Analog VLSI and neural systems
Analog VLSI and neural systems
Learning with temporal derivatives in pulse-coded neuronal systems
Advances in neural information processing systems 1
Performance of a stochastic learning microchip
Advances in neural information processing systems 1
Adaptive neural networks using MOS charge storage
Advances in neural information processing systems 1
Introduction to the theory of neural computation
Introduction to the theory of neural computation
VLSI implementation of neural networks
An introduction to neural and electronic networks
A hierarchical clustering network based on a model of olfactory processing
Analog Integrated Circuits and Signal Processing - Special issue on analog VLSI neural networks
Mixed analog/digital matrix-vector multiplier for neural network synapses
Analog Integrated Circuits and Signal Processing - Special issue: selected articles from the 1994 NORCHIP seminar
An analog feed-forward neural network with on-chip learning
Analog Integrated Circuits and Signal Processing - Special issue: selected articles from the 1994 NORCHIP seminar
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Introduction to Cognitive and Neural Modeling
Introduction to Cognitive and Neural Modeling
Silicon Implementation of Pulse Coded Neural Networks
Silicon Implementation of Pulse Coded Neural Networks
Analog VLSI System for Active Drag Reduction
IEEE Micro
Analog VLSI Implementation of Gradient Descent
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Teaching Pulsed Integrated Neural Systems: A Psychobiological Approach
MICRONEURO '96 Proceedings of the 5th International Conference on Microelectronics for Neural Networks and Fuzzy Systems
An analog VLSI recurrent neural network learning a continuous-time trajectory
IEEE Transactions on Neural Networks
Nonlinear backpropagation: doing backpropagation without derivatives of the activation function
IEEE Transactions on Neural Networks
Tolerance to analog hardware of on-chip learning in backpropagation networks
IEEE Transactions on Neural Networks
Switched-capacitor neuromorphs with wide-range variable dynamics
IEEE Transactions on Neural Networks
Unsupervised learning and self-organization in networks of spiking neurons
Self-Organizing neural networks
Modeling spiking neural networks
Theoretical Computer Science
| Hi-index | 0.00 |
Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the “biologically-inspired” approach, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue that useful conclusions as to the future of on-chip learning can be drawn from this work.