Mathematical Modeling of Neurons and Neural Networks Fall 2005 Math 8540
Mathematical Modeling of Neurons and Neural Networks Fall 2005 Math 8540

Nervous System
Nervous System

Statistical models of network connectivity in cortical microcircuits
Statistical models of network connectivity in cortical microcircuits

Katie Newhall Synchrony in stochastic pulse-coupled neuronal network models
Katie Newhall Synchrony in stochastic pulse-coupled neuronal network models

Chapter 1
Chapter 1

... • Computer Scientists – artificial intelligence, pattern recognition • Mathematicians – modelling tool when explicit relationships are unknown ...
Brain-Computer Interface
Brain-Computer Interface

intelligence artificielle
intelligence artificielle

... V 3.19 ...
PsychSim 5 neural messages
PsychSim 5 neural messages

... Section: ________________________ Date: __________________________________________ This activity explains the way that neurons communicate with each other. Neuron Parts Match the part of the neuron identified with its description: o ___ Axon ...
Activity of Spiking Neurons Stimulated by External Signals of
Activity of Spiking Neurons Stimulated by External Signals of

PsychSim - Stamford High School
PsychSim - Stamford High School

Module Worksheet - Germantown School District
Module Worksheet - Germantown School District

... Name: ...
PsychSim 5: NEURAL MESSAGES Name: Section: Date: ______
PsychSim 5: NEURAL MESSAGES Name: Section: Date: ______

... Date: __________________________________________ This activity explains the way that neurons communicate with each other. Neuron Parts Match the part of the neuron identified with its description: o ...
Copulae and network modeling
Copulae and network modeling

... Mathematical models for neuron activity are an important tool to increase our comprehension of neural code. Between single neuron models Leaky Integrate and Fire ones are particularly popular. This fact is due to two main features: they can fit a variety of experimental data and they are mathematica ...

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.