M. Jafarian
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5 records found
1
Journal article
(2026)
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W. Dziarnowska, M. Orhun, Yannan Zhu, Nils Kohn, Guillén Fernández, M. Jafarian
Objectives: The interplay between emotion and memory is a central topic in cognitive neuroscience, with open questions about the underlying neuronal mechanisms. This article aims to study the effects of order and intensity of emotional information on associative memory encoding. To this aim, we employ dynamic causal modeling to model the dynamic network composed of the hippocampus, amygdala, and orbitofrontal cortex during an fMRI associative memory encoding task and apply graph and control theory tools to obtain novel insights.
Methods: Participants were clustered into three condition groups, neutral–neutral, neutral–emotional, or emotional–emotional, and viewed image pairs associated with their assigned condition. Using the dynamic causal modeling framework, we explore several dynamic models and show that a stochastic bilinear state-space model best describes the neuronal dynamics in all conditions. Furthermore, we use graph and control theory techniques to both validate and analyze the model. Particularly, we analyze the network dynamics of each condition using tools from graph theory and stability theory and discuss the differences in the strength and direction of connectivity as well as stability of each of these networks.
Results: We confirm the prior finding that memory is enhanced in the neutral-emotional condition. In our work, this enhanced memory is associated with the increased hippocampus–amygdala coupling strength in this condition. Moreover, we show that in the emotional–emotional condition, coupling of hippocampus and amygdala, as well as the whole network connectivity increases. We further predict that the hippocampus–amygdala connectivity in this condition increases, when the first image's valence is substantially less negative rated than the second image, but decreases otherwise. This pattern mirrors the neutral–emotional condition, where the first image is emotionally neutral compared with the second. Moreover, our model-based analyses suggest that the amygdala predominantly influences the other two regions in the neutral–emotional condition.
Conclusion: Combined data-driven DCM modeling, stability analyses, and graph-theory tools led to new insights and enhanced the mechanistic understanding of dynamics of emotional associative memory. We discuss these insights, utilize these analytical tools to generalize our findings to some unmeasured conditions, and highlight the potential of these techniques to inform the development of future technological or pharmacological approaches targeting regulatory mechanisms. ...
Methods: Participants were clustered into three condition groups, neutral–neutral, neutral–emotional, or emotional–emotional, and viewed image pairs associated with their assigned condition. Using the dynamic causal modeling framework, we explore several dynamic models and show that a stochastic bilinear state-space model best describes the neuronal dynamics in all conditions. Furthermore, we use graph and control theory techniques to both validate and analyze the model. Particularly, we analyze the network dynamics of each condition using tools from graph theory and stability theory and discuss the differences in the strength and direction of connectivity as well as stability of each of these networks.
Results: We confirm the prior finding that memory is enhanced in the neutral-emotional condition. In our work, this enhanced memory is associated with the increased hippocampus–amygdala coupling strength in this condition. Moreover, we show that in the emotional–emotional condition, coupling of hippocampus and amygdala, as well as the whole network connectivity increases. We further predict that the hippocampus–amygdala connectivity in this condition increases, when the first image's valence is substantially less negative rated than the second image, but decreases otherwise. This pattern mirrors the neutral–emotional condition, where the first image is emotionally neutral compared with the second. Moreover, our model-based analyses suggest that the amygdala predominantly influences the other two regions in the neutral–emotional condition.
Conclusion: Combined data-driven DCM modeling, stability analyses, and graph-theory tools led to new insights and enhanced the mechanistic understanding of dynamics of emotional associative memory. We discuss these insights, utilize these analytical tools to generalize our findings to some unmeasured conditions, and highlight the potential of these techniques to inform the development of future technological or pharmacological approaches targeting regulatory mechanisms. ...
Objectives: The interplay between emotion and memory is a central topic in cognitive neuroscience, with open questions about the underlying neuronal mechanisms. This article aims to study the effects of order and intensity of emotional information on associative memory encoding. To this aim, we employ dynamic causal modeling to model the dynamic network composed of the hippocampus, amygdala, and orbitofrontal cortex during an fMRI associative memory encoding task and apply graph and control theory tools to obtain novel insights.
Methods: Participants were clustered into three condition groups, neutral–neutral, neutral–emotional, or emotional–emotional, and viewed image pairs associated with their assigned condition. Using the dynamic causal modeling framework, we explore several dynamic models and show that a stochastic bilinear state-space model best describes the neuronal dynamics in all conditions. Furthermore, we use graph and control theory techniques to both validate and analyze the model. Particularly, we analyze the network dynamics of each condition using tools from graph theory and stability theory and discuss the differences in the strength and direction of connectivity as well as stability of each of these networks.
