Biology subjects

Morozova, E. O.

Publications and source records attributed to Morozova, E. O..

3 recordsLinked to original sources

bioRxiv2024

Rapid, interpretable, data-driven models of neural dynamics using Recurrent Mechanistic Models

Obtaining predictive models of a neural system is notoriously challenging. Detailed models suffer from excess model complexity and are difficult to fit efficiently. Simplified models must negotiate a tradeoff between tractability, predictive power and ease of interpretation. We present a new modelling paradigm for estimating predictive mechanistic-like models of neurons and small circuits that navigates these issues using methods from systems theory. The key insight is that membrane currents can be modelled using two scalable system components optimized for learning: linear state space models, and nonlinear artificial neural networks (ANNs). Combining these components, we construct two types of membrane currents: lumped currents, which are flexible, and data-driven conductance-based currents, which are interpretable. The resulting class of models -- which we call Recurrent Mechanistic Models (RMMs) -- can be trained in a matter of seconds to minutes on intracellular recordings during an electrophysiology experiment, representing a step change in performance over previous approaches. As a proof-of-principle, we use RMMs to learn the dynamics of two groups of neurons, and their synaptic connections, in the Stomatogastric Ganglion (STG), a well-known central pattern generator. We show that RMMs are efficiently trained using teacher forcing and multiple shooting. Due to their reliability, efficiency and interpretability, RMMs enable qualitatively new kinds of experiments using predictive models in closed-loop neurophysiology and online estimation of neural properties in living preparations. Significance StatementOur ability to understand the nervous system has been hindered by the difficulty of constructing good predictive models of neurons and circuits. This difficulty persists despite vast accumulated knowledge of how the basic components of the nervous system work, marking a gap in our ability to explain neural dynamics in terms of underlying mechanisms. This work describes a new data-driven modelling approach for neurons and small circuits that combines predictive power with mechanistic interpretation.

neuroscience↗

bioRxiv2021

RECIPROCALLY INHIBITORY CIRCUITS OPERATING WITH DISTINCT MECHANISMS ARE DIFFERENTLY ROBUST TO PERTURBATION AND MODULATION

What features are important for circuit robustness? Reciprocal inhibition is a building block in many circuits. We used dynamic clamp to create reciprocally inhibitory circuits from pharmacologically isolated neurons of the crab stomatogastric ganglion by injecting artificial synaptic (ISyn) and hyperpolarization-activated inward (IH) currents. There are two mechanisms of antiphase oscillations in these circuits: "escape" and "release". In release, the active neuron primarily controls the off/on transitions. In escape, the inhibited neuron controls the transitions. We characterized the robustness of escape and release circuits to alterations in circuit parameters, temperature, and neuromodulation. We found that escape circuits rely on tight correlations between synaptic and H conductances to generate bursting but are resilient to temperature increase. Release circuits are robust to variations in synaptic and H conductances but fragile to temperature increase. The modulatory current (IMI) restores oscillations in release circuits but has little effect in escape circuits. Thus, the same perturbation can have dramatically different effects depending on the circuits mechanism of operation that may not be observable from basal circuit activity.

neuroscience↗

bioRxiv2019

Rapid adaptation to elevated extracellular potassium in the pyloric circuit of the crab, Cancer borealis

Elevated [K+] is often used to alter excitability in neurons and networks by shifting the potassium equilibrium potential (EK) and, consequently, the resting membrane potential. We studied the effects of increased extracellular [K+] on the well-described pyloric circuit of the crab, Cancer borealis. A 2.5-fold increase in extracellular [K+] (2.5x[K+]) depolarized Pyloric Dilator (PD) neurons and resulted in short-term loss of their normal bursting activity. This period of silence was followed within 5-10 minutes by the recovery of spiking and/or bursting activity during continued superfusion of 2.5x[K+] saline. In contrast, when PD neurons were pharmacologically isolated from pyloric presynaptic inputs, they exhibited no transient loss of spiking activity in 2.5x[K+], suggesting the presence of an acute inhibitory effect mediated by circuit interactions. Action potential threshold in PD neurons hyperpolarized during an hour-long exposure to 2.5x[K+] concurrent with the recovery of spiking and/or bursting activity. Thus, the initial loss of activity appears to be mediated by synaptic interactions within the network, but the secondary adaptation depends on changes in the intrinsic excitability of the pacemaker neurons. The complex sequence of events in the responses of pyloric neurons to elevated [K+] demonstrates that electrophysiological recordings are necessary to determine both the transient and longer-term effects of even modest alterations of K+ concentrations on neuronal activity. Significance StatementSolutions with elevated extracellular potassium are commonly used as a depolarizing stimulus. Moreover, hyperkalemia is associated with a number of disease states, including epileptic seizures and brain traumas. We studied the effects of high [K+] saline on the well-described pyloric circuit of the crab stomatogastric ganglion. A 2.5-fold increase in extracellular [K+] led to a transient loss of activity in pyloric neurons that was not due to depolarization block. This was followed by a rapid increase in excitability and concurrent recovery of spiking and rhythmic bursting activity within minutes. These results suggest that effects of high [K+] on neuronal circuits can be complex and non-stationary.

neuroscience↗