Reelin Mediates Hippocampal Cajal-Retzius Cell Positioning and Infrapyramidal Blade Morphogenesis

We have previously described hypomorphic reelin (Reln) mutant mice, RelnCTRdel, in which the morphology of the dentate gyrus is distinct from that seen in reeler mice. In the RelnCTRdel mutant, the infrapyramidal blade of the dentate gyrus fails to extend, while the suprapyramidal blade forms with a relatively compact granule neuron layer. Underlying this defect, we now report several developmental anomalies in the RelnCTRdel dentate gyrus. Most strikingly, the distribution of Cajal-Retzius cells was aberrant; Cajal-Retzius neurons were increased in the suprapyramidal blade, but were greatly reduced along the subpial surface of the prospective infrapyramidal blade. We also observed multiple abnormalities of the fimbriodentate junction. Firstly, progenitor cells were distributed abnormally; the “neurogenic cluster” at the fimbriodentate junction was absent, lacking the normal accumulation of Tbr2-positive intermediate progenitors. However, the number of dividing cells in the dentate gyrus was not generally decreased. Secondly, a defect of secondary glial scaffold formation, limited to the infrapyramidal blade, was observed. The densely radiating glial fibers characteristic of the normal fimbriodentate junction were absent in mutants. These fibers might be required for migration of progenitors, which may account for the failure of neurogenic cluster formation. These findings suggest the importance of the secondary scaffold and neurogenic cluster of the fimbriodentate junction in morphogenesis of the mammalian dentate gyrus. Our study provides direct genetic evidence showing that normal RELN function is required for Cajal-Retzius cell positioning in the dentate gyrus, and for formation of the fimbriodentate junction to promote infrapyramidal blade extension.

Влияние пренатального и постнатального уменьшения численности пометов на некоторые показатели развития головного мозга крыс в молочном периоде онтогенеза

The authors studied the brain of 14 day old rats born in diminished by number litters (n=7,7±0,7), received from mating females having been exposed to the removal of right uterine horn (1,5 month before mating) and a day after delivery – with litter diminishing up to 5-6. The control group included rats from the litters with an average amount 10,8±1,1, which were off spring from intact males and females. At the age of 14-days experimental animals had a higher body mass, and adrenals mass (P<0,05), and testes (P>0,05) than the control group. Their brain and cerebral hemispheres had increased mass. The thickness of cortex and parietal lobe proper layer I did not show any reliable diff erences between the groups. Number density of neurons in layer II and V in the experimental rats was decreased, the size of cytoplasm of hippo-campus neurons and nucleoli in the neuron layer II PLP were reliably increased compared to the control group. The obtained data confi rm than diminished number of animals in a litter in the prenatal and early postnatal periods odontogenesis is refl ected on the parameters of brain development. At the same time, the diff erences with the control are of the same type and are more pronounced than when the number of animals in a litter is decreased straight after birth.

Reelin Mediates Hippocampal Cajal-Retzius Cell Positioning and Infrapyramidal Blade Morphogenesis

We have previously described hypomorphic reelin (Reln) mutant mice, RelnCTRdel, in which the morphology of the dentate gyrus is distinct from that seen in reeler mice. In the RelnCTRdel mutant the infrapyramidal blade of the dentate gyrus fails to extend, while the suprapyramidal blade forms with a relatively compact granule neuron layer. The distribution of Cajal-Retzius cells in the dentate gyrus was aberrant; Cajal-Retzius neurons were increased in the suprapyramidal blade, but were greatly reduced along the subpial surface of the prospective infrapyramidal blade. We also observed multiple abnormalities of the fimbriodentate junction. Firstly, progenitor cells were distributed abnormally; the neurogenic cluster at the fimbriodentate junction was absent, lacking the normal accumulation of Tbr2-positive intermediate progenitors. However, the number of dividing cells in the dentate gyrus was not generally decreased. Secondly, a defect of secondary glial scaffold formation, limited to the infrapyramidal blade, was observed. The densely radiating glial fibers characteristic of the normal fimbriodentate junction were absent in mutants. These fibers might be required for migration of progenitors, which may account for the failure of neurogenic cluster formation. These findings suggest the importance of the secondary scaffold and neurogenic cluster of the fimbriodentate junction in morphogenesis of the mammalian dentate gyrus. Our study provides direct genetic evidence showing that normal RELN function is required for Cajal-Retzius cell positioning in the dentate gyrus, and for formation of the fimbriodentate junction to promote infrapyramidal blade extension.

