Mathematical modeling of the neuron morphology using two dimensional images

Abnormal dendritic morphology in the cerebellum of cyclooxygenase-2- knockin mice

Prostaglandin E2 (PGE2) is a bioactive signalling molecule metabolized from the phospholipid membranes by the enzymatic activity of cycloxygenase-2 (COX-2). In the developing brain, COX-2 constitutively regulates the production of PGE2, which is important in neuronal development. However, abnormal COX-2/PGE2 signalling has been linked to neurodevelopmental disorders including autism spectrum disorders (ASDs). We have previously demonstrated that COX-2^- -KI mice show autism-related behaviours including social deficits, repetitive behaviours and anxious behaviours. COX-2-deficient mice also have deficits in pathways involved in synaptic transmission and dendritic spine formation. In this study, we use a Golgi-COX staining method to examine sex-dependent differences in dendritic and dendritic spine morphology in neurons of COX-2^- -KI mice cerebellum compared with wild-type (WT) matched controls at postnatal day 25 (P25). We show that COX-2^- -KI mice have increased dendritic arborization closer to the cell soma and increased dendritic looping. We also observed a sex-dependent effect of the COX-2^- -KI on dendritic thickness, dendritic spine density, dendritic spine morphology, and the expression of β-actin and the actin-binding protein spinophilin. Our findings show that changes in COX-2/PGE2 signalling lead to impaired morphology of dendrites and dendritic spines in a sex-dependant manner and may contribute the pathology of the cerebellum seen in individuals with ASD. This study provides further evidence that the COX-2^- -KI mouse model can be used to study a subset of ASD pathologies.

Dissecting Sholl Analysis into Its Functional Components

Sholl analysis has been an important technique in dendritic anatomy for more than 60 years. The Sholl intersection profile is obtained by counting the number of dendritic branches at a given distance from the soma and is a key measure of dendritic complexity; it has applications from evaluating the changes in structure induced by pathologies to estimating the expected number of anatomical synaptic contacts. We find that the Sholl intersection profiles of most neurons can be reproduced from three basic, functional measures: the domain spanned by the dendritic arbor, the total length of the dendrite, and the angular distribution of how far dendritic segments deviate from a direct path to the soma (i.e., the root angle distribution). The first two measures are determined by axon location and hence microcircuit structure; the third arises from optimal wiring and represents a branching statistic estimating the need for conduction speed in a neuron.

FPGA Realization of Hodgkin-Huxley Neuronal Model

One of the appealing cases of the neuromorphic research area is the implementation of biological neural networks. The current study offers Multiplierless Hodgkin-Huxley Model (MHHM). This modified model may reproduce various spiking behaviors, like the biological HH neurons, with high accuracy. The presented modified model, in comparison to the original HH model, due to its exact similarity to the original model, has more top performances in the case of FPGA saving and more achievable frequency (speed-up). In this approach, the proposed model has a 69 % saving in FPGA resources and also the maximum frequency of 85 MHz that is more than other similar works. In this modification, all spiking behaviors of the original model have been generated with low error calculations. To validate the MHHM neuron, this proposed model has been implemented on digital hardware FPGA. This approach demonstrates that the original HH model and the proposed model have high similarity in terms of higher performance and digital hardware cost reduction.

Digital Multiplierless Realization of Coupled Wilson Neuron Model

The human brain is composed of 10¹¹ neurons with a switching speed of about 1 ms. Studying spiking neural networks, including the modeling, simulation, and implementation of the biological neuron models, helps us to learn about the brain and the related diseases, or to design more efficient bio-mimic processors and smarter robots. Such applications have made this part of neuromorphic research works very popular. In this paper, the Wilson neuron model has been implemented as an approximation of the Hodgkin-Huxley biological model that is adjusted for the efficient digital realization on the platforms. Results show that the proposed model can adequately reproduce neuron dynamical behaviors. The hardware implementation on the field-programmable gate array (FPGA) shows that our modifications on the Wilson original model imitate the biological behavior of neurons, besides using feasibility, targeting a low cost and high efficiency. The modifications raised a 15% speed-up compared with the original model. The mean normalized root-mean-square error, root-mean-square error, and the mean absolute error parameters are 6.43, 0.44, and 0.31, respectively.

Aspergillus fumigatus branching complexity in vitro: 2D images and dynamic modeling

BACKGROUND

Aspergillus fumigatus causes serious infections in humans, and its virulence correlates with hyphal growth, branching and formation of the filamentous mycelium. The filamentous mycelium is a complex structure inconvenient for quantity analysis. In this study, we monitored the branching of A. fumigatus filamentous mycelium in vitro at different points in time in order to assess the complexity degree and develop a dynamic model for the branching complexity.

METHOD

We used fractal analysis of microscopic images (FAMI) to measure the fractal dimensions (D) of the branching complexity within 24 h of incubation.

RESULTS

By photographing the filamentous mycelium dynamically and processing the images, the D variation curve of A. fumigatus complexity degree was obtained. We acquired the D variation curve which contained initial exponential period and stationary period of A. fumigatus branching. Further, the obtained data of D was modeled via the logistic model (LM) to develop a dynamic model of A. fumigatus branching for the prediction of the specific growth rate of branching value (0.23 h^-1).

CONCLUSIONS

Developed FAMI and LM models present a simple and non-destructive method of predicting the evolution of branching complexity of A. fumigatus. These models are useful as laboratory measurements for the prediction of hyphal and mycelium development, especially relevant to the pathogenesis study of aspergillosis, as well as pathogenesis of other diseases caused by moulds.

The Cellular and Synaptic Architecture of the Mechanosensory Dorsal Horn

The deep dorsal horn is a poorly characterized spinal cord region implicated in processing low-threshold mechanoreceptor (LTMR) information. We report an array of mouse genetic tools for defining neuronal components and functions of the dorsal horn LTMR-recipient zone (LTMR-RZ), a role for LTMR-RZ processing in tactile perception, and the basic logic of LTMR-RZ organization. We found an unexpectedly high degree of neuronal diversity in the LTMR-RZ: seven excitatory and four inhibitory subtypes of interneurons exhibiting unique morphological, physiological, and synaptic properties. Remarkably, LTMRs form synapses on between four and 11 LTMR-RZ interneuron subtypes, while each LTMR-RZ interneuron subtype samples inputs from at least one to three LTMR classes, as well as spinal cord interneurons and corticospinal neurons. Thus, the LTMR-RZ is a somatosensory processing region endowed with a neuronal complexity that rivals the retina and functions to pattern the activity of ascending touch pathways that underlie tactile perception.

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Convergent LTMR and cortical inputs define the mechanosensory dorsal horn

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A dorsal horn molecular-genetic toolbox defines 11 interneuron subtypes

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Dorsal horn interneurons receive specific patterns of cortical and LTMR inputs

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Dorsal horn interneurons modulate output pathways and tactile perception