Cognitive Enhancement Mediated Through Postsynaptic Actions of Norepinephrine on Ongoing Cortical Activity

2005 ◽  
Vol 17 (8) ◽  
pp. 1739-1775 ◽  
Author(s):  
Osamu Hoshino
Keyword(s):  
Neural Network ◽  
Cognitive Performance ◽  
Neuronal Activity ◽  
Dynamic Property ◽  
Cognitive Enhancement ◽  
Cortical Activity ◽  
Sensory Stimuli ◽  
The Neural Network ◽  
Higher Doses ◽  
The Brain

We propose two distinct types of norepinephrine (NE)-neuromodulatory systems: an enhanced-excitatory and enhanced-inhibitory (E-E/E-I) system and a depressed-excitatory and enhanced-inhibitory (D-E/E-I) system. In both systems, inhibitory synaptic efficacies are enhanced, but excitatory ones are modified in a contradictory manner: the E-E/E-I system enhances excitatory synaptic efficacies, whereas the D-E/E-I system depresses them. The E-E/E-I and D-E/E-I systems altered the dynamic property of ongoing (background) neuronal activity and greatly influenced the cognitive performance (S/N ratio) of a cortical neural network. The E-E/E-I system effectively enhanced S/N ratio for weaker stimuli with lower doses of NE, whereas the D-E/E-I system enhanced stronger stimuli with higher doses of NE. The neural network effectively responded to weaker stimuli if brief γ-bursts were involved in ongoing neuronal activity that is controlled under the E-E/E-I neuromodulatory system. If the E-E/E-I and the D-E/E-I systems interact within the neural network, depressed neurons whose activity is depressed by NE application have bimodal property. That is, S/N ratio can be enhanced not only for stronger stimuli as its original property but also for weaker stimuli, for which coincidental neuronal firings among enhanced neurons whose activity is enhanced by NE application are essential. We suggest that the recruitment of the depressed neurons for the detection of weaker (subthreshold) stimuli might be advantageous for the brain to cope with a variety of sensory stimuli.

scholarly journals Automatic substantia nigra segmentation in neuromelanin-sensitive MRI by deep neural network in patients with prodromal and manifest synucleinopathy

2019 ◽  
pp. S453-S458
Author(s):  
R. Krupička ◽  
S. Mareček ◽  
C. Malá ◽  
M. Lang ◽  
O. Klempíř ◽  
...  
Keyword(s):  
Neural Network ◽  
Substantia Nigra ◽  
Signal Intensity ◽  
Substantia Nigra Pars Compacta ◽  
The Neural Network ◽  
Intensity Information ◽  
Ideal Tool ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Neuromelanin (NM) is a black pigment located in the brain in substantia nigra pars compacta (SN) and locus coeruleus. Its loss is directly connected to the loss of nerve cells in this part of the brain, which plays a role in Parkinson’s Disease. Magnetic resonance imaging (MRI) is an ideal tool to monitor the amount of NM in the brain in vivo. The aim of the study was the development of tools and methodology for the quantification of NM in a special neuromelanin-sensitive MRI images. The first approach was done by creating regions of interest, corresponding to the anatomical position of SN based on an anatomical atlas and determining signal intensity threshold. By linking the anatomical and signal intensity information, we were able to segment the SN. As a second approach, the neural network U-Net was used for the segmentation of SN. Subsequently, the volume characterizing the amount of NM in the SN region was calculated. To verify the method and the assumptions, data available from various patient groups were correlated. The main benefit of this approach is the observer-independency of quantification and facilitation of the image processing process and subsequent quantification compared to the manual approach. It is ideal for automatic processing many image sets in one batch.

scholarly journals It may be time to perfect the neuron of artificial neural network

2020 ◽  
Author(s):  
Gang Liu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Input Data ◽  
Basic Unit ◽  
Future Research ◽  
Brain Neuron ◽  
Artificial Neuron ◽  
The Neural Network ◽  
Artificial Neural ◽  
The Brain

