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Model of Cortical-Basal Ganglionic Processing: Encoding the Serial
Model of Cortical-Basal Ganglionic Processing: Encoding the Serial

Short Course III - David Kleinfeld - University of California San Diego
Short Course III - David Kleinfeld - University of California San Diego

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A Logical Characterisation of Ordered Disjunction
A Logical Characterisation of Ordered Disjunction

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NMDA receptor blockade causes selective prefrontal

... not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license. ...
bioresources.com - NC State University
bioresources.com - NC State University

... available. Due to these features, the artificial neural network can be used in many fields. The ANN has an algorithm that has the capability to learn and make decisions during the process (Ataman 1999). The unknown and hard-to-determine relations between the input and output can be revealed without ...
Artificial Intelligence in Reservoir Simulation and
Artificial Intelligence in Reservoir Simulation and

... designed so that an engineer or a geologist with a Bachelor’s degree will be able to comfortably develop  a Top‐Down model in a relatively short period of time with minimum amount of data. The disadvantage  of Top‐Down modeling is that it cannot be performed on “any” field. It is designed for fields ...
INSTANTANEOUSLY TRAINED NEURAL NETWORKS WITH
INSTANTANEOUSLY TRAINED NEURAL NETWORKS WITH

Cellular and network mechanisms of electrographic
Cellular and network mechanisms of electrographic

... hyperpolarizing current pulses, probably caused by the activation of Ih. Also, models of isolated PY neurons with Ih included in their dendritic compartment showed that rebound depolarization was sufficient to generate single action potentials or spike-bursts [73]. The increased excitability of PY n ...
Logic and artificial intelligence - Stanford Artificial Intelligence
Logic and artificial intelligence - Stanford Artificial Intelligence

Learning Visual Representations for Perception
Learning Visual Representations for Perception

... discretized into a 50 × 50 grid. The brighter the location, the greater its value. Right: The final value function obtained by RLVC. 2298 different visual features, of which RLVC selected 200 (9%). The computation stopped after the generation of k = 84 image classifiers, which took 35 minutes on a 2 ...
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Predicting is not explaining: targeted learning of the dative alternation
Predicting is not explaining: targeted learning of the dative alternation

... problem, he compares the performances of different classifiers based either on the principle of parametric regression or on more data-adaptive algorithms gathered under the banner of machine learning, both in terms of accuracy of prediction and of quality of the underlying models for human learning. ...
bod02a - Carnegie Mellon School of Computer Science
bod02a - Carnegie Mellon School of Computer Science

A Logical Characterisation of Ordered Disjunction
A Logical Characterisation of Ordered Disjunction

A circular model for song motor control in Serinus canaria
A circular model for song motor control in Serinus canaria

... 2014). This model consisted of a neural motif with an excitatory population, an inhibitory population, and a simple time periodic input. Following this work, the first building block of our model is an excitatory population coupled to an inhibitory one, and we require that the activity of the excita ...
Theroleofdendritesinauditory coincidence detection
Theroleofdendritesinauditory coincidence detection

... Coincidence-detector neurons in the auditory brainstem of mammals and birds use interaural time differences to localize sounds1,2. Each neuron receives many narrow-band inputs from both ears and compares the time of arrival of the inputs with an accuracy of 10–100 ms (refs 3–6). Neurons that receive ...
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Encoding and Retrieval of Episodic Memories: Role of Hippocampus
Encoding and Retrieval of Episodic Memories: Role of Hippocampus

... learning of a single word in a list learning experiment. Note that these are not semantic representations of the word which would be activated in a wide range of different contexts—rather, they code the activation of the semantic representation in a specific episode. As described below, in each regi ...
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... • About 1 trillion (1012) neurons in the nervous system • Neuroglia outnumber the neurons by as much as 50 to 1 • Neuroglia or glial cells – Support and protect the neurons – Bind neurons together and form framework for nervous tissue – In fetus, guide migrating neurons to their destination – If mat ...
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Emergence of new signal-primitives in neural systems
Emergence of new signal-primitives in neural systems

... do in order to successfully predict the behavior of an evolving system. Combinatoric and creative emergence can be operationally distinguished by changes in apparent effective dimensionality. Whenever a new independent observable is added to a model, its dimensionality increases by one. A system tha ...
A Survey of the Eighth National Conference on Artificial Intelligence
A Survey of the Eighth National Conference on Artificial Intelligence

... methodological choices represented by fields 9–18 of table 1. For example, if most systemcentered papers present natural examples, and most model-centered papers present abstract examples (field 9), then because this distribution of task types is unlikely to have occurred by chance, the classificati ...
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Computational principles underlying recognition
Computational principles underlying recognition

... Abstract Grasshoppers and crickets independently evolved hearing organs and acoustic communication. They differ considerably in the organization of their auditory pathways, and the complexity of their songs, which are essential for mate attraction. Recent approaches aimed at describing the behaviora ...
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Neural modeling fields

Neural modeling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networks, fuzzy logic, and model based recognition. It has also been referred to as modeling fields, modeling fields theory (MFT), Maximum likelihood artificial neural networks (MLANS).This framework has been developed by Leonid Perlovsky at the AFRL. NMF is interpreted as a mathematical description of mind’s mechanisms, including concepts, emotions, instincts, imagination, thinking, and understanding. NMF is a multi-level, hetero-hierarchical system. At each level in NMF there are concept-models encapsulating the knowledge; they generate so-called top-down signals, interacting with input, bottom-up signals. These interactions are governed by dynamic equations, which drive concept-model learning, adaptation, and formation of new concept-models for better correspondence to the input, bottom-up signals.
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