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

... experimental studies suggest, however, that cortical microcircuits are not well represented by ER models [1,2]. One major finding that supports this idea is the fact that the probability of a directed connection between a pair of neurons increases with the number of common neighbors they have [2]. I ...
Application of ART neural networks in Wireless sensor networks
Application of ART neural networks in Wireless sensor networks

... with ART neural networks. There are two basic techniques:  Fast learning ○ new values of W are assigned in at discreet moments in time and are determined by algebraic equations  Slow learning ○ values of W at given point in time are determined by values of continuous functions at that point and de ...
Neural Networks - Temple Fox MIS
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... can be used instead ...
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Biological Inspiration for Artificial Neural Networks
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... • But still, we have no idea how we ‘perform’ face detection, we are just good at it • Nowadays, it’s « easy » to gather a lot of data (internet, social networks, …), so we have a lot of training data available ...
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... • An input is fed into the network and the output is being calculated. • We compare the output of the network with the target output, and we get the error. • We want to minimize the error, so we greedily adjust the weights such that error for this particular input will go towards zero. • We do so us ...
A Neural Network Model for the Representation of Natural Language
A Neural Network Model for the Representation of Natural Language

... 1999), theories of linguistic analysis, and known variables drawn from the brain and cognitive sciences as well as previous neural network systems built for similar purposes. My basic hypothesis is that the association among concepts is primarily an expression of domain-general cognitive mechanisms ...
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Neural Networks: An Application Of Linear Algebra

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Perceptrons and Backpropagation
Perceptrons and Backpropagation

< 1 ... 71 72 73 74 75 76 >

Catastrophic interference



Catastrophic Interference, also known as catastrophic forgetting, is the tendency of a artificial neural network to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. These networks use computer simulations to try and model human behaviours, such as memory and learning. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ractcliff (1990). It is a radical manifestation of the ‘sensitivity-stability’ dilemma or the ‘stability-plasticity’ dilemma. Specifically, these problems refer to the issue of being able to make an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionst networks like the standard backpropagation network are very sensitive to new information and can generalize on new inputs. Backpropagation models can be considered good models of human memory insofar as they mirror the human ability to generalize but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is considered an issue when attempting to model human memory because, unlike these networks, humans typically do not show catastrophic forgetting. Thus, the issue of catastrophic interference must be eradicated from these backpropagation models in order to enhance the plausibility as models of human memory.
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