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Vol 431 No 7010 pp723-882
Vol 431 No 7010 pp723-882

... disorders. In the past decade, biological findings on dopamine function have been infused with concepts taken from computational theories of reinforcement learning. These more abstract approaches have now been applied to describe the biological algorithms at play in our brains when we form value jud ...
Computational Intelligence in Intrusion Detection System
Computational Intelligence in Intrusion Detection System

... Many research efforts have been focused on how to construct affection and accurate intrusion detection models.A variety of computational intelligence techniques have been proposed in the literature including fuzzy logic [8], neural networks [9] and support vector machines (SVM) [8, 10]. In particula ...
What and Where Information in the Caudate Tail Guides Saccades
What and Where Information in the Caudate Tail Guides Saccades

... results suggest that CDt neurons receive both “what” and “where” information and guide saccades to visual objects. ...
Automatic clustering with application to time dependent fault detection in chemical processes
Automatic clustering with application to time dependent fault detection in chemical processes

... environment. Before clustering, process lags are identified via signal cross-correlations. From this, a least-squares optimal signal time shift is calculated. Dimensional reduction techniques are used to visualise the data. Various nonlinear dimensional reduction techniques have been proposed in rec ...
Amoeba-Based Emergent Computing: Combinatorial Optimization
Amoeba-Based Emergent Computing: Combinatorial Optimization

Selectivity for the Shape, Size, and Orientation of Objects for
Selectivity for the Shape, Size, and Orientation of Objects for

... closely related to the handgrip than to the object shape. We found a similar selectivity for handgrip in motor-dominant neurons that did not show any visual response. With regard to the size of the objects, 16 of 26 object-type neurons tested were selective for both size and shape, whereas 9 object- ...
pdf file
pdf file

... Societies are characterised by complex dynamics involving interaction between large numbers of actors and groups of actors. If such complex dynamics takes place in an completely unstructured, incoherent manner, any actor involved has not much to rely on to do prediction, and is not able to function ...
Selectivity for the Shape, Size, and Orientation of Objects for
Selectivity for the Shape, Size, and Orientation of Objects for

A Neural Theory of Visual Attention
A Neural Theory of Visual Attention

... Filtering is done in such a way that the number of cells in which an object is represented increases with the behavioral importance of the object (parallel processing with differential allocation of resources). More specifically, the probability that a cortical neuron represents a particular object ...
Midbrain Dopamine Neurons Encode a Quantitative Reward
Midbrain Dopamine Neurons Encode a Quantitative Reward

... and the theoretically defined reward prediction error by determining what precise function of previous rewards best predicts the activity of these neurons. To this end, we recorded the activity of midbrain dopamine neurons while overtrained awake-behaving primates performed a saccadic timing task th ...
PPT
PPT

The Nonparametric Kernel Bayes Smoother
The Nonparametric Kernel Bayes Smoother

A Fuzzy Ontology Extension of WordNet and EuroWordnet for
A Fuzzy Ontology Extension of WordNet and EuroWordnet for

... when dealing with longer sets of hyponymic synsets or multiple word senses in each of the synsets. Prototype theory [18] provides an approach to account for such effects of prototypicality on categorization. Prototype theory is based on the fact that concepts are graded. They show different degrees ...
A. Azzini "A New Genetic Approach for Neural Network Design and
A. Azzini "A New Genetic Approach for Neural Network Design and

... This thesis contributes several key items to the field of evolutionary algorithms for neural network design and optimization. The evolutionary process is a more integrated and rational way of designing ANNs since it allows single aspects of the design to be taken into account as well as several inte ...
New Concepts of the Neuroendocrine Regulation of Gonadotropin
New Concepts of the Neuroendocrine Regulation of Gonadotropin

... are functionally defined. It appears unlikely that a single neurotransmitter cell group mediates transmission of the daily neuronal signal to the LHRH releasing system, given the sheer volume of pharmacological and transmitter effects on preovulatory surges that have been demonstrated. How Are Neura ...
On Revising Fuzzy Belief Bases
On Revising Fuzzy Belief Bases

PRINCIPLES OF NEUROBIOLOGY CHAPTER 6
PRINCIPLES OF NEUROBIOLOGY CHAPTER 6

The encoding and decoding of com-
The encoding and decoding of com-

... The buffer cells B1 and B2 integrate, in time, the network activity performing a low-pass approximation of the signal over two adjacent time windows given by the asynchronous inhibition received from cell A. The differentiation performed by the excitatory and inhibitory connections to W gives rise t ...
Coordinated Optimization of Visual Cortical Maps
Coordinated Optimization of Visual Cortical Maps

... disregarded transient states that could in principle dominate developmental optimization on biologically relevant timescales. Such transient solutions are expected to be more irregular than the final attractor states. Analytical results were obtained using a perturbative treatment close to the patte ...
DCN principal cells respond to spectral edges, which requires additional inhibitory effects in DCN
DCN principal cells respond to spectral edges, which requires additional inhibitory effects in DCN

A Review of Cell Assemblies by Huyck and
A Review of Cell Assemblies by Huyck and

A thalamic reticular networking model of consciousness
A thalamic reticular networking model of consciousness

... Hz), simulating background activity, but showed shortterm depression in such amplitudes at gamma frequencies (more than 30 Hz), simulating sensory transmission [55]. The same study also found that intra-TRN inhibition suppresses TRN tonic-spike selectively at non-gamma stimulus frequencies, which ar ...
Modeling multiple time scale firing rate adaptation in a neural
Modeling multiple time scale firing rate adaptation in a neural

... 3.2 Quantifying rate adaptation using phase leads of linear filters By using the above filter models, specifying adaptation dynamics is equivalent to specifying the form of h(t). However, not all forms of h(t) are easy to implement, such as modeling power law adaptation in a neural network. One solu ...
Neuromorphic computing
Neuromorphic computing

... This kind of computing is very similar to what can be found in a robotic controller. But the sensors and actuators are completely different, compared to humans and animals, thus the brain is substituted by a computer. ...
osborne
osborne

... due to a mixture of poor experimental technique, poor experimenter/subject communication and an inability to appreciate the full implications of other psychologists work. These problems have cast doubt on a number of his ideas. It is important to examine these criticisms to ensure that the more rele ...
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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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