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TESIS DOCTORAL Dynamics and Synchronization in Neuronal Models
TESIS DOCTORAL Dynamics and Synchronization in Neuronal Models

... Afterwards we study the role played by the diversity on an ensemble of interacting neurons. In chapter 4 we demonstrate that the presence of heterogeneity in some parameters of the neurons can enhance the response of the system to an external periodic modulation. First, we ...
Machine Condition Monitoring Using Artificial Intelligence: The
Machine Condition Monitoring Using Artificial Intelligence: The

Sensory Adaptation and Short Term Plasticity as Bayesian
Sensory Adaptation and Short Term Plasticity as Bayesian

... filter). We then model the response of the postsynaptic neuron by the observed presynaptic activity divided by the total estimate of the presynaptic excitability (see Methods for details). The effect of this optimal adaptation rule is to normalize the inputs from each presynaptic neuron. Inputs from ...
Genetic Generation of Connection Patterns for a Dynamic Artificial
Genetic Generation of Connection Patterns for a Dynamic Artificial

... synapses on other neurons, and just like sensor elements, their outputs are in one of two states: active or inactive. The outputs of the artificial neurons are sampled in the same way that the sensor elements are sampled, and the corresponding artificial synapses are activated if the neuron they are ...
A Dendritic Disinhibitory Circuit Mechanism for Pathway
A Dendritic Disinhibitory Circuit Mechanism for Pathway

... This dendritic disinhibitory circuit formed by VIP and SOM neurons is proposed to gate the ex- ...
Learning Symbolic Models of Stochastic Domains
Learning Symbolic Models of Stochastic Domains

Different roles and mutual dependencies of data
Different roles and mutual dependencies of data

... examples of this trend. Recent methodologies for knowledge acquisition and development of knowledge-based systems also follow this general path, by turning attention to methods and tools that relate knowledge-based system components to other parts of an integrated system [53, 71]. In order to devel ...
Linking Objects to Actions: Encoding of Target Object and Grasping
Linking Objects to Actions: Encoding of Target Object and Grasping

Hold your horses: A dynamic computational role
Hold your horses: A dynamic computational role

... 2 See Frank and O’Reilly (2006) for more biological justification, including discussion on how DA dips can be effective learning signals despite the already low tonic firing rates of DA neurons. ...
Fuzzy Information Approaches to Equipment Condition Monitoring and Diagnosis
Fuzzy Information Approaches to Equipment Condition Monitoring and Diagnosis

Structure-function relationship in hierarchical model of brain networks
Structure-function relationship in hierarchical model of brain networks

... The idea to use well-known cortical networks in the modelling of the neural dynamics and investigating its relation to the underlying topology has already been considered in the literature. One of the first models was proposed by Kötter and Sommer [69]. The dynamics of areas in a cat cortical netwo ...
Relational Dynamic Bayesian Networks
Relational Dynamic Bayesian Networks

... AI Access Foundation. All rights reserved. ...
Inferring preferred extensions by Pstable semantics
Inferring preferred extensions by Pstable semantics

Viewpoint - Columbia University
Viewpoint - Columbia University

... explains why even neurons that are not part of the same local network should have a common crossing time. But first, we examine the data for evidence of this one-dimensional dynamics. Figure 2. Concept of One-Dimensional Dynamics An example of firing-rate space for N = 2 neurons. Visual (V), delay ( ...
A neuronal network model of primary visual cortex explains spatial
A neuronal network model of primary visual cortex explains spatial

Modelling Neuronal Mechanisms of the Processing of Tones and System
Modelling Neuronal Mechanisms of the Processing of Tones and System

... Wang 2010) and using models (Loebel, Nelken & Tsodyks 2007, de la Rocha, Marchetti, Schiff & Reyes 2008). In the present work we intend to shed light on this issue, and we do so by modelling in detail the (putative) neuronal correlates, at the level of the A1 circuit, of a well-known phenomenon in p ...
Down - 서울대 : Biointelligence lab
Down - 서울대 : Biointelligence lab

... The firing time of the IF-neuron is mainly determined by the average firing input current Measure this statement using cross-correlation function C (n)  s pre (t ) s post t  nt   s pre s post Fig. 4.13 Average cross-correlation function between pre-synaptic Poisson spike trains and the postsyn ...
Predominance of Movement Speed Over Direction in Neuronal
Predominance of Movement Speed Over Direction in Neuronal

... to motor-cortical activity have been previously studied using voluntary movement paradigms that broadly fall into 2 categories: (1) “event-related” tasks (Pfurtscheller and Lopes da Silva 1999), where subjects make repetitive, short-duration movements of up to a few seconds, for example, after a “go ...
Neural Networks
Neural Networks

... non-commercial purposes. It was originally designed for high performance simulations with lots and lots of neural networks (even large ones) being trained simultaneously. Recently, I decided to give it away as a professional reference implementation that covers network aspects handled within this wo ...
[pdf]
[pdf]

... execution status from within the program are vital for the experience acquisition in RoLL. Because of the RPL task network and the information kept therein, the experience acquisition can take place completely independent from the rest of the program. With the concept of fluents changes in state var ...
final scientific program
final scientific program

Connectionism and Information Processing Abstractions
Connectionism and Information Processing Abstractions

A Cholinergic Mechanism for Reward Timing within Primary Visual Cortex Please share
A Cholinergic Mechanism for Reward Timing within Primary Visual Cortex Please share

... two visual cues (100 ms, full-field retinal cue presented to either the left or right eye through removable, head-mounted goggles), and lick on a spout to receive delayed water reward (Fig. 1A). Each cue was experimentally associated with a discrete number of licks necessary to release a drop of wat ...
Introduction to Artificial Neural Networks (ANNs)
Introduction to Artificial Neural Networks (ANNs)

18 Coordination in Behavior and Cognition
18 Coordination in Behavior and Cognition

... modulation, which shows the breadth of its range of potential application. Our discussions, however, also reected somewhat different views on the degree to which coordinating interactions can change the local “meaning” or representational contents. The Coherent Infomax Theory suggests that coordina ...
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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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