In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence. The basic unit of an ANN is to mimic neurons in the brain. Neuron in ANNs is expressed as f(wx+b) or f(wx).This structure does not consider the information processing capabilities of dendrites. However, recently, studies shown that dendrites participate in pre-calculation in the brain. Concretely, biological dendrites play a role in the pre-processing to the interaction information of input data. Therefore, it's time to perfect the neuron of the neural network. This paper, add dendrite processing section, presents a novel artificial neuron, according to our previous studies (CR-PNN or Gang transform). The dendrite processing section can be expressed as WA.X. Because I perfected the basic unit of ANNs-neuron, there are so many networks to try, this article gives the basic architecture for reference in future research.

scholarly journals A Comprehensive Analysis of Metabolomics and Transcriptomics Reveals Novel Biomarkers and Mechanistic Insights on Lorlatinib Crosses the Blood-Brain Barrier

2021 ◽  
Vol 12 ◽  
Author(s):  
Wei Chen ◽  
Chunyu Li ◽  
Yafei Shi ◽  
Yujun Zhang ◽  
Dujia Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Interaction Network ◽  
Gene Interaction ◽  
Accuracy Rate ◽  
Gene Interaction Network ◽  
The Neural Network ◽  
Novel Biomarkers ◽  
And Control ◽  
The Brain

Of late, lorlatinib has played an increasingly pivotal role in the treatment of brain metastasis from non-small cell lung cancer. However, its pharmacokinetics in the brain and the mechanism of entry are still controversial. The purpose of this study was to explore the mechanisms of brain penetration by lorlatinib and identify potential biomarkers for the prediction of lorlatinib concentration in the brain. Detection of lorlatinib in lorlatinib-administered mice and control mice was performed using liquid chromatography and mass spectrometry. Metabolomics and transcriptomics were combined to investigate the pathway and relationships between metabolites and genes. Multilayer perceptron was applied to construct an artificial neural network model for prediction of the distribution of lorlatinib in the brain. Nine biomarkers related to lorlatinib concentration in the brain were identified. A metabolite-reaction-enzyme-gene interaction network was built to reveal the mechanism of lorlatinib. A multilayer perceptron model based on the identified biomarkers provides a prediction accuracy rate of greater than 85%. The identified biomarkers and the neural network constructed with these metabolites will be valuable for predicting the concentration of drugs in the brain. The model provides a lorlatinib to treat tumor brain metastases in the clinic.

scholarly journals Structural constraints on learning in the neural network

2015 ◽  
Vol 114 (5) ◽  
pp. 2555-2557
Author(s):  
Clarisa A. Martinez ◽  
Chunji Wang
Keyword(s):  
Neural Network ◽  
Structural Characteristics ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Structural Constraints ◽  
The Neural Network ◽  
Behavioral Evidence ◽  
The Brain ◽  
Future Learning ◽  
Insight Into

Recent research suggests the brain can learn almost any brain-computer interface (BCI) configuration; however, contrasting behavioral evidence from structural learning theory argues that previous experience facilitates, or impedes, future learning. A study by Sadtler and colleagues ( Nature 512: 423–426, 2014) used BCI to demonstrate that neural network structural characteristics constrain learning, a finding that might also provide insight into how the brain responds to and recovers after injury.

scholarly journals Suppression of Phase Synchronization in Scale-Free Neural Networks Using External Pulsed Current Protocols

2019 ◽  
Vol 24 (2) ◽  
pp. 46
Author(s):  
B. R. R. Boaretto ◽  
R. C. Budzinski ◽  
T. L. Prado ◽  
S. R. Lopes
Keyword(s):  
Neural Network ◽  
Phase Synchronization ◽  
Numerical Approach ◽  
Research Field ◽  
Delayed Feedback Control ◽  
Multidisciplinary Research ◽  
Scale Free ◽  
The Neural Network ◽  
The Brain ◽  
Deep Brain

The synchronization of neurons is fundamental for the functioning of the brain since its lack or excess may be related to neurological disorders, such as autism, Parkinson’s and neuropathies such as epilepsy. In this way, the study of synchronization, as well as its suppression in coupled neurons systems, consists of an important multidisciplinary research field where there are still questions to be answered. Here, through mathematical modeling and numerical approach, we simulated a neural network composed of 5000 bursting neurons in a scale-free connection scheme where non-trivial synchronization phenomenon is observed. We proposed two different protocols to the suppression of phase synchronization, which is related to deep brain stimulation and delayed feedback control. Through an optimization process, it is possible to suppression the abnormal synchronization in the neural network.

