Dagstuhl-Seminar
... obtained from a local dimensionality reduction, and conversely, to infer the local dimensionilty reduction from the clustering. This model can also be thought of as a reduced parameter method of fitting a mixture of Gaussians in high dimensions. The model can be generalised to deal with time series, ...
... obtained from a local dimensionality reduction, and conversely, to infer the local dimensionilty reduction from the clustering. This model can also be thought of as a reduced parameter method of fitting a mixture of Gaussians in high dimensions. The model can be generalised to deal with time series, ...
Neural characterization in partially observed populations of spiking
... been successfully applied to neurons in the early sensory pathway, they have fared less well capturing the response properties of neurons in deeper brain areas, owing in part to the fact that they do not take into account multiple stages of processing. Here we introduce a new twist on the point-proc ...
... been successfully applied to neurons in the early sensory pathway, they have fared less well capturing the response properties of neurons in deeper brain areas, owing in part to the fact that they do not take into account multiple stages of processing. Here we introduce a new twist on the point-proc ...
CS 561a: Introduction to Artificial Intelligence
... 1950s: beginning of computer vision Aim: give to machines same or better vision capability as ours Drive: AI, robotics applications and factory automation Initially: passive, feedforward, layered and hierarchical process that was just going to provide input to higher reasoning processes (from AI) Bu ...
... 1950s: beginning of computer vision Aim: give to machines same or better vision capability as ours Drive: AI, robotics applications and factory automation Initially: passive, feedforward, layered and hierarchical process that was just going to provide input to higher reasoning processes (from AI) Bu ...
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... Theoretical physicist Michio Kaku has pointed out that there are so many people who have worked so hard for so long, the neuroscientists have hardly come up with any theory about the design principles of intelligence (Kaku, 2014). Not necessarily agreeing with his conclusion, but I think that Dr. Ka ...
... Theoretical physicist Michio Kaku has pointed out that there are so many people who have worked so hard for so long, the neuroscientists have hardly come up with any theory about the design principles of intelligence (Kaku, 2014). Not necessarily agreeing with his conclusion, but I think that Dr. Ka ...
State-dependent computations - Frankfurt Institute for Advanced
... incoming stimuli and the internal state of a neural network will shape the population response in a complex fashion. However, defining the internal state of a neural network is not straightforward, and it will thus be useful to distinguish between two components, which we will refer to as the active ...
... incoming stimuli and the internal state of a neural network will shape the population response in a complex fashion. However, defining the internal state of a neural network is not straightforward, and it will thus be useful to distinguish between two components, which we will refer to as the active ...
Mechanisms of neural specification from embryonic stem cells
... neuronal networks Perhaps the most surprising lesson learned from recent ES cell models is that at least some important components of complex features such as cytoarchitecture and hodological properties can be specified in vitro. When ES cells are cultured as bowls of cells differentiating into cort ...
... neuronal networks Perhaps the most surprising lesson learned from recent ES cell models is that at least some important components of complex features such as cytoarchitecture and hodological properties can be specified in vitro. When ES cells are cultured as bowls of cells differentiating into cort ...
Hybrid Computing Algorithm in Representing Solid Model
... line drawing that represent solid model on graph paper. Second, the 2D line drawing is assumed to represent a valid solid model where all unwanted junctions or lines have been removed and there are no unconnected junctions or lines. Third, the solid model is assumed as a 2D line drawing with all inf ...
... line drawing that represent solid model on graph paper. Second, the 2D line drawing is assumed to represent a valid solid model where all unwanted junctions or lines have been removed and there are no unconnected junctions or lines. Third, the solid model is assumed as a 2D line drawing with all inf ...
Computational physics: Neural networks
... This reader introduces stochastic neural networks, a simple paradigm for distributed computing in the brain. The neuron is the central computing element of the brain which performs a non-linear input to output mapping between its synaptic inputs and its spiky output. The neurons are connected by syn ...
... This reader introduces stochastic neural networks, a simple paradigm for distributed computing in the brain. The neuron is the central computing element of the brain which performs a non-linear input to output mapping between its synaptic inputs and its spiky output. The neurons are connected by syn ...
10_Solla_Sara_10_CTP0608
... Many complex networks have a smallworld topology characterized by dense local clustering or cliquishness of connections between neighboring nodes yet a short path length between any (distant) pair of nodes due to the existence of relatively few long-range connections. This is an attractive model for ...
... Many complex networks have a smallworld topology characterized by dense local clustering or cliquishness of connections between neighboring nodes yet a short path length between any (distant) pair of nodes due to the existence of relatively few long-range connections. This is an attractive model for ...
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.