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Corticothalamic feedback and sensory processing
Corticothalamic feedback and sensory processing

... promising for two reasons. First, any concerns over anesthesia-dependent activity patterns are eliminated. Second, several recent studies have shown that thalamic activity can be markedly modulated by behaviorally relevant tasks [45–48,49,50,51]. For most of these tasks, it is difficult, if not imp ...
the central nervous system
the central nervous system

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Online Textbook Worksheets
Online Textbook Worksheets

... _____ 2. Sensory neurons carry nerve impulses from the brain and spinal cord to muscles and glands. _____ 3. The peripheral nervous system includes the brain and spinal cord. _____ 4. The myelin sheath is similar to the plastic that encases an electrical cord. _____ 5. The somatic nervous system con ...
Spinal Sensorimotor System: An Overview
Spinal Sensorimotor System: An Overview

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Volitional enhancement of firing synchrony and oscillation by
Volitional enhancement of firing synchrony and oscillation by

... We trained rats to engage in a free-operant task in which nosepoke behavior was rewarded in session 1, and firing rates and synchrony of multiple neighboring neurons above preset criteria were rewarded in sessions 2 and 3, respectively. Placing contingency of reward on firing synchrony in session 3 ...
Evolution of Patterning Systems and Circuit Elements for Locomotion
Evolution of Patterning Systems and Circuit Elements for Locomotion

... to only a few classes of neurons (Jessell, 2000). Despite these significant differences, many species appear to use a common set of intrinsic determinants during early neural patterning. In this section, we compare and contrast the mechanisms of neural induction and global patterning within the two ...
Neural Coding and Auditory Perception
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Hierarchical Processing of Auditory Objects in Humans
Hierarchical Processing of Auditory Objects in Humans

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Chaper 1. A Brief History of Cognitive Neuroscience
Chaper 1. A Brief History of Cognitive Neuroscience

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Cerebellum: The Brain for an Implicit Self
Cerebellum: The Brain for an Implicit Self

... Current systems neurobiology addresses this issue to some extent, but available methodology and technology are limited and guiding hypotheses are still sparse. To this end, research on the cerebellum is on the forefront for asking the question “How does our brain accomplish its most complex and soph ...
A Dualistic Theory of Consciousness
A Dualistic Theory of Consciousness

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Membrane Properties Underlying the Firing of Neurons in the Avian
Membrane Properties Underlying the Firing of Neurons in the Avian

... potential, the arrival rate is at least equal to the neuron’s discharge rate. Moreover, because an NM neuron is innervated by 2-3 VIIIth nerve afferents (Hackett et al., 1982; Jackson and Parks, 1982) and because each fiber can discharge at a maximum sustained rate of approximately 400 Hz (Manley et ...
ppt - IISER Pune
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Functional Synaptic Contacts by Intranuclear
Functional Synaptic Contacts by Intranuclear

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A neurocomputational model of the mammalian fear
A neurocomputational model of the mammalian fear

... regions of the mammalian brain. Parts of the amygdala, periaqueductal gray, cortex and thalamus, and hippocampus are included and are connected to each other in a biologically plausible manner. The model was built using the principles of the Neural Engineering Framework (NEF): a mathematical framewo ...
Lissencephaly - Cambridge University Press
Lissencephaly - Cambridge University Press

... lissencephaly. The other affected infants are said to have been "just the same". neuronal migration, with production of a four-layered cortex similar to that of a 50-100 mm. foetus (Hanaway et a l . , 1968). It has been suggested that factors intrinsic or extrinsic to the neurons could cause such a ...
Direct and Indirect Activation of Cortical Neurons by Electrical
Direct and Indirect Activation of Cortical Neurons by Electrical

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Ch. 11 Review
Ch. 11 Review

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Linking Cognitive Tokens to Biological Signals: Dialogue Context Improves
Linking Cognitive Tokens to Biological Signals: Dialogue Context Improves

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2015 Paget Lecture transcript Four stories about the brain
2015 Paget Lecture transcript Four stories about the brain

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Electron microscopical reconstruction of the anterior sensory
Electron microscopical reconstruction of the anterior sensory

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disparity detection from stereo
disparity detection from stereo

... type or error-back propagation type. There has been no biological evidence to support error back-propagation, but the Hebbian type of learning has been supported by the Spike-time dependent plasticity (STDP) [7]. SOM type of networks that use both top-down and bottom-up inputs has not be studied unt ...
Fetal Awareness
Fetal Awareness

... After fertilisation, the embryo's cells multiply and after about 10 days separate into the ectoderm (precursors of the outer skin, nervous system amd other parts) and endoderm (precursor to the digestive system and lungs), soon separated by the mesoderm (to become muscles, bones, circulatory system ...
Human Brain Networks: Spiking Neuron Models
Human Brain Networks: Spiking Neuron Models

... The human central nervous system involves a complex large-scale interconnected neural network involving feedforward and feedback (or recurrent) networks, with the brain serving as the central element of this network system. The brain is interconnected to receptors that transmit sensory information t ...
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Nervous system network models

Network of human nervous system comprises nodes (for example, neurons) that are connected by links (for example, synapses). The connectivity may be viewed anatomically, functionally, or electrophysiologically. These are presented in several Wikipedia articles that include Connectionism (a.k.a. Parallel Distributed Processing (PDP)), Biological neural network, Artificial neural network (a.k.a. Neural network), Computational neuroscience, as well as in several books by Ascoli, G. A. (2002), Sterratt, D., Graham, B., Gillies, A., & Willshaw, D. (2011), Gerstner, W., & Kistler, W. (2002), and Rumelhart, J. L., McClelland, J. L., and PDP Research Group (1986) among others. The focus of this article is a comprehensive view of modeling a neural network (technically neuronal network based on neuron model). Once an approach based on the perspective and connectivity is chosen, the models are developed at microscopic (ion and neuron), mesoscopic (functional or population), or macroscopic (system) levels. Computational modeling refers to models that are developed using computing tools.
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