Modeling the spinal cord neural circuitry controlling cat hindlimb
... high-threshold calcium, calcium-dependent potassium, leakage and synaptic ionic channels. The schematic of re4ex circuits was modi;ed from the previous models [1,4] and applied to each antagonistic group of muscles. The synaptic connections within and between the NMs and the structure of inputs of I ...
... high-threshold calcium, calcium-dependent potassium, leakage and synaptic ionic channels. The schematic of re4ex circuits was modi;ed from the previous models [1,4] and applied to each antagonistic group of muscles. The synaptic connections within and between the NMs and the structure of inputs of I ...
a quantitative study of pruning by optimal brain
... improvements in performance have been obtained. The Optimal Brain Damage (OBD) scheme of Le Cun, Denker and Solla [12], stands out for its success in reducing the complexity of a well-trained network for identi cation of hand-written ZIP-codes [11, 12]. The method is based on the computation of the ...
... improvements in performance have been obtained. The Optimal Brain Damage (OBD) scheme of Le Cun, Denker and Solla [12], stands out for its success in reducing the complexity of a well-trained network for identi cation of hand-written ZIP-codes [11, 12]. The method is based on the computation of the ...
How Molecules Matter to Mental Computation
... networks (see Thagard 1996 for a concise survey). In particular, artificial neural networks have the same abstract computational power as Turing machines and rule-based systems, but they are advocated by many researchers because they implement structures and procedures that seem to capture more clos ...
... networks (see Thagard 1996 for a concise survey). In particular, artificial neural networks have the same abstract computational power as Turing machines and rule-based systems, but they are advocated by many researchers because they implement structures and procedures that seem to capture more clos ...
NEURAL REGULATION OF BREATHING Section 4, Part A
... I. Medullary Respiratory Center A. Medulla isolated from cranial nerves and higher centers can drive respiratory muscles 1. rhythm appears "ataxic" B. Integration of neural centers 1. nucleus of the tractus solitarus (NTS) or dorsal resp. group a. appears to receive and integrate sensory information ...
... I. Medullary Respiratory Center A. Medulla isolated from cranial nerves and higher centers can drive respiratory muscles 1. rhythm appears "ataxic" B. Integration of neural centers 1. nucleus of the tractus solitarus (NTS) or dorsal resp. group a. appears to receive and integrate sensory information ...
A novel neuroprosthetic interface with the peripheral nervous system
... developed. CNS-based approaches attempt to restore motor function by directly deriving commands from the patient’s motor cortex. Two major strategies have emerged to accomplish this. The first is a non-invasive technique that obtains a movement intent via surface (scalp) electrodes over the motor co ...
... developed. CNS-based approaches attempt to restore motor function by directly deriving commands from the patient’s motor cortex. Two major strategies have emerged to accomplish this. The first is a non-invasive technique that obtains a movement intent via surface (scalp) electrodes over the motor co ...
OCULAR HEMORRHAGE IN CHILDREN
... turn at the interhemispheric fissure and run parallel to that fissure forming the longitudinal bundle of Probst. Probst bundles invaginate the medial borders of the lateral ventricles, giving the ventricles a crescentic shape (batwing), more so frontally. When the callosal body is absent, the bodies ...
... turn at the interhemispheric fissure and run parallel to that fissure forming the longitudinal bundle of Probst. Probst bundles invaginate the medial borders of the lateral ventricles, giving the ventricles a crescentic shape (batwing), more so frontally. When the callosal body is absent, the bodies ...
CHAPTER TWO
... the network is provided with inputs but not with desired outputs. The system itself must then decide what features it will use to group the input data. This is often referred to as self-organization or adaption. At the present time, unsupervised learning is not well understood. This adaption to the ...
... the network is provided with inputs but not with desired outputs. The system itself must then decide what features it will use to group the input data. This is often referred to as self-organization or adaption. At the present time, unsupervised learning is not well understood. This adaption to the ...
Open access
... linearities and the possible competence of only a few neurons in the final behavior initiating mechanism, deep buried in the flys brain. Such mechanism or agent provide the fly with genuine spontaneity -a distinctive label of living creaturesenabling the insect to get bored about tedious situations and ...
... linearities and the possible competence of only a few neurons in the final behavior initiating mechanism, deep buried in the flys brain. Such mechanism or agent provide the fly with genuine spontaneity -a distinctive label of living creaturesenabling the insect to get bored about tedious situations and ...
Unsupervised models and clustering
... Data dimensionality reduction, from N (input size) to m (map size, usually 23) Each data is represented by the coordinate of the unit on which it is projected, that is the one that has the maximum activation, or, in other words, whose weight is more similar (closer) to the data itself ...
... Data dimensionality reduction, from N (input size) to m (map size, usually 23) Each data is represented by the coordinate of the unit on which it is projected, that is the one that has the maximum activation, or, in other words, whose weight is more similar (closer) to the data itself ...
Modeling Economic Choice under Radical Uncertainty: Machine
... sentiment (Ye et al., 2009; Cheung et al., 2003; Huang et al., 2012). Neural networks are models that are heavily influenced by the way the human brain works. It is structured by neurons that send activation impulses to each other, and so is the overall architecture of the neural network model. The ...
... sentiment (Ye et al., 2009; Cheung et al., 2003; Huang et al., 2012). Neural networks are models that are heavily influenced by the way the human brain works. It is structured by neurons that send activation impulses to each other, and so is the overall architecture of the neural network model. The ...
Machine Learning
... Artificial Neural Networks • An artificial neural network (or simply a neural network) can be defined as a model of reasoning based on the human brain. • The brain consists of a densely interconnected set of nerve cells, or basic information-processing units, called neurons. • The human brain incorp ...
... Artificial Neural Networks • An artificial neural network (or simply a neural network) can be defined as a model of reasoning based on the human brain. • The brain consists of a densely interconnected set of nerve cells, or basic information-processing units, called neurons. • The human brain incorp ...
Intelligent data engineering
... Figure 15.2: Architecture of a cellular network (a) and simple view of the hierarchical analysis algorithm (b). In outlier detection as well neural as statistical methods can be used to find out network elements with decreased performance or otherwise anomalous traffic profile. Statistical approache ...
... Figure 15.2: Architecture of a cellular network (a) and simple view of the hierarchical analysis algorithm (b). In outlier detection as well neural as statistical methods can be used to find out network elements with decreased performance or otherwise anomalous traffic profile. Statistical approache ...
Artificial neural network
In machine learning and cognitive science, artificial neural networks (ANNs) are a family of statistical learning models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected ""neurons"" which exchange messages between each other. The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning.For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.