Biology subjects

Borisyuk, A.

Publications and source records attributed to Borisyuk, A..

6 recordsLinked to original sources

bioRxiv2026

Low-dimensional factorized neural computations underlie risk-adaptive choices

Real-world decision-making rarely occurs with perfect information. Instead, individuals must constantly weigh potential rewards against the probability of adverse outcomes.1 Failures of this process can lead to maladaptive decisions associated with reduced lifetime success, and numerous psychiatric disorders such as gambling addictions, bulimia nervosa, and substance use disorder.2,3 The neural computations that facilitate inference about the landscape of potential outcomes remain unclear, but are thought to occur in distributed frontotemporal circuits.4 Here we used deep reinforcement learning agents to predict distinct behavioral strategies and their underlying neural population dynamics during a risky decision-making task. Across a range of training conditions, deep reinforcement learning agents separated into strategies marked by either overly cautious exploration of the reward contingency space or a high-performing, risk-adaptive Bimodal strategy. The internal dynamics of high-performing Bimodal agents formed low-dimensional representations that segregated safe and risky states. In contrast, the cautious exploration agents were associated with more skewed and entangled neural representations. We found remarkably similar dynamical representations and their associated behavioral strategies in neuronal ensemble recordings from human epilepsy patients performing a similar risky decision-making task. These results reveal the structure of dynamical computations that underlie inferences about uncertain outcomes and their associated behavioral strategies.

neuroscience↗

bioRxiv2026

Asymmetric Reinforcement Learning Explains Human Choice Patterns in Decision-making Under Risk

Human decisions under uncertainty are shaped by experience, but the computations that translate expectation and experience into choice remain debated in neural and cognitive science. Prior studies highlight reinforcement learning (RL) as a unifying framework, yet it is unclear whether human behavior under risk is better captured by symmetric updating from outcomes or by asymmetric learning that weights reward and loss differently. This work examines which learning strategies better explain trial-by-trial choices given contextual uncertainty and manipulations of outcome distributions. Our results show that a Risk Sensitive (RS) model with asymmetric learning rates best explains human behavior in our novel decision-making task. Fitting candidate models to individual trial histories yielded value signals that predicted both choice and response time. These results highlight that RS model, as an asymmetric learning provides a concise and identifiable account of behavior in decision-making under risk tasks.

neuroscience↗

bioRxiv2025

Protein Kinase C positively regulates peroxisome biogenesis by promoting peroxisome-endoplasmic reticulum interaction

Peroxisomes carry out a diverse set of metabolic functions, including oxidation of very long-chained fatty acids, degradation of D-amino acids and hydrogen peroxide, and bile acid production. Many of these functions are upregulated on demand, therefore cells control peroxisome abundance, and by extension peroxisome function, in response to environmental and developmental cues. The mechanisms upregulating peroxisomes in mammalian cells have remained unclear. Here we identify a signaling regulatory network and a mechanism that coordinate cellular demand for peroxisomes and peroxisome abundance by regulating peroxisome proliferation. We show that protein kinase C (PKC) promotes peroxisome PEX11b-dependent formation. PKC activation leads to an increase in peroxisome formation, promoting peroxisome-ER contact site formation through inactivation of GSK3{beta}. We show that removal of VAPA and VAPB impairs peroxisome biogenesis and PKC regulation. Inhibition of PKC reduces peroxisome-ER interactions, leading to a decrease in peroxisome abundance. During neuronal differentiation, active PKC leads to a significant increase in peroxisome formation. We propose that peroxisomal regulation by transient active PKC signaling enables rapid and fine-tuned responses to the need for peroxisomal activity.

