This study investigated the morphology and quantitative distribution of neurons containing NADPH diaphorase activity in the ventral lateral geniculate nucleus of the rat. The pattern of diaphorase staining revealed a strongly reactive lateral subdivision and a weakly staining medial subdivision. A characteristic feature of the diaphorase staining in the lateral part was its "stripe-like" appearance. These "diaphorase stripes" resulted from regions of strong somatic and neuropil diaphorase activity lying between unstained fibre bundles coursing dorsoventrally through the nucleus. Two distinct populations of diaphorase reactive cell types were present--class A and class B neurons. The ratio of class A to class B diaphorase neurons was approximately 14:1 (A:B). Diaphorase reactive neurons made up 73% of the total neuron population in the lateral subdivision, and 31% in the medial subdivision. A third population of cells was found exclusively in the optic tract--class C neurons. Quantitative analyses in the coronal and sagittal planes indicated that the principal processes of both class A and class B neurons were oriented preferentially--either parallel with, or perpendicular to the outlying optic tract. Diaphorase enzyme histochemistry in combination with GABA immunocytochemistry demonstrated the co-localization of GABA immunoreactivity in the majority of class B neurons, whereas class A and class C neurons were GABA immunonegative. Furthermore a large population of GABA-immunoreactive neurons was present that were not stained for diaphorase activity. From this and previous studies, it can be concluded that a high proportion of the diaphorase reaction class A neurons are geniculotectal projection cells, while diaphorase reaction class B neurons represent a numerically small subpopulation of "local-circuit" inhibitory neurons. Since diaphorase activity co-localizes with nitric oxide synthase, the results indicate the likely involvement of nitric oxide in the neuronal operations of both subpopulations of geniculotectal projection neurons and "local-circuit" GABAergic neurons in the rat's ventral lateral geniculate nucleus.
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http://dx.doi.org/10.1016/0306-4522(94)90254-2 | DOI Listing |
Biol Psychiatry
January 2025
Translational Neuroscience Program, Department of Psychiatry, School of Medicine, University of Pittsburgh; Department of Neuroscience, Dietrich School of Arts and Sciences, University of Pittsburgh; Center for the Neural Basis of Cognition, Carnegie Mellon University. Electronic address:
Background: Certain cognitive processes require inhibition provided by the somatostatin (SST) class of gamma-aminobutyric acid (GABA) neurons in the dorsolateral prefrontal cortex (DLPFC). This inhibition onto pyramidal neuron dendrites depends on both SST and GABA signaling. Although SST mRNA levels are lower in the DLPFC in schizophrenia, it is not known if SST neurons exhibit alterations in the capacity to synthesize GABA, principally via the 67-kilodalton isoform of glutamic acid decarboxylase (GAD67).
View Article and Find Full Text PDFFront Neurosci
January 2025
School of Data Science, Lingnan University, Hong Kong SAR, China.
Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.
View Article and Find Full Text PDFNat Commun
January 2025
Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece.
Artificial neural networks (ANNs) are at the core of most Deep Learning (DL) algorithms that successfully tackle complex problems like image recognition, autonomous driving, and natural language processing. However, unlike biological brains who tackle similar problems in a very efficient manner, DL algorithms require a large number of trainable parameters, making them energy-intensive and prone to overfitting. Here, we show that a new ANN architecture that incorporates the structured connectivity and restricted sampling properties of biological dendrites counteracts these limitations.
View Article and Find Full Text PDFNeuropharmacology
January 2025
Department of Anatomy & Neuroscience, School of Medicine, University College Cork (UCC), Cork, Ireland; APC Microbiome Ireland, UCC, Cork, Ireland. Electronic address:
Degeneration of midbrain nigrostriatal dopaminergic neurons is a pathological hallmark of Parkinson's disease (PD). Peripheral delivery of a compound(s) to arrest or slow this dopaminergic degeneration is a key therapeutic goal. Pan-inhibitors of histone deacetylase (HDAC) enzymes, key epigenetic regulators, have shown therapeutic promise in PD models.
View Article and Find Full Text PDFNanotechnology
January 2025
Department of Electrical and Computer Engineering, Virginia Commonwealth University, 601 W. Main Street, Richmond, VA 23284, USA, Richmond, 23284, UNITED STATES.
Stochastic neurons are extremely efficient hardware for solving a large class of problems and usually come in two varieties - "binary" where the neuronal state varies randomly between two values of ±1 and "analog" where the neuronal state can randomly assume any value between -1 and +1. Both have their uses in neuromorphic computing and both can be implemented with low- or zero-energy-barrier nanomagnets whose random magnetization orientations in the presence of thermal noise encode the binary or analog state variables. In between these two classes is n-ary stochastic neurons, mainly ternary stochastic neurons (TSN) whose state randomly assumes one of three values (-1, 0, +1), which have proved to be efficient in pattern classification tasks such as recognizing handwritten digits from the MNIST data set or patterns from the CIFAR-10 data set.
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