to previous task gradients, showing improved performance on standard datasets.

Modeling the tonotopic map using a two-dimensional array of neural oscillators

The paper introduces OTSOM, a model of the tonotopic map using Hopf oscillators, and shows it can perform Fourier-like decomposition with phase and frequency tuning.

A Multiscale, Systems-level, Neuropharmacological Model of Cortico-Basal Ganglia System for Arm Reaching under Normal, Parkinsonian and Levodopa Medication Conditions

This paper presents a multiscale computational model of the cortico-basal ganglia system to simulate arm reaching behavior under normal, Parkinsonian, and levodopa conditions, showing how SNc cell loss and medication affect motor performance.

Artificial Neurovascular Network (ANVN) to Study the Accuracy Vs. Efficiency Trade-Off in an Energy Dependent Neural Network

The paper constructs an Artificial Neurovascular Network (ANVN) to study how training both neural and vascular networks affects performance and energy efficiency.

A Network Architecture for Bidirectional Neurovascular Coupling in Rat Whisker Barrel Cortex

This paper presents a computational model of bidirectional neurovascular coupling in rat whisker barrel cortex, showing how vascular feedback influences neural activity and map formation under normal and pathological conditions.

A Complex-valued Oscillatory Neural Network for Storage and Retrieval of Multichannel Electroencephalogram Signals

This paper introduces a complex-valued oscillatory neural network that models multichannel EEG signals with high accuracy using Hopf oscillators and power coupling.

INFLUENCE OF ENERGY DEFICIENCY ON THE MOLECULAR PROCESSES OF SUBSTANTIA NIGRA PARS COMPACTA CELL FOR UNDERSTANDING PARKINSONIAN NEURODEGENERATION: A COMPREHENSIVE BIOPHYSICAL COMPUTATIONAL MODEL

The paper develops a computational model of SNc cells to investigate how energy deficiency contributes to Parkinson's disease neurodegeneration by simulating molecular processes like ATP variation and alpha-synuclein aggregation.

A Multi-Scale Computational Model of Excitotoxic Loss of Dopaminergic Cells in Parkinson's Disease

This study uses a multi-scale computational model to show that energy deficiencies and calcium elevation contribute to excitotoxic loss of dopaminergic neurons in Parkinson's disease, with specific therapies halting cell loss.

A Computational Model of Levodopa-Induced Toxicity in Substantia Nigra Pars Compacta in Parkinson's Disease

The paper develops a multiscale computational model of the SNc-striatum system to study LDOPA-induced toxicity in Parkinson's disease and shows that energy deficiency contributes to neurodegeneration.

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

This study develops a computational model of the basal ganglia to simulate bipolar oscillations between positive and negative mood states by analyzing reward and risk sensitivity.

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

This paper develops a cortico-basal ganglia model to simulate reaching tasks in stroke patients and evaluates therapy effectiveness based on lesion size.

A computational model of loss of dopaminergic cells in Parkinson’s disease due to glutamate-induced excitotoxicity

The paper develops a computational model of dopaminergic cell loss in Parkinson’s disease due to glutamate-induced excitotoxicity and evaluates therapeutic interventions.

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

This paper presents a hierarchical anti-Hebbian network model that simulates the formation of 3D spatial cells, including new cell types and explains anisotropic coding and discrepancies in 3D navigation.