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Kristine Heiney

Bio: Kristine Heiney is an academic researcher from Norwegian University of Science and Technology. The author has contributed to research in topics: Computational model & Biological neural network. The author has an hindex of 3, co-authored 11 publications receiving 27 citations. Previous affiliations of Kristine Heiney include Metropolitan University & University of Oslo.

Papers
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Journal ArticleDOI
TL;DR: In this article, the authors highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders.
Abstract: It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed "neuronal avalanches" The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered

16 citations

Journal ArticleDOI
TL;DR: An advanced yet easy-to-use publically available computational tool, μSpikeHunter, which provides a detailed quantification of several communication-related properties such as propagation velocity, conduction failure, spike timings, and coding mechanisms is developed.
Abstract: Understanding neuronal communication is fundamental in neuroscience, but there are few methodologies offering detailed analysis for well-controlled conditions. By interfacing microElectrode arrays with microFluidics (μEF devices), it is possible to compartmentalize neuronal cultures with a specified alignment of axons and microelectrodes. This setup allows the extracellular recording of spike propagation with a high signal-to-noise ratio over the course of several weeks. Addressing these μEF devices, we developed an advanced yet easy-to-use publically available computational tool, μSpikeHunter, which provides a detailed quantification of several communication-related properties such as propagation velocity, conduction failure, spike timings, and coding mechanisms. The combination of μEF devices and μSpikeHunter can be used in the context of standard neuronal cultures or with co-culture configurations where, for example, communication between sensory neurons and other cell types is monitored and assessed. The ability to analyze axonal signals (in a user-friendly, time-efficient, high-throughput manner) opens the door to new approaches in studies of peripheral innervation, neural coding, and neuroregeneration, among many others. We demonstrate the use of μSpikeHunter in dorsal root ganglion neurons where we analyze the presence of both anterograde and retrograde signals in μEF devices. A fully functional version of µSpikeHunter is publically available for download from https://github.com/uSpikeHunter .

11 citations

Proceedings ArticleDOI
01 Dec 2019
TL;DR: In this article, the authors report the preliminary analysis of the electrophysiological behavior of in vitro neuronal networks to identify when the networks are in a critical state based on the size distribution of network-wide avalanches of activity.
Abstract: In this work, we report the preliminary analysis of the electrophysiological behavior of in vitro neuronal networks to identify when the networks are in a critical state based on the size distribution of network-wide avalanches of activity. The results presented here demonstrate the importance of selecting appropriate parameters in the evaluation of the size distribution and indicate that it is possible to perturb networks showing highly synchronized—or supercritical—behavior into the critical state by increasing the level of inhibition in the network. The classification of critical versus non-critical networks is valuable in identifying networks that can be expected to perform well on computational tasks, as criticality is widely considered to be the state in which a system is best suited for computation. In addition to enabling the identification of networks that are well-suited for computation, this analysis is expected to aid in the classification of networks as perturbed or healthy. This study is part of a larger research project, the overarching aim of which is to develop computational models that are able to reproduce target behaviors observed in in vitro neuronal networks. These models will ultimately be used to aid in the realization of these behaviors in nanomagnet arrays to be used in novel computing hardwares.

8 citations

Journal ArticleDOI
TL;DR: A patterned spread of proteinopathy represents a common characteristic of many neurodegenerative diseases, such as Parkinson's disease as discussed by the authors, where misfolded forms of α-synuclein proteins accumulate in hallmar...
Abstract: A patterned spread of proteinopathy represents a common characteristic of many neurodegenerative diseases. In Parkinson’s disease (PD), misfolded forms of α-synuclein proteins accumulate in hallmar...

5 citations

Posted Content
TL;DR: SpikeHunter as mentioned in this paper is an open-source computational tool that provides detailed quantification of several communication-related properties such as propagation velocity, conduction failure, spike timings, and coding mechanisms.
Abstract: Understanding neuronal communication is fundamental in neuroscience but there are few methodologies offering detailed analysis for well-controlled conditions. By interfacing microElectrode arrays with microFluidics ({\mu}EF devices), it is possible to compartmentalize neuronal cultures with a specified alignment of axons and microelectrodes. This setup allows extracellular recordings of spike propagation with high signal-to-noise ratio over the course of several weeks. Addressing these {\mu}EF systems we developed an advanced, yet easy-to-use, open-source computational tool, {\mu}SpikeHunter, which provides detailed quantification of several communication-related properties such as propagation velocity, conduction failure, spike timings, and coding mechanisms. The combination of {\mu}EF devices and {\mu}SpikeHunter can be used in the context of standard neuronal cultures or with co-culture configurations where, for example, communication between sensory neurons and other cell types is monitored and assessed. The ability to analyze axonal signals (in a user-friendly, time-efficient, high-throughput manner) opens doors to new approaches to studies of peripheral innervation, neural coding, and neuroregeneration approaches, among many others. We demonstrate the use of {\mu}SpikeHunter in dorsal root ganglion neurons where we analyze the presence of anterograde signals in {\mu}EF devices.

