Biology subjects

Kraynyukova, N.

Publications and source records attributed to Kraynyukova, N..

3 recordsLinked to original sources

bioRxiv2025

An integrative model of AMPA receptor trafficking reveals the central contribution of local translation in subtype-specific kinetics

AMPA-type glutamate receptors (AMPARs) underlie most of the excitatory synaptic transmission in the brain and are crucial for implementing long-term synaptic plasticity. AMPARs are multi-protein complexes composed of two types of subunits: pore-forming subunits GluA1-4 that assemble in the endoplasmic reticulum and form the glutamate-gated ion channel, and auxiliary subunits that modulate receptor bio-physical properties and mediate their forward trafficking to the plasma membrane. Here, using a combination of theoretical and experimental approaches, we elucidate the kinetics of essential trafficking steps and the protein sources necessary to explain the experimentally observed distribution of AMPARs and the response of different AMPAR subtypes to LTP induction. Our data indicate that the mRNA coding for one of the most abundant AMPAR auxiliary subunits, CNIH-2, is abundant in dendrites. Consistent with this mRNA distribution, CNIH-2 is locally synthesized. In contrast, the pore-forming subunits GluA1 and GluA2 are mostly synthesized in the cell body. CNIH-2 synthesis increases after the (chemical) induction of long-term potentiation. Strikingly, the translation of CNIH-2 is required for the plasma membrane insertion of GluA2-containing receptors and not GluA1-homomeric AMPARs. Using the selective trafficking of GluA2-containing AMPARs by CNIH-2, our computational model can explain the distinct temporal profiles in response to plasticity induction of two major subtypes of AMPARs, the slow-response of the calciumimpermeable (GluA2-containing) and fast kinetics of the calcium-permeable (GluA2-lacking) AMPARs.

neuroscience↗

bioRxiv2022

Targeting diverse operational regimes in recurrent spiking networks

Neural computations emerge from recurrent neural circuits that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require tractable spiking network models that can consistently incorporate new information about the network structure and reproduce the recorded neural activity features. However, it is challenging to predict which spiking network connectivity configurations and neural properties can generate fundamental operational states and specific experimentally reported nonlinear cortical computations. Theoretical descriptions for the computational state of cortical spiking circuits are diverse, including the balanced state where excitatory and inhibitory inputs balance almost perfectly or the inhibition stabilized state (ISN) where the excitatory part of the circuit is unstable. It remains an open question whether these states can co-exist with experimentally reported nonlinear computations and whether they can be recovered in biologically realistic implementations of spiking networks. Here, we show how to identify spiking network connectivity patterns underlying diverse nonlinear computations such as XOR, bistability, inhibitory stabilization, supersaturation, and persistent activity. We established a mapping between the stabilized supralinear network (SSN) and spiking activity which allowed us to pinpoint the location in parameter space where these activity regimes occur. Notably, we found that biologically-sized spiking networks can have irregular asynchronous activity that does not require strong excitation-inhibition balance or large feedforward input and we showed that the dynamic firing rate trajectories in spiking networks can be precisely targeted without error-driven training algorithms.

neuroscience↗

bioRxiv2020

Structure and variability of optogenetic responses identify the operating regime of cortex

The visual cortex receives non-sensory inputs containing behavioral and brain state information. Here we propose a parallel between optogenetic and behavioral modulations of activity and characterize their impact on cell-type-specific V1 processing under a common theoretical framework. We infer cell-type-specific circuitry from large-scale V1 recordings and demonstrate that, given strong recurrent excitation, the cell-type-specific responses imply key aspects of the known connectivity. In the inferred models, parvalbumin-expressing (PV), but not other, interneurons have responses to perturbations that we show theoretically imply that their activity stabilizes the circuit. We infer inputs that explain locomotion-induced changes in firing rates and find that, contrary to hypotheses of simple disinhibition, locomotory drive to VIP cells and to SOM cells largely cancel, with enhancement of excitatory-cell visual responses likely due to direct locomotory drive to them. We show that this SOM/VIP cancellation is a property emerging from V1 connectivity structure.

neuroscience↗