Connectivity and Condensation in Dreaming

1992 ◽  
Vol 40 (4) ◽  
pp. 1139-1159 ◽  
Author(s):  
Stanley R. Palombo
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Selection Process ◽  
Psychoanalytic Theory ◽  
Distributed Representation ◽  
Mental Functioning ◽  
Neural Network Architecture ◽  
New Developments ◽  
The Neural Network ◽  
The Brain

Converging developments in the cognitive- and neurosciences have brought Freud's hope of a bridge between psychoanalysis and psychophysiology nearer to hand. This paper concerns the relation between dream construction and memory in terms of these new developments. The neural network architecture of memory structures in the brain is described and illustrated with simple examples. We see how a network is connected and how connection weights vary with experience. The distributed representation stored by the network and its crucial properties for mental functioning are discussed. These concepts are used to explain how particular memories of past events are selected for inclusion in the dream. The properties of the neural network suggest that images of distinct past events are conflated at times during the selection process. The appearance of these conflated images may complicate the matching of day residues with representations of past events in the dream itself. Some likely implications for psychoanalytic theory are explored.

Self-Organization Processes in Chaotic Neural Networks Under External Periodic Force

1997 ◽  
Vol 07 (08) ◽  
pp. 1887-1895 ◽  
Author(s):  
Vladimir E. Bondarenko
Keyword(s):  
Neural Network ◽  
External Force ◽  
Self Organization ◽  
Largest Lyapunov Exponent ◽  
Periodic Force ◽  
Chaotic Neural Networks ◽  
The Neural Network ◽  
Quantitative Characteristics ◽  
The Individual ◽  
The Brain

Self-organization processes in an analog asymmetric neural network with time delay and under an external sinusoidal force are considered. Quantitative characteristics of the neuron outputs (spectrum, correlation dimension, largest Lyapunov exponent, Shannon entropy, normalized and renormalized Shannon entropies) are studied in dependence on the frequency and amplitude of the external force. It is shown that the external sinusoidal force allows the control of the degree of chaos and produces transitions "order–chaos", "chaos–order" and "chaos–chaos" with different quantitative characteristics. Information processing both by the individual neurons and by the neural network as a system is discussed. Chaotic neural network under an external force is considered as a qualitative model of the infra-frequencies action on the brain.

scholarly journals It may be time to perfect the neuron of artificial neural network

2020 ◽  
Author(s):  
Gang Liu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Input Data ◽  
Basic Unit ◽  
Future Research ◽  
Brain Neuron ◽  
Artificial Neuron ◽  
The Neural Network ◽  
Artificial Neural ◽  
The Brain

In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence. The basic unit of an ANN is to mimic neurons in the brain. Neuron in ANNs is expressed as f(wx+b) or f(wx).This structure does not consider the information processing capabilities of dendrites. However, recently, studies shown that dendrites participate in pre-calculation in the brain. Concretely, biological dendrites play a role in the pre-processing to the interaction information of input data. Therefore, it's time to perfect the neuron of the neural network. This paper added dendrite processing section, and presented a novel artificial neuron, according to our previous studies (CR-PNN or Gang transform). The dendrite processing section can be expressed as WA.X. Because I perfected the basic unit of ANNs-neuron, there are so many networks to try, this article gives the basic architecture for reference in future research.

The Lamprey’s Dilemma

2020 ◽  
pp. 35-45
Author(s):  
Thomas Boraud
Keyword(s):  
Neural Network ◽  
Spinal Cord ◽  
Reticular Formation ◽  
Motor System ◽  
The Other ◽  
Sensory Stimuli ◽  
Reticulospinal Neurons ◽  
The Neural Network ◽  
Effector System

This chapter focuses on the neural network, demonstrating how the principles described in the previous chapter are implemented in vertebrates, taking as a blueprint the oldest one: the lamprey. The reticulospinal neurons belong to the reticular formation located in the brainstem of the lamprey. These reticulospinal neurons act as the effector system. Apart from the peripheral input that comes back from the spinal cord, the reticular formation receives, among other things, input from the diencephalon and specifically the thalamus. This structure allows interfacing between sensory stimuli (visual, auditory, and olfactory) and the motor system. The other very important targets of the thalamus in the lamprey are the basal ganglia. The chapter then goes on to explain the diencephalic and telencephalic loops.

Sign in / Sign up

Export Citation Format

Share Document