A stochastic approach to serotonergic fibers in mental disorders

Janušonis, Skirmantas; Detering, Nils

doi:10.1016/j.biochi.2018.07.014

Cited by 16 publications

(39 citation statements)

References 62 publications

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“…A branching point can be unambiguously demonstrated only by examining an individual fiber at high resolution in all three-dimensions, within the physical section (Pratelli et al, 2017 ). Even when a confocal system with high-power objectives is used, a branching point can be difficult to distinguish from fibers crossing each other at sub-micrometer distances (Janušonis and Detering, 2019 ; Janušonis et al, 2019 ). The extent of local sprouting in regeneration also remains an open problem (Hawthorne et al, 2010 ; Jin et al, 2016 ), but current evidence suggests that it may not be significant (Jin et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Serotonergic Axons as Fractional Brownian Motion Paths: Insights Into the Self-Organization of Regional Densities

Janušonis

Detering

Metzler

et al. 2020

Front. Comput. Neurosci.

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All vertebrate brains contain a dense matrix of thin fibers that release serotonin (5-hydroxytryptamine), a neurotransmitter that modulates a wide range of neural, glial, and vascular processes. Perturbations in the density of this matrix have been associated with a number of mental disorders, including autism and depression, but its self-organization and plasticity remain poorly understood. We introduce a model based on reflected Fractional Brownian Motion (FBM), a rigorously defined stochastic process, and show that it recapitulates some key features of regional serotonergic fiber densities. Specifically, we use supercomputing simulations to model fibers as FBM-paths in two-dimensional brain-like domains and demonstrate that the resultant steady state distributions approximate the fiber distributions in physical brain sections immunostained for the serotonin transporter (a marker for serotonergic axons in the adult brain). We suggest that this framework can support predictive descriptions and manipulations of the serotonergic matrix and that it can be further extended to incorporate the detailed physical properties of the fibers and their environment.

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Section: Introductionmentioning

confidence: 99%

Serotonergic Axons as Fractional Brownian Motion Paths: Insights Into the Self-Organization of Regional Densities

Janušonis

Detering

Metzler

et al. 2020

Front. Comput. Neurosci.

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“…Finally, we point out that the tempering of the correlations provides a powerful tool in applications in which a stochastic process is used to model experimental data. For example, FBM was recently put forward as a model to explain the spatial distribution of serotonergic fibers in vertebrate brains [24,25]. Despite the limited "neurobiological input", the model captures important aspects of the highly nonuniform distributions of these fibers throughout the brain.…”

Section: Discussionmentioning

confidence: 99%

“…In the marginal case, α = 1, FBM is identical to normal Brownian motion with uncorrelated increments. FBM processes have been used to describe the motion inside biological cells [18][19][20][21][22][23], the patterns of serotonergic fibers in verebrate brains [24,25]. polymer dynamics [26,27], electronic network traffic [28], as well as fluctuations of financial markets [29,30].…”

Section: Introductionmentioning

confidence: 99%

Tempered fractional Brownian motion on finite intervals

Vojta,

Miller,

Halladay

2021

Preprint

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Diffusive transport in many complex systems features a crossover between anomalous diffusion at short times and normal diffusion at long times. This behavior can be mathematically modeled by cutting off (tempering) beyond a mesoscopic correlation time the power-law correlations between the increments of fractional Brownian motion. Here, we investigate such tempered fractional Brownian motion confined to a finite interval by reflecting walls. Specifically, we explore how the tempering of the long-time correlations affects the strong accumulation and depletion of particles near reflecting boundaries recently discovered for untempered fractional Brownian motion. We find that exponential tempering introduces a characteristic size for the accumulation and depletion zones but does not affect the functional form of the probability density close to the wall. In contrast, power-law tempering leads to more complex behavior that differs between the superdiffusive and subdiffusive cases.

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“…Therefore, the number of fiber contacts received by an individual neuron in any brain region is a random event. The mathematical models of serotonergic trajectories are an active area of research (Janušonis and Detering, 2019; Janušonis et al, 2020). Some features of these fibers are captured by the superdiffusive fractional Brownian motion (FBM), an anomalous diffusion process that generalizes normal Brownian motion.…”

Section: Introductionmentioning

confidence: 99%

Brain Serotonergic Fibers Suggest Anomalous Diffusion-Based Dropout in Artificial Neural Networks

Lee

Zhang

Janušonis

2022

Preprint

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Random dropout has become a standard regularization technique in artificial neural networks (ANNs), but it is currently unknown whether an analogous mechanism exists in biological neural networks (BioNNs). If it does, its structure is likely to be optimized by hundreds of millions of years of evolution, which may suggest novel dropout strategies in large-scale ANNs. We propose that the brain serotonergic fibers meet some of the expected criteria because of their ubiquitous presence, stochastic structure, and ability to grow throughout the individual’s lifespan. Since the trajectories of serotonergic fibers can be modeled as paths of anomalous diffusion processes, in this proof-of-concept study we investigated a dropout algorithm based on the superdiffusive fractional Brownian motion (FBM). This research contributes to biologically-inspired regularization in ANNs.

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A stochastic approach to serotonergic fibers in mental disorders

Cited by 16 publications

References 62 publications

Serotonergic Axons as Fractional Brownian Motion Paths: Insights Into the Self-Organization of Regional Densities

Serotonergic Axons as Fractional Brownian Motion Paths: Insights Into the Self-Organization of Regional Densities

Tempered fractional Brownian motion on finite intervals

Brain Serotonergic Fibers Suggest Anomalous Diffusion-Based Dropout in Artificial Neural Networks

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