Biology subjects

Vinogradov, O.

Publications and source records attributed to Vinogradov, O..

3 recordsLinked to original sources

bioRxiv2025

Data-driven burst shape analysis for functional phenotyping of neuronal cultures

Cultures of neurons in vitro are instrumental for studying network dynamics in normal and pathological conditions. Mature networks typically exhibit network bursting activity, which has traditionally been quantified by simplified features such as inter-burst intervals and burst durations. While these features advanced the understanding of development, disease phenotypes, and drug effects, they overlook the temporal structure of activity within bursts. Here, we developed a comprehensive framework to quantify burst shapes, the time course of network firing during bursts. Applying this approach to four datasets, including rodent- and human pluripotent stem cell-derived cultures, we show that burst shapes contain rich information about the underlying network dynamics. We quantify this information by using traditional and shape features to classify the recording conditions (types of genetic disorder, presence of pharmacological agents) and demonstrate that shapes significantly increase classification accuracy. We provide a pipeline for burst shape characterization, including simplified features that capture most of the shape information, establishing burst shape as a robust and biologically meaningful marker for functional phenotyping in disease modeling and drug screening.

neuroscience↗

bioRxiv2024

Effective excitability captures network dynamics across development and phenotypes

Neuronal cultures in vitro are a versatile system for studying the fundamental properties of individual neurons and neuronal networks. Recently, this approach has gained attention as a precision medicine tool. Mature neuronal cultures in vitro exhibit synchronized collective dynamics called network bursting. If analyzed appropriately, this activity could offer insights into the networks properties, such as its composition, topology, and developmental and pathological processes. A promising method for investigating the collective dynamics of neuronal networks is to map them onto simplified dynamical systems. This approach allows the study of dynamical regimes and the characteristics of the parameters that lead to data-consistent activity. We designed a simple biophysically inspired dynamical system and used Bayesian inference to fit it to a large number of recordings of in vitro population activity. Even with a small number of parameters, the model showed strong inter-parameter dependencies leading to invariant bursting dynamics for many parameter combinations. We further validated this observation in our analytical solution. We found that in vitro bursting can be well characterized by each of three dynamical regimes: oscillatory, bistable, and excitable. The probability of finding a data-consistent match in a particular regime changes with network composition and development. The more informative way to describe the in vitro network bursting is the effective excitability, which we analytically show to be related to the parameter-invariance of the models dynamics. We establish that the effective excitability can be estimated directly from the experimentally recorded data. Finally, we demonstrate that effective excitability reliably detects the differences between cultures of cortical, hippocampal, and human pluripotent stem cell-derived neurons, allowing us to map their developmental trajectories. Our results open a new avenue for the model-based description of in vitro network phenotypes emerging across different experimental conditions.

neuroscience↗

bioRxiv2023

Recurrent connectivity structure controls the emergence of co-tuned excitation and inhibition

Cortical neurons are versatile and efficient coding units that develop strong preferences for specific stimulus characteristics. The sharpness of tuning and coding efficiency is hypothesized to be controlled by delicately balanced excitation and inhibition. These observations suggest a need for detailed co-tuning of excitatory and inhibitory populations. Theoretical studies have demonstrated that a combination of plasticity rules can lead to the emergence of excitation/inhibition (E/I) cotuning in neurons driven by independent, low-noise signals. However, cortical signals are typically noisy and originate from highly recurrent networks, generating correlations in the inputs. This raises questions about the ability of plasticity mechanisms to self-organize co-tuned connectivity in neurons receiving noisy, correlated inputs. Here, we study the emergence of input selectivity and weight co-tuning in a neuron receiving input from a recurrent network via plastic feedforward connections. We demonstrate that while strong noise levels destroy the emergence of co-tuning in the readout neuron, introducing specific structures in the non-plastic pre-synaptic connectivity can re-establish it by generating a favourable correlation structure in the population activity. We further show that structured recurrent connectivity can impact the statistics in fully plastic recurrent networks, driving the formation of co-tuning in neurons that do not receive direct input from other areas. Our findings indicate that the network dynamics created by simple, biologically plausible structural connectivity patterns can enhance the ability of synaptic plasticity to learn input-output relationships in higher brain areas.

neuroscience↗