← Back to papers
2007 NIPS NeurIPS 2007

Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons

Abstract

A non–linear dynamic system is called contracting if initial conditions are for- gotten exponentially fast, so that all trajectories converge to a single trajectory. We use contraction theory to derive an upper bound for the strength of recurrent connections that guarantees contraction for complex neural networks. Specifi- cally, we apply this theory to a special class of recurrent networks, often called Cooperative Competitive Networks (CCNs), which are an abstract representation of the cooperative-competitive connectivity observed in cortex. This specific type of network is believed to play a major role in shaping cortical responses and se- lecting the relevant signal among distractors and noise. In this paper, we analyze contraction of combined CCNs of linear threshold units and verify the results of our analysis in a hybrid analog/digital VLSI CCN comprising spiking neurons and dynamic synapses.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning
🧭 Keyword Pioneer — contracting neural networks
🐣 Hot Topic Early Bird — neural network
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
📈 Trend Setter — Deep Learning

Authors

Emre Neftci , Elisabetta Chicca , Giacomo Indiveri , Jean-jeacques Slotine , Rodney J. Douglas

Topics

Machine Learning > Optimization & Theory > Theory Deep Learning > Architectures > Neural Networks Machine Learning > Learning Types > Deep Learning Interdisciplinary > Science > Neuroscience

Keywords

dynamic synapses contracting neural networks recurrent connections contraction theory vlsi circuits cooperative competitive network spiking neuron spiking neural network dynamical system recurrent neural network recurrent network neural network
Download PDF

Related papers

Exponential Family Predictive Representations of State 2007
Privacy-Preserving Belief Propagation and Sampling 2007
Efficient Principled Learning of Thin Junction Trees 2007
How SVMs can estimate quantiles and the median 2007
Rapid Inference on a Novel AND/OR graph for Object Detection, Segmentation and Parsing 2007