Results: We confirm the prior finding that memory is enhanced in the neutral-emotional condition. In our work, this enhanced memory is associated with the increased hippocampus–amygdala coupling strength in this condition. Moreover, we show that in the emotional–emotional condition, coupling of hippocampus and amygdala, as well as the whole network connectivity increases. We further predict that the hippocampus–amygdala connectivity in this condition increases, when the first image's valence is substantially less negative rated than the second image, but decreases otherwise. This pattern mirrors the neutral–emotional condition, where the first image is emotionally neutral compared with the second. Moreover, our model-based analyses suggest that the amygdala predominantly influences the other two regions in the neutral–emotional condition.
Conclusion: Combined data-driven DCM modeling, stability analyses, and graph-theory tools led to new insights and enhanced the mechanistic understanding of dynamics of emotional associative memory. We discuss these insights, utilize these analytical tools to generalize our findings to some unmeasured conditions, and highlight the potential of these techniques to inform the development of future technological or pharmacological approaches targeting regulatory mechanisms.
Methods: Participants were clustered into three condition groups, neutral–neutral, neutral–emotional, or emotional–emotional, and viewed image pairs associated with their assigned condition. Using the dynamic causal modeling framework, we explore several dynamic models and show that a stochastic bilinear state-space model best describes the neuronal dynamics in all conditions. Furthermore, we use graph and control theory techniques to both validate and analyze the model. Particularly, we analyze the network dynamics of each condition using tools from graph theory and stability theory and discuss the differences in the strength and direction of connectivity as well as stability of each of these networks.
Results: We confirm the prior finding that memory is enhanced in the neutral-emotional condition. In our work, this enhanced memory is associated with the increased hippocampus–amygdala coupling strength in this condition. Moreover, we show that in the emotional–emotional condition, coupling of hippocampus and amygdala, as well as the whole network connectivity increases. We further predict that the hippocampus–amygdala connectivity in this condition increases, when the first image's valence is substantially less negative rated than the second image, but decreases otherwise. This pattern mirrors the neutral–emotional condition, where the first image is emotionally neutral compared with the second. Moreover, our model-based analyses suggest that the amygdala predominantly influences the other two regions in the neutral–emotional condition.
Conclusion: Combined data-driven DCM modeling, stability analyses, and graph-theory tools led to new insights and enhanced the mechanistic understanding of dynamics of emotional associative memory. We discuss these insights, utilize these analytical tools to generalize our findings to some unmeasured conditions, and highlight the potential of these techniques to inform the development of future technological or pharmacological approaches targeting regulatory mechanisms.
This paper studies the problem of output regulation for a class of nonlinear systems experiencing matched input disturbances. It is assumed that the disturbance signal is generated by an external autonomous dynamical system. First, we show that for a class of nonlinear systems admitting a finite-dimensional Koopman representation, the problem is equivalent to a bilinear output regulation. We then prove that a linear dynamic output feedback controller, inspired by the linear output regulation framework, locally solves the original nonlinear problem. Numerical results validate our analysis.
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This paper studies the problem of output regulation for a class of nonlinear systems experiencing matched input disturbances. It is assumed that the disturbance signal is generated by an external autonomous dynamical system. First, we show that for a class of nonlinear systems admitting a finite-dimensional Koopman representation, the problem is equivalent to a bilinear output regulation. We then prove that a linear dynamic output feedback controller, inspired by the linear output regulation framework, locally solves the original nonlinear problem. Numerical results validate our analysis.
This article studies stochastic relative phase stability, i.e., stochastic phase-cohesiveness, of discrete-time phase-coupled oscillators. The stochastic phase-cohesiveness in two types of networks is studied. First, we consider oscillators coupled with 2π-periodic odd functions over underlying undirected graphs subject to both multiplicative and additive stochastic uncertainties. We prove stochastic phase-cohesiveness of the network with respect to two specific, namely, in-phase and antiphase, sets by deriving sufficient coupling conditions. We show the dependency of these conditions on the size of the mean values of additive and multiplicative uncertainties, as well as the sign of the mean values of multiplicative uncertainties. Furthermore, we discuss the results under a relaxation of the odd property of the coupling function. Second, we study an uncertain network in which the multiplicative uncertainties are governed by the Bernoulli process representing the well-known Erdös-Rényi network. We assume constant exogenous frequencies and derive sufficient conditions for achieving both stochastic phase-cohesive and phase-locked solutions, i.e., stochastic phase-cohesiveness with respect to the origin. For the latter case, where identical exogenous frequencies are assumed, we prove that any positive probability of connectivity leads to phase-locking. Thorough analyses are provided, and insights obtained from stochastic analysis are discussed, along with numerical simulations to validate the analytical results.
