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- Gert Cauwenberghs, Tomaso A. Poggio
- NIPS
- 2000

An on-line recursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the KuhnTucker conditions on all previously seen training data, in a number of steps each computed analytically. The incremental procedure is reversible, and decremental “unlearning” offers an efficient method to exactly… (More)

The objective of machine learning is to identify a model that yields good generalization performance. This involves repeatedly selecting a hypothesis class, searching the hypothesis class by minimizing a given objective function over the model’s parameter space, and evaluating the generalization performance of the resulting model. This search can be… (More)

- R. Jacob Vogelstein, Udayan Mallik, Joshua T. Vogelstein, Gert Cauwenberghs
- IEEE Transactions on Neural Networks
- 2007

A mixed-signal very large scale integration (VLSI) chip for large scale emulation of spiking neural networks is presented. The chip contains 2400 silicon neurons with fully programmable and reconfigurable synaptic connectivity. Each neuron implements a discrete-time model of a single-compartment cell. The model allows for analog membrane dynamics and an… (More)

- Giacomo Indiveri, Bernabé Linares-Barranco, +17 authors Kwabena Boahen
- Front. Neurosci
- 2011

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this… (More)

- Yu M. Chi, Tzyy-Ping Jung, Gert Cauwenberghs
- IEEE reviews in biomedical engineering
- 2010

Recent demand and interest in wireless, mobile-based healthcare has driven significant interest towards developing alternative biopotential electrodes for patient physiological monitoring. The conventional wet adhesive Ag/AgCl electrodes used almost universally in clinical applications today provide an excellent signal but are cumbersome and irritating for… (More)

- Gert Cauwenberghs
- NIPS
- 1992

A parallel stochastic algorithm is investigated for error-descent learning and optimization in deterministic networks of arbitrary topology. No explicit information about internal network structure is needed. The method is based on the model-free distributed learning mechanism of Dembo and Kailath. A modified parameter update rule is proposed by which each… (More)

- Christian M. Sauer, Milutin Stanacevic, Gert Cauwenberghs, Nitish V. Thakor
- IEEE Trans. on Circuits and Systems
- 2005

Implanted sensors offer many advantages to study and monitor the human body. Wires or batteries often compromise their usefulness. We describe a telemetry chip that by inductive coupling supplies power to and transmits digital data from an implantable sensor. The same two coils are used to transmit both power and data. The chip fabricated in 0.5μm CMOS… (More)

- Mohsen Mollazadeh, Kartikeya Murari, Gert Cauwenberghs, Nitish V. Thakor
- IEEE Trans. Biomed. Circuits and Systems
- 2009

Electrical activity in the brain spans a wide range of spatial and temporal scales, requiring simultaneous recording of multiple modalities of neurophysiological signals in order to capture various aspects of brain state dynamics. Here, we present a 16-channel neural interface integrated circuit fabricated in a 0.5 mum 3M2P CMOS process for selective… (More)

- Gert Cauwenberghs
- IEEE Trans. Neural Networks
- 1996

Real-time algorithms for gradient descent supervised learning in recurrent dynamical neural networks fail to support scalable VLSI implementation, due to their complexity which grows sharply with the network dimension. We present an alternative implementation in analog VLSI, which employs a stochastic perturbation algorithm to observe the gradient of the… (More)

- Roman Genov, Gert Cauwenberghs
- SVM
- 2002

Detection of complex objects in streaming video poses two fundamental challenges: training from sparse data with proper generalization across variations in the object class and the environment; and the computational power required of the trained classifier running real-time. The Kerneltron supports the generalization performance of a support vector machine… (More)