Neuron classification based on temporal firing patterns by the dynamical analysis with changing time resolution (DCT) method

Oshio, Ken-ichi; Yamada, Satoshi; Nakashima, Michio

doi:10.1007/s00422-003-0396-8

Cited by 9 publications

(2 citation statements)

References 16 publications

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“…Although positively correlated results have not been obtained, reconsidering the basic concept of CSF physiology, such as the fact that CSF does not flow and that the shunt itself does not have a constant flow of CSF, may improve medical treatment strategies. Distribution of the waste material in the brain may also be visualized non-invasively using MRI in future ( 50 ).…”

Section: Acceleration Of Removal Of Waste Materials From the Brain By Shaking Headmentioning

confidence: 99%

Cerebrospinal fluid dynamics

2021

Croat Med J

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The classical cerebrospinal fluid (CSF) circulation theory has been accepted as an established theory of CSF physiology. It describes bulk CSF flow from production site to absorption site. However, much controversy remains regarding the basic CSF physiology and the mechanisms behind the development of hydrocephalus. In the recent observations made using advanced magnetic resonance imaging (MRI) technique, namely, the time spatial inversion pulse (Time-SLIP) method, CSF was used as internal CSF tracer to trace true CSF movement. Observation of the CSF dynamics using this method reveals aspects of CSF dynamics that are different from those of classical CSF circulation theory. Cerebrospinal fluid shows pulsation but does not show bulk flow from production site to absorption site, a theory that was built upon externally injected tracer studies. Observation of the exogeneous tracer studies were true but misinterpreted. Causes of misinterpretations are the differences between results obtained using the true CSF tracer and exogenous tracers. A better understanding of the real CSF physiology can be significant for the advancement of medical sciences in the future. Revisiting CSF flow physiology is a necessary step toward this goal.

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Section: Acceleration Of Removal Of Waste Materials From the Brain By Shaking Headmentioning

confidence: 99%

Cerebrospinal fluid dynamics

2021

Croat Med J

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“…The electrical characteristic of neurons contains different action potential firing patterns. The potential spike pattern of the neuronal cells can be obtained in response to their puncture by a microelectrode; however, it is more complicated to distinguish the types of neurons by the spike firing characteristic of neuronal cells [6]. Dendritic and axonal trees stemming from the soma develop an extremely complex branching pattern that occupies and fills three-dimensional (3D) space [7].…”

Section: Introductionmentioning

confidence: 99%

A Neuronal Morphology Classification Approach Based on Locally Cumulative Connected Deep Neural Networks

Lin

Zheng

2019

Applied Sciences

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Neurons are the basic building and computational units of the nervous system, and have complex and diverse spatial geometric structures. By solving the neuronal classification problem, we can further understand the characteristics of neurons and the process of information transmission. This paper presents a neuronal morphology classification approach based on locally cumulative connected deep neural networks, where 43 geometric features were extracted from two different neuron datasets and applied to classify types of neurons. Then, the effects of different parameters of deep learning networks on the performance of neuron classification were analyzed including mini-batch size, number of intermediate layers, and number of building blocks. The accuracy of the approach was also compared with that of the other mainstream machine learning approaches. The experimental results showed that the proposed approach is effective for solving complex neuronal morphology classification problems.

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Pooled spike trains of correlated presynaptic inputs as realizations of cluster point processes

et al. 2005

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The pooled spike trains of correlated presynaptic terminals acting synchronously upon a single neuron are realizations of cluster point processes: the notions of spikes synchronizing in bursts and of points bunching in clusters are conceptually identical. The primary processes constituent specifies the timing of the cluster series; subsidiary processes and poolings specify burst structure and tightness. This representation and the Poisson process representation of independent terminals complete the formal approach to pooled trains. The notion's usefulness was illustrated by expressing physiological questions in terms of those constituents, each possessing a clear biological embodiment; constituents provided the control variables in simulations using leaky integrate-and-fire postsynaptic neurons excited by multiple weak terminals. Regular or irregular primary processes and bursts series determined low or high postsynaptic dispersions. When convergent set synchrony increased, its postsynaptic consequences approached those of single powerful synapses; concomitantly, output spike trains approached periodic, quasiperiodic, or aperiodic behaviors. The sequence in which terminals fired within bursts affected the predictee and predictor roles of presynaptic and postsynaptic spikes; when inhibition was added, EPSP and IPSP delays and order were influential (summation was noncommutative). Outputs to different correlations were heterogeneous; heterogeneity was accentuated by conditioning by variables such as DC biases.

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Neuron classification based on temporal firing patterns by the dynamical analysis with changing time resolution (DCT) method

Cited by 9 publications

References 16 publications

Cerebrospinal fluid dynamics

Cerebrospinal fluid dynamics

A Neuronal Morphology Classification Approach Based on Locally Cumulative Connected Deep Neural Networks

Pooled spike trains of correlated presynaptic inputs as realizations of cluster point processes

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