5 citations


Cited by
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Journal Article
TL;DR: In this article, the authors characterized the dopaminergic nature of striatal tyrosine hydroxylase-immunoreactive (TH-i) cells and their regulatory response to nigrostriatal deafferentation.
Abstract: Intrinsic, striatal tyrosine hydroxylase-immunoreactive (TH-i) cells have received little consideration. In this study we have characterized these neurons and their regulatory response to nigrostriatal dopaminergic deafferentation. TH-i cells were observed in the striatum of both control and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated monkeys; TH-i cell counts, however, were 3.5-fold higher in the striatum of MPTP-lesioned monkeys. To establish the dopaminergic nature of the TH-i cells, sections were double-labeled with antibodies to dopamine transporter (DAT). Immunofluorescence studies demonstrated that nearly all TH-i cells were double-labeled with DAT, suggesting that they contain the machinery to be functional dopaminergic neurons. Two types of TH-i cells were identified in the striatum: small, aspiny, bipolar cells with varicose dendrites and larger spiny, multipolar cells. The aspiny cells, which were more prevalent, corresponded morphologically to the GABAergic interneurons of the striatum. Double-label immunofluorescence studies using antibodies to TH and glutamate decarboxylase (GAD67), the synthetic enzyme for GABA, showed that 99% of the TH-i cells were GAD67-positive. Very few (

241 citations

Journal ArticleDOI
TL;DR: Criticality is defined as the singular state of complex systems poised at the brink of a phase transition between order and randomness as discussed by the authors , i.e. the property of a process whose trajectory in phase space is sensitive to small differences in initial conditions.

46 citations

Journal ArticleDOI
20 Mar 2020-eLife
TL;DR: It is shown that in rat hippocampal neurons the MPS is an actomyosin network that controls axonal expansion and contraction, and that MPS destabilization through NMII inactivation affects axonal electrophysiology, increasing action potential conduction velocity.
Abstract: Neurons have a membrane periodic skeleton (MPS) composed of actin rings interconnected by spectrin. Here, combining chemical and genetic gain- and loss-of-function assays, we show that in rat hippocampal neurons the MPS is an actomyosin network that controls axonal expansion and contraction. Using super-resolution microscopy, we analyzed the localization of axonal non-muscle myosin II (NMII). We show that active NMII light chains are colocalized with actin rings and organized in a circular periodic manner throughout the axon shaft. In contrast, NMII heavy chains are mostly positioned along the longitudinal axonal axis, being able to crosslink adjacent rings. NMII filaments can play contractile or scaffolding roles determined by their position relative to actin rings and activation state. We also show that MPS destabilization through NMII inactivation affects axonal electrophysiology, increasing action potential conduction velocity. In summary, our findings open new perspectives on axon diameter regulation, with important implications in neuronal biology.

44 citations

Journal ArticleDOI
TL;DR: In this article, the authors highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders.
Abstract: It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed "neuronal avalanches" The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered

16 citations

Journal ArticleDOI
22 Oct 2021
TL;DR: In this paper, the authors study the field-driven spin dynamics on the hysteresis loop in a network with higher-order structures described by simplicial complexes, and demonstrate how the self-organised criticality occurs at the interplay of the complex topology and driving mode.
Abstract: Studies of many complex systems have revealed new collective behaviours that emerge through the mechanisms of self-organised critical fluctuations. Subject to the external and endogenous driving forces, these collective states with long-range spatial and temporal correlations often arise from the intrinsic dynamics with the threshold nonlinearity and geometry-conditioned interactions. The self-similarity of critical fluctuations enables us to describe the system using fewer parameters and universal functions that, on the other hand, can simplify the computational and information complexity. Currently, the cutting-edge research on self-organised critical systems across the scales strives to formulate a unifying mathematical framework, utilise the critical universal properties in information theory, and decipher the role of hidden geometry. As a prominent example, we study the field-driven spin dynamics on the hysteresis loop in a network with higher-order structures described by simplicial complexes, which provides a geometric-frustration environment. While providing motivational illustrations from physical, biological, and social systems, along with their networks, we also demonstrate how the self-organised criticality occurs at the interplay of the complex topology and driving mode. This study opens up new promising routes with powerful tools to address a long-standing challenge in the theory and applications of complexity science ingrained in the efficient analysis of self-organised critical states under the competing higher-order interactions embedded in complex geometries.

16 citations