Neural-Symbolic Learning and Reasoning: Contributions and
... d'Avila Garcez, Lamb, and Gabbay (2009) for an overview). Meanwhile, there has been some suggestive recent work showing that neural networks can learn entire sequences of actions, thus amounting to "mental simulation" of some concrete, temporally extended activity. There is also a very well develope ...
... d'Avila Garcez, Lamb, and Gabbay (2009) for an overview). Meanwhile, there has been some suggestive recent work showing that neural networks can learn entire sequences of actions, thus amounting to "mental simulation" of some concrete, temporally extended activity. There is also a very well develope ...
The Neural Optimal Control Hierarchy
... learned motor components (i.e., synergies), are used as basis functions, and combined through weighted summation to compose the desired movement. This process is described in detail in [?]. The PM/SMA areas act as the highest levels in the motor hierarchy, generating signals that proceed through M1, ...
... learned motor components (i.e., synergies), are used as basis functions, and combined through weighted summation to compose the desired movement. This process is described in detail in [?]. The PM/SMA areas act as the highest levels in the motor hierarchy, generating signals that proceed through M1, ...
Sensory system evolution at the origin of craniates
... types, including the bipolar neurons of the peripheral nervous system, occurred at the same time as the elaboration of the brain (and the rest of the central nervous system) has been suggested (Holland & Graham 1995; Northcutt 1996) and is referred to here as the `concurrent gain hypothesis’. Northc ...
... types, including the bipolar neurons of the peripheral nervous system, occurred at the same time as the elaboration of the brain (and the rest of the central nervous system) has been suggested (Holland & Graham 1995; Northcutt 1996) and is referred to here as the `concurrent gain hypothesis’. Northc ...
ECE 517 Final Project Development of Predator/Prey Behavior via Reinforcement Learning
... In the Player-Stage version, each robot’s code runs as a separate process. The program works by initializing the robots in the world. An action is taken by setting the linear and angular speeds of the robot. After this, the program determines the new state and the reward. The reward is observed acco ...
... In the Player-Stage version, each robot’s code runs as a separate process. The program works by initializing the robots in the world. An action is taken by setting the linear and angular speeds of the robot. After this, the program determines the new state and the reward. The reward is observed acco ...
Brain-implantable biomimetic electronics as the next era in neural
... result from stroke, and the impaired ability to execute skilled movements following trauma to brain regions responsible for motor control. Although the barriers to creating intracranial, electronic neural prosthetics have seemed insurmountable in the past, the biological and engineering sciences are ...
... result from stroke, and the impaired ability to execute skilled movements following trauma to brain regions responsible for motor control. Although the barriers to creating intracranial, electronic neural prosthetics have seemed insurmountable in the past, the biological and engineering sciences are ...
Evolutionary Connectionism and Mind/Brain Modularity - laral
... arrows connecting boxes indicate that information processed by some particular module is then passed on to another module for further processing. In contrast, connectionism tends to be antimodularistic. In neural networks information is represented by distributed patterns of activation in potentiall ...
... arrows connecting boxes indicate that information processed by some particular module is then passed on to another module for further processing. In contrast, connectionism tends to be antimodularistic. In neural networks information is represented by distributed patterns of activation in potentiall ...
Retrieval of the diffuse attenuation coefficient Kd(λ)
... Purely empirical method Non-linear inversion Universal approximator of any derivable function Can handle “easily” noise and outliers Taking more spectral information ...
... Purely empirical method Non-linear inversion Universal approximator of any derivable function Can handle “easily” noise and outliers Taking more spectral information ...
Resistive communications based on neuristors
... Memristors are used in hardware neural networks, both to implement different learning algorithms and back propagation. It is also used to build neuromorphic systems (hardware that mimics the brain). Memristors can also be used in analog circuits, for example as reconfigurable resistors to change the ...
... Memristors are used in hardware neural networks, both to implement different learning algorithms and back propagation. It is also used to build neuromorphic systems (hardware that mimics the brain). Memristors can also be used in analog circuits, for example as reconfigurable resistors to change the ...
Event-Related Potentials
... attracting its dynamics to learned (attractor) patterns. In this regard, artificial neural networks that operate according to attractor dynamics bear a resemblance to cortical networks at the local level. (See COMPUTING WITH ATTRACTORS.) An essential element of overall cortical network function, how ...
... attracting its dynamics to learned (attractor) patterns. In this regard, artificial neural networks that operate according to attractor dynamics bear a resemblance to cortical networks at the local level. (See COMPUTING WITH ATTRACTORS.) An essential element of overall cortical network function, how ...
the neural impulse
... The soma (or cell body) is the neuron’s control centre. It contains the nucleus and other organelles which are necessary for the neuron’s survival. Dendrites are branched structures specialized to receive information from other neurons or cells. Therefore, the dendrites are the neuron's information ...
... The soma (or cell body) is the neuron’s control centre. It contains the nucleus and other organelles which are necessary for the neuron’s survival. Dendrites are branched structures specialized to receive information from other neurons or cells. Therefore, the dendrites are the neuron's information ...
(1996). "A multi-threshold neural network for frequency estimation,"
... in their response thresholds. These response differences suggest that auditory sound coding could be based on more than just the CF of the neurons. In fact, physiological experiments demonstrate that when stimulated by a complex sound, bres with low spontaneous rates predominantly respond to the en ...
... in their response thresholds. These response differences suggest that auditory sound coding could be based on more than just the CF of the neurons. In fact, physiological experiments demonstrate that when stimulated by a complex sound, bres with low spontaneous rates predominantly respond to the en ...
Ne_plas_cause
... visual, auditory and olfactory) signals that regulate social behavior, or relate then to their own affective states (moods), which regulate approach to or avoidance of other members of the group and are thus the building blocks of social interactions. They avoid other members of the group and seem a ...
... visual, auditory and olfactory) signals that regulate social behavior, or relate then to their own affective states (moods), which regulate approach to or avoidance of other members of the group and are thus the building blocks of social interactions. They avoid other members of the group and seem a ...
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.