scholarly journals Neural Contributions of the Hypothalamus to Parental Behaviour

2021 ◽  
Vol 22 (13) ◽  
pp. 6998
Author(s):  
Chitose Orikasa
Keyword(s):  
Neural Networks ◽  
Neural Circuits ◽  
Social Cues ◽  
Male Mice ◽  
Neural Responses ◽  
Parental Behaviour ◽  
Melanin Concentrating Hormone ◽  
Virgin Male ◽  
And Function ◽  
The Brain

Parental behaviour is a comprehensive set of neural responses to social cues. The neural circuits that govern parental behaviour reside in several putative nuclei in the brain. Melanin concentrating hormone (MCH), a neuromodulator that integrates physiological functions, has been confirmed to be involved in parental behaviour, particularly in crouching behaviour during nursing. Abolishing MCH neurons in innate MCH knockout males promotes infanticide in virgin male mice. To understand the mechanism and function of neural networks underlying parental care and aggression against pups, it is essential to understand the basic organisation and function of the involved nuclei. This review presents newly discovered aspects of neural circuits within the hypothalamus that regulate parental behaviours.

scholarly journals Learning the architectural features that predict functional similarity of neural networks

2020 ◽  
Author(s):  
Adam Haber ◽  
Elad Schneidman
Keyword(s):  
Neural Networks ◽  
Architectural Design ◽  
Neural Circuits ◽  
High Accuracy ◽  
Structure And Function ◽  
Functional Similarity ◽  
Architectural Features ◽  
Wide Range ◽  
And Function ◽  
Sparse Set

ABSTRACTThe mapping of the wiring diagrams of neural circuits promises to allow us to link structure and function of neural networks. Current approaches to analyzing connectomes rely mainly on graph-theoretical tools, but these may downplay the complex nonlinear dynamics of single neurons and networks, and the way networks respond to their inputs. Here, we measure the functional similarity of simulated networks of neurons, by quantifying the similitude of their spiking patterns in response to the same stimuli. We find that common graph theory metrics convey little information about the similarity of networks’ responses. Instead, we learn a functional metric between networks based on their synaptic differences, and show that it accurately predicts the similarity of novel networks, for a wide range of stimuli. We then show that a sparse set of architectural features - the sum of synaptic inputs that each neuron receives and the sum of each neuron’s synaptic outputs - predicts the functional similarity of networks of up to 100 cells, with high accuracy. We thus suggest new architectural design principles that shape the function of neural networks, which conform with experimental evidence of homeostatic mechanisms.

scholarly journals Achieving stable dynamics in neural circuits

2020 ◽  
Author(s):  
Leo Kozachkov ◽  
Mikael Lundqvist ◽  
Jean-Jacques Slotine ◽  
Earl K. Miller
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Neural Circuits ◽  
Time Varying ◽  
Biological Complexity ◽  
Multiple Sources ◽  
Hebbian Plasticity ◽  
Autonomous Activity ◽  
The Brain ◽  
Shed Light

1AbstractThe brain consists of many interconnected networks with time-varying, partially autonomous activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable, reproducible state (or sequence of states) for its computations to make sense. We approached this problem from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included inhibitory Hebbian plasticity, excitatory anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. Our findings shed light on how stable computations might be achieved despite biological complexity.

Deep Leaning Using Keras

2020 ◽  
pp. 33-60
Author(s):  
Deepa Joshi ◽  
Shahina Anwarul ◽  
Vidyanand Mishra
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Structure And Function ◽  
Learning Problems ◽  
Complex Data ◽  
Deep Layers ◽  
And Function ◽  
The Brain

A branch of artificial intelligence (AI) known as deep learning consists of statistical analysis algorithms known as artificial neural networks (ANN) inspired by the structure and function of the brain. The accuracy of predicting a task has tremendously improved with the implementation of deep neural networks, which in turn incorporate deep layers into the model allowing the system to learn complex data. This chapter intends to give a straightforward manual for the complexities of Google's Keras framework that is easy to understand. The basic steps for the installation of Anaconda, CUDA, along with deep learning libraries, specifically Keras and Tensorflow, are discussed. A practical approach towards solving deep learning problems in order to identify objects in CIFAR 10 dataset is explained in detail. This will help the audience in understanding deep learning through substantial practical examples to perceive algorithms instead of theory discussions.