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This article studies stochastic relative phase stability, i.e., stochastic phase-cohesiveness, of discrete-time phase-coupled oscillators. The stochastic phase-cohesiveness in two types of networks is studied. First, we consider oscillators coupled with 2π-periodic odd functions over underlying undirected graphs subject to both multiplicative and additive stochastic uncertainties. We prove stochastic phase-cohesiveness of the network with respect to two specific, namely, in-phase and antiphase, sets by deriving sufficient coupling conditions. We show the dependency of these conditions on the size of the mean values of additive and multiplicative uncertainties, as well as the sign of the mean values of multiplicative uncertainties. Furthermore, we discuss the results under a relaxation of the odd property of the coupling function. Second, we study an uncertain network in which the multiplicative uncertainties are governed by the Bernoulli process representing the well-known Erdös-Rényi network. We assume constant exogenous frequencies and derive sufficient conditions for achieving both stochastic phase-cohesive and phase-locked solutions, i.e., stochastic phase-cohesiveness with respect to the origin. For the latter case, where identical exogenous frequencies are assumed, we prove that any positive probability of connectivity leads to phase-locking. Thorough analyses are provided, and insights obtained from stochastic analysis are discussed, along with numerical simulations to validate the analytical results.
Journal article
(2023)
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Matin Jafarian, David Chavez Huerta, Gianluca Villani, Anders Lansner, Karl H. Johansson
This article studies free recall, i.e., the reactivation of stored memory items, namely patterns , in any order, of a model of working memory. Our free recall model is based on a biologically plausible modular neural network composed of H modules, namely hypercolumns , each of which is a bundle of M minicolumns . The coupling weights and constant bias values of the network are determined by a Hebbian plasticity rule. Using techniques from nonlinear stability theory, we show that cluster synchronization is the central mechanism governing free recall of orthogonally encoded patterns. Particularly, we show that free recall's cluster synchronization is the combination of two main mechanisms: simultaneous activities of minicolumns representing an encoded pattern, i.e., within-pattern synchronization, together with time-divided activities of minicolumns representing different patterns. We characterize the coupling and bias value conditions under which cluster synchronization emerges. We also discuss the role of heterogeneous coupling weights and bias values of minicolumns' dynamics in free recall. Specifically, we compare the behaviour of two H×2 networks with identical and non-identical coupling weights and bias values. For these two networks, we obtain bounds on couplings and bias values under which both encoded patterns are recalled. Our analysis shows that having non-identical couplings and bias values for different patterns increases the possibility of their free recall. Numerical simulations are given to validate the theoretical analysis.
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This article studies free recall, i.e., the reactivation of stored memory items, namely patterns , in any order, of a model of working memory. Our free recall model is based on a biologically plausible modular neural network composed of H modules, namely hypercolumns , each of which is a bundle of M minicolumns . The coupling weights and constant bias values of the network are determined by a Hebbian plasticity rule. Using techniques from nonlinear stability theory, we show that cluster synchronization is the central mechanism governing free recall of orthogonally encoded patterns. Particularly, we show that free recall's cluster synchronization is the combination of two main mechanisms: simultaneous activities of minicolumns representing an encoded pattern, i.e., within-pattern synchronization, together with time-divided activities of minicolumns representing different patterns. We characterize the coupling and bias value conditions under which cluster synchronization emerges. We also discuss the role of heterogeneous coupling weights and bias values of minicolumns' dynamics in free recall. Specifically, we compare the behaviour of two H×2 networks with identical and non-identical coupling weights and bias values. For these two networks, we obtain bounds on couplings and bias values under which both encoded patterns are recalled. Our analysis shows that having non-identical couplings and bias values for different patterns increases the possibility of their free recall. Numerical simulations are given to validate the theoretical analysis.
Astrocytic gliotransmission as a pathway for stable stimulation of post-synaptic spiking
Implications for working memory
The brain consists not only of neurons but also of non-neuronal cells, including astrocytes. Recent discoveries in neuroscience suggest that astrocytes directly regulate neuronal activity by releasing gliotransmitters such as glutamate. In this paper, we consider a biologically plausible mathematical model of a tripartite neuron-astrocyte network. We study the stability of the nonlinear astrocyte dynamics, as well as its role in regulating the firing rate of the postsynaptic neuron. We show that astrocytes enable storing neuronal information temporarily. Motivated by recent findings on the role of astrocytes in explaining mechanisms of working memory, we numerically verify the utility of our analysis in showing the possibility of two competing theories of persistent and sparse neuronal activity of working memory.
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The brain consists not only of neurons but also of non-neuronal cells, including astrocytes. Recent discoveries in neuroscience suggest that astrocytes directly regulate neuronal activity by releasing gliotransmitters such as glutamate. In this paper, we consider a biologically plausible mathematical model of a tripartite neuron-astrocyte network. We study the stability of the nonlinear astrocyte dynamics, as well as its role in regulating the firing rate of the postsynaptic neuron. We show that astrocytes enable storing neuronal information temporarily. Motivated by recent findings on the role of astrocytes in explaining mechanisms of working memory, we numerically verify the utility of our analysis in showing the possibility of two competing theories of persistent and sparse neuronal activity of working memory.