About

Alla Borisyuk is a Professor with a Ph.D. from the University of Utah, affiliated with the Department of Mathematics and the Interdepartmental Program in Neuroscience. Her research field is Computational Neuroscience, where she employs mathematical and computational tools to contribute to the understanding of neurons and brain function. She works on both theoretical projects and collaborative efforts directly related to experimental studies. Her research has included applications in the auditory system, odor coding in the olfactory system, and the emergence of oscillations in noisy networks. She is actively involved in mentoring students and fostering research collaborations, and she is engaged with professional organizations such as the Organization for Computational Neurosciences (OCNS), SIAM Life Sciences Society, and the Society for Neuroscience (SFN).

Research topics

• Computer Science
• Biology
• Neuroscience
• Artificial Intelligence
• Biological system

Selected publications

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

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-11

  articleOpen access

  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.

  PublisherDOI

• The financial aspect of evaluating the effectiveness of an organization in the field of industrial production

  Sibirskaya finansovaya shkola · 2025-04-18

  articleOpen accessSenior author

  Improving operational efficiency is an urgent task for all organizations. The concept of efficiency in this context is inextricably linked to the success of an organization, which is determined by achieving high economic results and ensuring competitive advantages in the field of resource management. Currently, in conditions of high competition and a constantly changing external environment, the issue of maintaining and improving the efficiency of any commercial organization, including in the field of industrial production, is the most acute and requires modern solutions.

  PublisherOA PDFDOI

• A Role of Environmental Complexity on Representation Learning in Deep Reinforcement Learning Agents

  arXiv (Cornell University) · 2024-07-03

  preprintOpen accessSenior author

  We developed a simulated environment to train deep reinforcement learning agents on a shortcut usage navigation task, motivated by the Dual Solutions Paradigm test used for human navigators. We manipulated the frequency with which agents were exposed to a shortcut and a navigation cue, to investigate how these factors influence shortcut usage development. We find that all agents rapidly achieve optimal performance in closed shortcut trials once initial learning starts. However, their navigation speed and shortcut usage when it is open happen faster in agents with higher shortcut exposure. Analysis of the agents' artificial neural networks activity revealed that frequent presentation of a cue initially resulted in better encoding of the cue in the activity of individual nodes, compared to agents who encountered the cue less often. However, stronger cue representations were ultimately formed through the use of the cue in the context of navigation planning, rather than simply through exposure. We found that in all agents, spatial representations develop early in training and subsequently stabilize before navigation strategies fully develop, suggesting that having spatially consistent activations is necessary for basic navigation, but insufficient for advanced strategies. Further, using new analysis techniques, we found that the planned trajectory rather than the agent's immediate location is encoded in the agent's networks. Moreover, the encoding is represented at the population rather than the individual node level. These techniques could have broader applications in studying neural activity across populations of neurons or network nodes beyond individual activity patterns.

  PublisherOA PDFDOI

• Investigating the ability of astrocytes to drive neural network synchrony

  PLoS Computational Biology · 2023 · 13 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Neuroscience

  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.

  PublisherOA PDFDOI

• Investigating Navigation Strategies in the Morris Water Maze through Deep Reinforcement Learning

  arXiv (Cornell University) · 2023-06-01 · 1 citations

  preprintOpen accessSenior author

  Navigation is a complex skill with a long history of research in animals and humans. In this work, we simulate the Morris Water Maze in 2D to train deep reinforcement learning agents. We perform automatic classification of navigation strategies, analyze the distribution of strategies used by artificial agents, and compare them with experimental data to show similar learning dynamics as those seen in humans and rodents. We develop environment-specific auxiliary tasks and examine factors affecting their usefulness. We suggest that the most beneficial tasks are potentially more biologically feasible for real agents to use. Lastly, we explore the development of internal representations in the activations of artificial agent neural networks. These representations resemble place cells and head-direction cells found in mouse brains, and their presence has correlation to the navigation strategies that artificial agents employ.

  PublisherOA PDFDOI

• Investigating navigation strategies in the Morris Water Maze through deep reinforcement learning

  Neural Networks · 2023-12-14 · 15 citations

  articleSenior authorCorresponding
  PublisherDOI

• Dynamical Systems: Overview

  Encyclopedia of Computational Neuroscience · 2022-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Investigating the ability of astrocytes to drive neural network synchrony

  bioRxiv (Cold Spring Harbor Laboratory) · 2022-09-27 · 1 citations

  preprintOpen accessSenior authorCorresponding

  Abstract 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 summary In 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.

  PublisherOA PDFDOI

• Morris–Lecar Model

  Encyclopedia of Computational Neuroscience · 2022-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Effect of interglomerular inhibitory networks on olfactory bulb odor representations

  bioRxiv (Cold Spring Harbor Laboratory) · 2020-03-05 · 1 citations

  preprintOpen access

  Abstract 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 Statement Lateral 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.

  PublisherOA PDFDOI

Recent grants

• Incorporating the Effects of Synaptic Ensheathment in Neuronal Networks: A Multi-scale Investigation

  NSF · $160k · 2019–2024

• Coding of timing information in the auditory system

  NSF · $150k · 2010–2015

Frequent coauthors

• John Rinzel

  New York University

  7 shared

• Gregory Handy

  University of Chicago

  6 shared

• Matt Wachowiak

  University of Utah

  4 shared

• Daniel Zavitz

  Washington University in St. Louis

  4 shared

• R. G. Terekhov

  S.P. Timoshenko Institute of Mechanics

  3 shared

• Malcolm N. Semple

  New York University

  3 shared

• Michael T. Shipley

  Centers for Disease Control and Prevention

  3 shared

• David Terman

  The Ohio State University

  3 shared

Education

• Ph.D., Mathematics

  University of Utah