scholarly journals White matter and cognition: making the connection

2016 ◽  
Vol 116 (5) ◽  
pp. 2093-2104 ◽  
Author(s):  
Christopher M. Filley ◽  
R. Douglas Fields
Keyword(s):  
Neural Networks ◽  
White Matter ◽  
Gray Matter ◽  
Chronic Traumatic Encephalopathy ◽  
White Matter Lesions ◽  
Normal Brain ◽  
Brain Structure And Function ◽  
And Function ◽  
The Brain

Whereas the cerebral cortex has long been regarded by neuroscientists as the major locus of cognitive function, the white matter of the brain is increasingly recognized as equally critical for cognition. White matter comprises half of the brain, has expanded more than gray matter in evolution, and forms an indispensable component of distributed neural networks that subserve neurobehavioral operations. White matter tracts mediate the essential connectivity by which human behavior is organized, working in concert with gray matter to enable the extraordinary repertoire of human cognitive capacities. In this review, we present evidence from behavioral neurology that white matter lesions regularly disturb cognition, consider the role of white matter in the physiology of distributed neural networks, develop the hypothesis that white matter dysfunction is relevant to neurodegenerative disorders, including Alzheimer's disease and the newly described entity chronic traumatic encephalopathy, and discuss emerging concepts regarding the prevention and treatment of cognitive dysfunction associated with white matter disorders. Investigation of the role of white matter in cognition has yielded many valuable insights and promises to expand understanding of normal brain structure and function, improve the treatment of many neurobehavioral disorders, and disclose new opportunities for research on many challenging problems facing medicine and society.

scholarly journals Astrocytes in Neural Circuits: Key Factors in Synaptic Regulation and Potential Targets for Neurodevelopmental Disorders

2021 ◽  
Vol 14 ◽  
Author(s):  
Xing Liu ◽  
Jun Ying ◽  
Xifeng Wang ◽  
Qingcui Zheng ◽  
Tiancheng Zhao ◽  
...  
Keyword(s):  
Neurodevelopmental Disorders ◽  
Neural Circuits ◽  
Key Factors ◽  
Space Filling ◽  
Synaptic Development ◽  
Synaptic Regulation ◽  
New Treatments ◽  
And Function ◽  
The Brain

Astrocytes are the major glial cells in the brain, which play a supporting role in the energy and nutritional supply of neurons. They were initially regarded as passive space-filling cells, but the latest progress in the study of the development and function of astrocytes highlights their active roles in regulating synaptic transmission, formation, and plasticity. In the concept of “tripartite synapse,” the bidirectional influence between astrocytes and neurons, in addition to their steady-state and supporting function, suggests that any negative changes in the structure or function of astrocytes will affect the activity of neurons, leading to neurodevelopmental disorders. The role of astrocytes in the pathophysiology of various neurological and psychiatric disorders caused by synaptic defects is increasingly appreciated. Understanding the roles of astrocytes in regulating synaptic development and the plasticity of neural circuits could help provide new treatments for these diseases.

scholarly journals How neural circuits achieve and use stable dynamics

2019 ◽  
Author(s):  
Leo Kozachkov ◽  
Mikael Lundqvist ◽  
Jean-Jacques Slotine ◽  
Earl K. Miller
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Recurrent Neural Networks ◽  
Neural Circuits ◽  
Stable State ◽  
Distributed Networks ◽  
Time Varying ◽  
Multiple Sources ◽  
Hebbian Plasticity ◽  
The Brain

1AbstractThe brain consists of many interconnected networks with time-varying activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable state for its computations to make sense. We approached this from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. We leveraged these findings to construct networks that could perform functionally relevant computations in the presence of noise and disturbance. Our work provides a blueprint for how to construct stable plastic and distributed networks.

scholarly journals Chunking sequence information by mutually predicting recurrent neural networks

2017 ◽  
Author(s):  
Toshitake Asabuki ◽  
Naoki Hiratani ◽  
Tomoki Fukai
Keyword(s):  
Neural Networks ◽  
Basal Ganglia ◽  
Language Processing ◽  
Cognitive Tasks ◽  
Sequence Information ◽  
Reservoir Computing ◽  
Neural Responses ◽  
Complex Sequences ◽  
Unsupervised Neural Networks ◽  
The Brain

AbstractInterpretation and execution of complex sequences is crucial for various cognitive tasks such as language processing and motor control. The brain solves this problem arguably by dividing a sequence into discrete chunks of contiguous items. While chunking has been accounted for by predictive uncertainty, alternative mechanisms have also been suggested, and the mechanism underlying chunking is poorly understood. Here, we propose a class of unsupervised neural networks for learning and identifying repeated patterns in sequence input with various degrees of complexity. In this model, a pair of reservoir computing modules, each of which comprises a recurrent neural network and readout units, supervise each other to consistently predict others’ responses to frequently recurring segments. Interestingly, this system generates neural responses similar to those formed in the basal ganglia during habit formation. Our model extends reservoir computing to higher cognitive function and demonstrates its resemblance to sequence processing by cortico-basal ganglia loops.