Elemental characterisation of the pyramidal neuron layer within the rat and mouse hippocampus

X-ray fluorescence microscopy reveals unique elemental signatures within sub-populations of hippocampal pyramidal neurons.

Penerapan Algoritma Optimasi Chaos pada Jaringan Ridge Polynomial untuk Prediksi Jumlah Pengangguran

AbstrakRidge polynomial neural network (RPNN) awalnya diusulkan oleh Shin dan Ghosh, dibangun dari jumlah peningkatan order pi-sigma neuron (PSN). RPNN mempertahankan pembelajaran cepat, pemetaan yang kuat dari layer tunggal higher order neural network (HONN) dan menghindari banyaknya bobot karena meningkatnya sejumlah input. Algoritma optimasi chaos digunakan dengan memanfaatkan persamaan logistik yang sensitif terhadap kondisi awal, sehingga pergerakan chaos dapat berubah di setiap keadaan dalam skala tertentu menurut keteraturan, ergodik dan mempertahankan keragaman solusi.Algoritma Optimasi Chaos diterapkan pada RPNN dan digunakan untuk prediksi jumlah pengangguran di Kalimantan Barat. Proses pelatihan jaringan menggunakan ridge polynomial neural network, sedangkan pencarian nilai awal bobot dan bias jaringan menggunakan algoritma optimasi chaos. Struktur yang digunakan terdiri dari 6 neuron layer input dan 1 neuron layer output. Data diperoleh dari Badan Pusat Statistik.Hasil dari penelitian ini menunjukkan bahwa algoritma yang diusulkan dapat digunakan untuk prediksi. Kata kunci—prediksi jumlah pengangguran, jaringan syaraf tiruan, algoritma optimasi chaos, ridge polynomial neural network Abstract Ridge polynomial neural network was initially proposed by Shin and Ghosh, made of total increased pi-sigma neural (PSN) orders. Ridge polynomial neural network maintains quick learning, strong mapping of single layer of higher order neural network (HONN) and avoids many weights because total increased inputs. Chaos optimization algorithm is used by utilizing sensitive logistic equation to initial condition, so that chaos movement can change in each condition in specific scale according to orderliness, ergodic, and maintaining solution variety. Chaos optimization algorithm is applied to ridge polynomial neural network and used to predict total unemployed persons in West Kalimantan. Network training process used ridge polynomial neural network; while, initial values and weights and bias of network were found using Chaos optimization algorithm. Structure used consisted of 6 input layer neurons and one output layer neuron. Data were obtained from Central Statistic Agency. The results of research indicated that algorithm proposed could be used to predict Keywords— predict the number of unemployed, neural networks, chaos optimization algorithm, ridge polynomial neural network

Implementing a Neural Network System to Solve the Inverse Kinematics of a Biologically Inspired Robotic Cat Leg

A neural network capable of solving the inverse kinematics of a four degree of freedom biologically inspired robotic cat leg (qualified as a serial linkage system) within its effective 3-D workspace is presented in this paper. The workspace consists of layers of similar but highly nonlinear cells whose vertices are associated with known kinematic variables provided by the robotic leg. The proposed neural network uses geometric properties coupled with the desired end effecter location as the neural network inputs to locate the cell for which encapsulates the associated location. Another neuron layer utilizing activation functions trained with the Perceptron Fixed learning rule is applied to interpolate within the identified cell. The similarity associated between all of the cells allows the trained neural network to effectively be applied in solving the inverse kinematics of the entire workspace.