Dependence of Sensitive Responses of Chaotic Wandering States on Configuration of Inhibited In-Coming Synaptic Connections

2014 ◽  
Vol 1 ◽  
pp. 644-647 ◽  
Author(s):  
Toshiyuki Hamada ◽  
Jousuke Kuroiwa ◽  
Hisakazu Ogura ◽  
Tomohiro Odaka ◽  
Izumi Suwa ◽  
...  
Keyword(s):  
Synaptic Connections

Structure and function of neural networks in the retina

1988 ◽  
Vol 46 ◽  
pp. 26-27
Author(s):  
Peter Sterling
Keyword(s):  
Ganglion Cells ◽  
Three Dimensional ◽  
Structure And Function ◽  
Synaptic Connections ◽  
Visual Axis ◽  
One Dimensional ◽  
Cat Retina ◽  
Ultrathin Sections ◽  
And Function ◽  
Insight Into

The synaptic connections in cat retina that link photoreceptors to ganglion cells have been analyzed quantitatively. Our approach has been to prepare serial, ultrathin sections and photograph en montage at low magnification (˜2000X) in the electron microscope. Six series, 100-300 sections long, have been prepared over the last decade. They derive from different cats but always from the same region of retina, about one degree from the center of the visual axis. The material has been analyzed by reconstructing adjacent neurons in each array and then identifying systematically the synaptic connections between arrays. Most reconstructions were done manually by tracing the outlines of processes in successive sections onto acetate sheets aligned on a cartoonist's jig. The tracings were then digitized, stacked by computer, and printed with the hidden lines removed. The results have provided rather than the usual one-dimensional account of pathways, a three-dimensional account of circuits. From this has emerged insight into the functional architecture.

Inflammational Animal Models for Schizophrenia

2015 ◽  
Vol 223 (3) ◽  
pp. 157-164 ◽  
Author(s):  
Georg Juckel
Keyword(s):  
Animal Model ◽  
Animal Models ◽  
Immune Activation ◽  
Microglial Activation ◽  
Treatment Options ◽  
Early Adulthood ◽  
Brain Regions ◽  
Synaptic Connections ◽  
Preventive Interventions ◽  
Immunological System

Abstract. Inflammational-immunological processes within the pathophysiology of schizophrenia seem to play an important role. Early signals of neurobiological changes in the embryonal phase of brain in later patients with schizophrenia might lead to activation of the immunological system, for example, of cytokines and microglial cells. Microglia then induces – via the neurotoxic activities of these cells as an overreaction – a rarification of synaptic connections in frontal and temporal brain regions, that is, reduction of the neuropil. Promising inflammational animal models for schizophrenia with high validity can be used today to mimic behavioral as well as neurobiological findings in patients, for example, the well-known neurochemical alterations of dopaminergic, glutamatergic, serotonergic, and other neurotransmitter systems. Also the microglial activation can be modeled well within one of this models, that is, the inflammational PolyI:C animal model of schizophrenia, showing a time peak in late adolescence/early adulthood. The exact mechanism, by which activated microglia cells then triggers further neurodegeneration, must now be investigated in broader detail. Thus, these animal models can be used to understand the pathophysiology of schizophrenia better especially concerning the interaction of immune activation, inflammation, and neurodegeneration. This could also lead to the development of anti-inflammational treatment options and of preventive interventions.

Synaptic Connections between a Transplanted Insect Ganglion and Muscles of the Host

Science ◽  
1967 ◽  
Vol 156 (3782) ◽  
pp. 1638-1640 ◽  
Author(s):  
J. W. Jacklet ◽  
M. J. Cohen
Keyword(s):  
Synaptic Connections

scholarly journals Quantification of Dendritic Spines Remodeling under Physiological Stimuli and in Pathological Conditions

2021 ◽  
Vol 22 (8) ◽  
pp. 4053
Author(s):  
Ewa Bączyńska ◽  
Katarzyna Karolina Pels ◽  
Subhadip Basu ◽  
Jakub Włodarczyk ◽  
Błażej Ruszczycki
Keyword(s):  
Dendritic Spines ◽  
Quantitative Data ◽  
Statistical Significance ◽  
Brain Diseases ◽  
Synaptic Connections ◽  
Brain Pathology ◽  
Biological Models ◽  
Structural Geometry ◽  
Pathological Conditions ◽  
Experimental Approaches

Numerous brain diseases are associated with abnormalities in morphology and density of dendritic spines, small membranous protrusions whose structural geometry correlates with the strength of synaptic connections. Thus, the quantitative analysis of dendritic spines remodeling in microscopic images is one of the key elements towards understanding mechanisms of structural neuronal plasticity and bases of brain pathology. In the following article, we review experimental approaches designed to assess quantitative features of dendritic spines under physiological stimuli and in pathological conditions. We compare various methodological pipelines of biological models, sample preparation, data analysis, image acquisition, sample size, and statistical analysis. The methodology and results of relevant experiments are systematically summarized in a tabular form. In particular, we focus on quantitative data regarding the number of animals, cells, dendritic spines, types of studied parameters, size of observed changes, and their statistical significance.

scholarly journals Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3276
Author(s):  
Szymon Szczęsny ◽  
Damian Huderek ◽  
Łukasz Przyborowski
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Waveform Analysis ◽  
Spiking Neural Network ◽  
Synaptic Connections ◽  
Practical Application ◽  
Accuracy Parameter ◽  
The Stability ◽  
Time Waveform ◽  
Network Mapping

The paper describes the architecture of a Spiking Neural Network (SNN) for time waveform analyses using edge computing. The network model was based on the principles of preprocessing signals in the diencephalon and using tonic spiking and inhibition-induced spiking models typical for the thalamus area. The research focused on a significant reduction of the complexity of the SNN algorithm by eliminating most synaptic connections and ensuring zero dispersion of weight values concerning connections between neuron layers. The paper describes a network mapping and learning algorithm, in which the number of variables in the learning process is linearly dependent on the size of the patterns. The works included testing the stability of the accuracy parameter for various network sizes. The described approach used the ability of spiking neurons to process currents of less than 100 pA, typical of amperometric techniques. An example of a practical application is an analysis of vesicle fusion signals using an amperometric system based on Carbon NanoTube (CNT) sensors. The paper concludes with a discussion of the costs of implementing the network as a semiconductor structure.

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