Ultrastructure of developing visual cortical synapses in the cat superior colliculus

1991 ◽  
Vol 49 ◽  
pp. 156-157
Author(s):  
Caroline A. Miller ◽  
Laura L. Bruce
Keyword(s):  
Superior Colliculus ◽  
Phosphate Buffer ◽  
Receptive Fields ◽  
Visual Cortical Area ◽  
Cortical Synapses ◽  
Visual Cortical ◽  
And Function ◽  
Phosphate Buffered Saline ◽  
The Brain ◽  
Cat Superior Colliculus

The first visual cortical axons arrive in the cat superior colliculus by the time of birth. Adultlike receptive fields develop slowly over several weeks following birth. The developing cortical axons go through a sequence of changes before acquiring their adultlike morphology and function. To determine how these axons interact with neurons in the colliculus, cortico-collicular axons were labeled with biocytin (an anterograde neuronal tracer) and studied with electron microscopy.Deeply anesthetized animals received 200-500 nl injections of biocytin (Sigma; 5% in phosphate buffer) in the lateral suprasylvian visual cortical area. After a 24 hr survival time, the animals were deeply anesthetized and perfused with 0.9% phosphate buffered saline followed by fixation with a solution of 1.25% glutaraldehyde and 1.0% paraformaldehyde in 0.1M phosphate buffer. The brain was sectioned transversely on a vibratome at 50 μm. The tissue was processed immediately to visualize the biocytin.

Comparative effects of Orchis anatolica vs. the red Korean Panax ginseng treatments on testicular structure and function of adult male mice

2015 ◽  
Vol 12 (1) ◽  
Author(s):  
Nabil A. Khouri ◽  
Haytham M. Daradka ◽  
Mohammed Z. Allouh ◽  
Ahmad S. Alkofahi
Keyword(s):  
Panax Ginseng ◽  
Male Fertility ◽  
Virgin Female ◽  
Reproductive Organs ◽  
Serum Markers ◽  
Structure And Function ◽  
Control Group ◽  
Male Mice ◽  
Fertility Potential ◽  
And Function

Abstract: The effects of: Both plants were administered orally to two separate mice groups at a dose of 800 mg/kg/day for 35 days and compared with control group. After treatment, 5 mice of each group were sacrificed and total mice weights, reproductive organs’ weights, spermatogenesis, and androgenic serum markers were investigated. The remaining mice from all groups were allowed to mate with virgin female mice to explore male fertility potential.: Results indicated that body and organs’ weights were increased significantly in mice treated with: We can conclude that

Revision Targeted Muscle Reinnervation Improves Secondary Pain Insult in an Upper Extremity Amputee: A Case Report

Hand ◽  
2021 ◽  
pp. 155894472199246
Author(s):  
David D. Rivedal ◽  
Meng Guo ◽  
James Sanger ◽  
Aaron Morgan
Keyword(s):  
Neuropathic Pain ◽  
Peripheral Nerves ◽  
Nerve Biopsy ◽  
Sensory Cortex ◽  
Denervated Muscle ◽  
Unique Case ◽  
Targeted Muscle Reinnervation ◽  
Muscle Reinnervation ◽  
And Function ◽  
The Brain

Targeted muscle reinnervation (TMR) has been shown to improve phantom and neuropathic pain in both the acute and chronic amputee population. Through rerouting of major peripheral nerves into a newly denervated muscle, TMR harnesses the plasticity of the brain, helping to revert the sensory cortex back toward the preinsult state, effectively reducing pain. We highlight a unique case of an above-elbow amputee for sarcoma who was initially treated with successful transhumeral TMR. Following inadvertent nerve biopsy of a TMR coaptation site, his pain returned, and he was unable to don his prosthetic. Revision of his TMR to a more proximal level was performed, providing improved pain and function of the amputated arm. This is the first report to highlight the concept of secondary neuroplasticity and successful proximal TMR revision in the setting of multiple insults to the same extremity.

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