Biology subjects

Chintaluri, C.

Publications and source records attributed to Chintaluri, C..

7 recordsLinked to original sources

bioRxiv2026

Dale's law, stability and nonlinearity are sufficient constraints to ignite transient and self-sustained neural dynamics

The motor cortex orchestrates a rich and flexible repertoire of network dynamics for rhythmic and goal-directed movements. Computational studies have begun to illuminate the mechanistic origins of this repertoire, but a comprehensive model that can explain the emergence of both transient and self-sustained dynamics is still missing. Here, we show that three simple ingredients: Dalean connectivity, stability, and nonlinear neural responses, suffice to reverse-engineer networks that produce transient, steady-state, and self-sustained periodic activity. A single dynamical principle underlies this repertoire: the interaction of non-normal amplification, inherent to Dalean networks, with neuronal nonlinearity, so to ignite and sustain multi-stable, controllable dynamics. Our approach yields entire families of connectivity matrices that require no hand-tuning or learning of weights. Without fitting them to data, these networks reproduce the population-level signatures of motor cortex, implying that the richness of cortical dynamics need not be sculpted by learning, but may emerge from simple biological ingredients.

neuroscience↗

bioRxiv2025

An ion channel omnimodel for standardized biophysical neuron modelling

Biophysical neuron modeling is an indispensable tool in neuroscience research, with the combination of diverse ion channel kinetics and morphologies being used to explain various single-neuron properties and responses. Despite this, there is no standard way of formulating ion channel models, making it challenging to relate models to one another and experimental data. Here, we revive the idea of a standard model for ion channels based on the Hodgkin-Huxley formulation, and apply it to a recently curated database of ion channel models. We demonstrate that this standard formulation, which we refer to as an omnimodel, accurately fits the majority of voltage-gated models in the database (over 3,000 models). It produces similar, if not identical, responses to voltage-clamp protocols in simulations where the ion channel omnimodels were used. Importantly, the standard formalism enables easy comparison of models based on parameter settings. It can also be used to make new observations about the space of ion channel kinetics found in neurons. Furthermore, it facilitates the inference of ion channel parameters from the responses to standard protocols. We provide an interactive platform to compare and select channel models, and encourage the community to use this standard formulation whenever possible, to facilitate understanding and comparison among models.

neuroscience↗

bioRxiv2024

Mitochondrial Ca2+ efflux controls neuronal metabolism and long-term memory across species

While impairing neuronal metabolism limits brain performance, it remains poorly understood whether enhancing metabolism in neurons, in contrast, could boost brain function. We find that reducing the expression of the mitochondrial H+/Ca2+ exchanger Letm1 results in increased Ca2+ retention in the mitochondrial matrix of firing neurons, which overactivates neuronal metabolism in flies and rodents. We find that upscaled metabolic states in active neurons of central memory circuits of flies and mice enable storing long-term memories in training paradigms in which wild-type counterparts of both species fail to remember. Our findings unveil an evolutionarily conserved mechanism that controls mitochondrial metabolism in active neurons and prove its crucial role in governing higher brain functions, such as long-term memory formation. Highlights- Letm1 controls activity-driven mitochondrial Ca2+ efflux in neurons - Increased mitochondrial Ca2+ retention during activity overactivates neuronal metabolism - Activity-driven upscaling of neuronal metabolism facilitates long-term olfactory memory in flies and mice

neuroscience↗

bioRxiv2022

Metabolically driven action potentials serve neuronal energy homeostasis and protect from reactive oxygen species

So-called spontaneous neuronal activity is a central hallmark of most nervous systems. Such non-causal firing is contrary to the tenet of spikes as a means of communication, and its origin and purpose remain unclear. Here, we propose that non-input driven firing can serve as a release valve to protect neurons from the toxic conditions arising in mitochondria from lower-than-baseline energy consumption. We built a framework of models that incorporate homeostatic control of metabolic products-ATP, ADP, and reactive oxygen species, among others-by way of changes in firing. Our theory can account for key features of neuronal activity observed in many experiments in studies ranging from ion channels function all the way to resting state dynamics. We propose an integrated, crucial role for metabolic spiking that bridges the gap between metabolic homeostasis and neuronal function. Finally, we make testable predictions to validate or falsify our theory.

neuroscience↗

bioRxiv2020

What we can and what we cannot see with extracellular multielectrodes

Extracellular recording is an accessible technique used in animals and humans to study the brain physiology and pathology. As the number of recording channels and their density grows it is natural to ask how much improvement the additional channels bring in and how we can optimally use the new capabilities for monitoring the brain. Here we show that for any given distribution of electrodes we can establish exactly what information about current sources in the brain can be recovered and what information is strictly unobservable. We demonstrate this in the general setting of previously proposed kernel Current Source Density method and illustrate it with simplified examples as well as using evoked potentials from the barrel cortex obtained with a Neuropixels probe and with compatible model data. Author SummaryEvery set of measurements is a window into reality rendering its incomplete or distorted picture. It is often difficult to relate the obtained representation of the world to underlying ground truth. Here we show, for brain electrophysiology, for arbitrary experimental setup (distribution of electrodes), and arbitrary analytical setup (function space of current source densities), that one can identify distributions of current sources which can be recovered precisely, and those which are invisible in the system. This shows what is and what is not observable in the studied system for a given setup, allows to improve the analysis results by modifying analytical setup, and facilitates interpretation of the measured sets of LFP, ECoG and EEG recordings.

neuroscience↗

bioRxiv2019

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.

neuroscience↗

bioRxiv2017

Four-Sphere Head Model For EEG Signals Revisited

Electric potential recorded at the scalp (EEG) is dominated by contributions from current dipoles set by active neurons in the cortex. Estimation of these currents, called inverse modeling, requires a forward model, which gives the potential when the positions, sizes, and directions of the current dipoles are known. Di[ff]erent models of varying complexity and realism are used in the field. An important analytical example is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. This model has been used extensively in the analysis of EEG recordings. Since it is analytical, it can also serve as a benchmark against which numerical schemes, such as the Finite Element Method (FEM), can be tested. While conceptually clear, the mathematical expression for the scalp potentials in the four-sphere model is quite cumbersome, and we observed the formulas presented in the literature to contain errors. We here derive and present the correct analytical formulas for future reference. They are compared with the results of FEM simulations of four-sphere model. We also provide scripts for computing EEG potentials in this model with the correct analytical formula and using FEM.

neuroscience↗