cell biology↗

bioRxiv2022

Investigating the ability of astrocytes to drive neural network synchrony

Recent experimental works have implicated astrocytes as a significant cell type underlying several neuronal processes in the mammalian brain, from encoding sensory information to neurological disorders. Despite this progress, it is still unclear how astrocytes are communicating with and driving their neuronal neighbors. While previous computational modeling works have helped propose mechanisms responsible for driving these interactions, they have primarily focused on interactions at the synaptic level, with microscale models of calcium dynamics and neurotransmitter diffusion. Since it is computationally infeasible to include the intricate microscale details in a network-scale model, little computational work has been done to understand how astrocytes may be influencing spiking patterns and synchronization of large networks. We overcome this issue by first developing an "effective" astrocyte that can be easily implemented to already established network frameworks. We do this by showing that the astrocyte proximity to a synapse makes synaptic transmission faster, weaker, and less reliable. Thus, our "effective" astrocytes can be incorporated by considering heterogeneous synaptic time constants, which are parametrized only by the degree of astrocytic proximity at that synapse. We then apply our framework to large networks of exponential integrate-and-fire neurons with various spatial structures. Depending on key parameters, such as the number of synapses ensheathed and the strength of this ensheathment, we show that astrocytes can push the network to a synchronous state and exhibit spatially correlated patterns. Author summaryIn many areas of the brain, glial cells called astrocytes wrap their processes around synapses - the points of contact between neurons. The number of wrapped synapses and the tightness of wrapping varies between brain areas and changes during some diseases, such as epilepsy. We investigate the effect that this synaptic ensheathment has on communication between neurons and the resulting collective dynamics of the neuronal network. We present a general, computationally-efficient way to include astrocytes in neuronal networks using an "effective astrocyte" representation derived from detailed microscopic scale models. The resulting hybrid networks allow us to emulate and observe the effect of ensheathment conditions corresponding to different brain areas and disease states. In particular, we find that it makes the networks more likely to switch into a highly correlated regime, contrary to predictions from the traditional neurons-only view. These results open a new perspective on neural network dynamics, where our understanding of conditions for generating correlated brain activity (e.g., rhythms associated with various brain functions, epileptic seizures) needs to be reevaluated.

neuroscience↗

bioRxiv2021

Connectivity patterns shape sensory representation in a cerebellum-like network

Cerebellum-like structures -- such as the insect mushroom body -- are found in many brains and share a basic fan-out-fan-in network architecture. How the specific structural features of these networks give rise to their learning function remains largely unknown. To investigate this structure-function relationship, we developed a minimal computational model of the extensively studied Drosophila melanogaster mushroom body. We show how well-defined connectivity patterns between the Kenyon cells -- the constituent neurons of the mushroom body -- and their input projection neurons endow different functions, enabling the mushroom body to process olfactory information more efficiently. First, biases in the likelihoods at which individual projection neurons connect to Kenyon cells allow the mushroom body to prioritize the learning of particular, ethologically meaningful odors. Second, groups of projection neurons connecting preferentially to the same Kenyon cells facilitate the mushroom body to generalize across similar odors. Altogether, our results demonstrate how different connectivity patterns shape the representation space of a well-studied cerebellum-like network and impact its learning outcomes.

neuroscience↗

bioRxiv2020

Effect of interglomerular inhibitory networks on olfactory bulb odor representations

Lateral inhibition is a fundamental feature of circuits that process sensory information. In the mammalian olfactory system, inhibitory interneurons called short axon cells comprise the first network mediating lateral inhibition between glomeruli, the functional units of early olfactory coding and processing. The connectivity of this network and its impact on odor representations is not well understood. To explore this question, we constructed a computational model of the interglomerular inhibitory network using detailed characterizations of short axon cell morphologies taken from mouse olfactory bulb. We then examined how this network transformed glomerular patterns of odorant-evoked sensory input (taken from previously-published datasets) as a function of the selectivity of interglomerular inhibition. We examined three connectivity schemes: selective (each glomerulus connects to few others with heterogeneous strength), nonselective (glomeruli connect to most others with heterogenous strength) or global (glomeruli connect to all others with equal strength). We found that both selective and nonselective interglomerular networks could mediate heterogeneous patterns of inhibition across glomeruli when driven by realistic sensory input patterns, but that global inhibitory networks were unable to produce input-output transformations that matched experimental data and were poor mediators of intensity-dependent gain control. We further found that networks whose interglomerular connectivity was tuned by sensory input profile decorrelated odor representations more effectively. These results suggest that, despite their multiglomerular innervation patterns, short axon cells are capable of mediating odorant-specific patterns of inhibition between glomeruli that could, theoretically, be tuned by experience or evolution to optimize discrimination of particular odorants. Significance StatementLateral inhibition is a key feature of circuitry in many sensory systems including vision, audition, and olfaction. We investigate how lateral inhibitory networks mediated by short axon cells in the mouse olfactory bulb might shape odor representations as a function of their interglomerular connectivity. Using a computational model of interglomerular connectivity derived from experimental data, we find that short axon cell networks, despite their broad innervation patterns, can mediate heterogeneous patterns of inhibition across glomeruli, and that the canonical model of global inhibition does not generate experimentally observed responses to stimuli. In addition, inhibitory connections tuned by input statistics yield enhanced decorrelation of similar input patterns. These results elucidate how the organization of inhibition between neural elements may affect computations.

neuroscience↗