Slide 1
... Determine result of competition for influencing hippocampus between grid cells and more localized sensory input (e.g. vision, smell). Investigate the role of network topology and synaptic learning rules in ...
... Determine result of competition for influencing hippocampus between grid cells and more localized sensory input (e.g. vision, smell). Investigate the role of network topology and synaptic learning rules in ...
Graduiertenkolleg Adaptivity in Hybrid Cognitive Systems Artificial
... reasoning, search etc., but do have obvious problems in modeling lower cognitive abilities like motor control or image recognition. The situation is mirror-inverted in the case of neural networks, i.e. both approaches have complementary strengths and weaknesses. This obvious gap between the two type ...
... reasoning, search etc., but do have obvious problems in modeling lower cognitive abilities like motor control or image recognition. The situation is mirror-inverted in the case of neural networks, i.e. both approaches have complementary strengths and weaknesses. This obvious gap between the two type ...
Analysis of Complex Data with Applications to Biological Systems
... In this talk, I will discuss about past, current, and future research topics that characterize my research activity. First, I will talk about learning in “non-geometric spaces”. Typical learning problems are conceived on geometric domains, such as Euclidean spaces. However, many realworld applicatio ...
... In this talk, I will discuss about past, current, and future research topics that characterize my research activity. First, I will talk about learning in “non-geometric spaces”. Typical learning problems are conceived on geometric domains, such as Euclidean spaces. However, many realworld applicatio ...
Artificial Neural Networks
... The neuron sends out spikes of electrical activity through a long, thin strand known as an axon, which splits into thousands of branches. At the end of the branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity in the connecte ...
... The neuron sends out spikes of electrical activity through a long, thin strand known as an axon, which splits into thousands of branches. At the end of the branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity in the connecte ...
document
... An unsupervised learning system. Two layers of nodes: an input layer and a cluster (output) layer. Uses competitive learning: Every input is compared with the weight vectors of each node in the cluster node. The node which most closely matches the input, fires. This is the classification of the ...
... An unsupervised learning system. Two layers of nodes: an input layer and a cluster (output) layer. Uses competitive learning: Every input is compared with the weight vectors of each node in the cluster node. The node which most closely matches the input, fires. This is the classification of the ...
... designed to collaborate in the development of pre-diction technics to find information that allows to study the behavior of the natural phenomena, such as the solar insolation. This paper presents the ob-tained results when using the DBN architecture for solar insolation prediction, simulated throug ...
Physical Neural Networks Jonathan Lamont November 16, 2015
... systems to adapt at all scales • Each adaptation must reduce to memory-processor communication as state variables are modified – Energy consumed in moving this information grows linearly with number of state variables that must be ...
... systems to adapt at all scales • Each adaptation must reduce to memory-processor communication as state variables are modified – Energy consumed in moving this information grows linearly with number of state variables that must be ...
Supervised learning
... why a neural network is good at classification. The action of a single neuron is quite easy ; only the cooperation of a great number of neurons can make complex tasks. ...
... why a neural network is good at classification. The action of a single neuron is quite easy ; only the cooperation of a great number of neurons can make complex tasks. ...
Extracting Single-trialViews of Brain Activity
... monitor simultaneously. To make further scientific progress with the ever-growing volume of neural data being collected, new analytical methods are needed that can leverage the simultaneous recording of large populations of neurons. In this talk, I will take a step in this direction by describing ho ...
... monitor simultaneously. To make further scientific progress with the ever-growing volume of neural data being collected, new analytical methods are needed that can leverage the simultaneous recording of large populations of neurons. In this talk, I will take a step in this direction by describing ho ...
