Biological neurons that show a locally tuned response to input may arise from the network topology of interneurons in the system. By considering such a subnetwork, a learning algorithm is developed for the online learning of the centre, width and shape of locally tuned response functions. The response function for each input is trained independently, resulting in a very good fit for the presented data. Two example networks utilising these neurons were considered. The first was a completely supervised network while the second utilised a Kohonen-like training scheme for the hidden layer. The adaptive response function neurons (ARFNs) were able to achieve excellent class separation while maintaining good generalisation with relatively few neur...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
Kühn S, Beyn W-J, Cruse H. Modelling memory functions with recurrent neural networks consisting of i...
Neuroevolution methods evolve the weights of a neural network, and in some cases the topology, but l...
Biological neurons that show a locally tuned response to input may arise from the network topology o...
Artificial neural network learning is typically accomplished via adaptation between neurons. This pa...
This paper presents a new artificial neuron model capable of learning its receptive field in the top...
<p>(<b>A</b>) Weight matrix of 117 excitatory neurons in a WTA network. After learning the network e...
We introduce a new paradigm of neural networks where neurons autonomously search for the best recipr...
Network training algorithms have heavily concentrated on the learning of connection weights. Little ...
12&.. O•ITN-UW, IAVAILAINY STATIEMINT '121 oTI0mmUTM cmo' Approved for public release;...
In biological and artificial neural networks the response properties of a visual neuron are often de...
The Problem: How can a distributed system of independent processors, armed with local communication ...
Data mining techniques have become extremely important with the proliferation of data. One technique...
The brain uses spikes in neural circuits to perform many dynamical computations. The computations ar...
This network extends the training network in Fig 2, represented by components with the gray backgrou...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
Kühn S, Beyn W-J, Cruse H. Modelling memory functions with recurrent neural networks consisting of i...
Neuroevolution methods evolve the weights of a neural network, and in some cases the topology, but l...
Biological neurons that show a locally tuned response to input may arise from the network topology o...
Artificial neural network learning is typically accomplished via adaptation between neurons. This pa...
This paper presents a new artificial neuron model capable of learning its receptive field in the top...
<p>(<b>A</b>) Weight matrix of 117 excitatory neurons in a WTA network. After learning the network e...
We introduce a new paradigm of neural networks where neurons autonomously search for the best recipr...
Network training algorithms have heavily concentrated on the learning of connection weights. Little ...
12&.. O•ITN-UW, IAVAILAINY STATIEMINT '121 oTI0mmUTM cmo' Approved for public release;...
In biological and artificial neural networks the response properties of a visual neuron are often de...
The Problem: How can a distributed system of independent processors, armed with local communication ...
Data mining techniques have become extremely important with the proliferation of data. One technique...
The brain uses spikes in neural circuits to perform many dynamical computations. The computations ar...
This network extends the training network in Fig 2, represented by components with the gray backgrou...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
Kühn S, Beyn W-J, Cruse H. Modelling memory functions with recurrent neural networks consisting of i...
Neuroevolution methods evolve the weights of a neural network, and in some cases the topology, but l...