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- E L Bienenstock, L N Cooper, P W Munro
- The Journal of neuroscience : the official…
- 1982

The development of stimulus selectivity in the primary sensory cortex of higher vertebrates is considered in a general mathematical framework. A synaptic evolution scheme of a new kind is proposed in which incoming patterns rather than converging afferents compete. The change in the efficacy of a given synapse depends not only on instantaneous pre- and… (More)

- Stuart Geman, Elie Bienenstock, René Doursat
- Neural Computation
- 1992

Feedforward neural networks trained by error backpropagation are examples of nonparametric regression estimators. We present a tutorial on nonparametric inference and its relation to neural networks, and we use the statistical viewpoint to highlight strengths and weaknesses of neural models. We illustrate the main points with some recognition experiments… (More)

- Wei Wu, Yun Gao, Elie Bienenstock, John P. Donoghue, Michael J. Black
- Neural Computation
- 2006

Effective neural motor prostheses require a method for decoding neural activity representing desired movement. In particular, the accurate reconstruction of a continuous motion signal is necessary for the control of devices such as computer cursors, robots, or a patient's own paralyzed limbs. For such applications, we developed a real-time system that uses… (More)

- Wei Wu, Michael J. Black, David Mumford, Yun Gao, Elie Bienenstock, John P. Donoghue
- IEEE Trans. Biomed. Engineering
- 2004

We present a switching Kalman filter model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A "hidden state" models the probability of each mixture component and evolves over time in a… (More)

- Yariv Maron, Michael Lamar, Elie Bienenstock
- NIPS
- 2010

Motivated by an application to unsupervised part-of-speech tagging, we present an algorithm for the Euclidean embedding of large sets of categorical data based on co-occurrence statistics. We use the CODE model of Globerson et al. but constrain the embedding to lie on a hig h-dimensional unit sphere. This constraint allows for efficient optimization, even… (More)

- Wei Wu, Michael J. Black, Yun Gao, Elie Bienenstock, Mijail Serruya, A. Shaikhouni +1 other
- NIPS
- 2002

The direct neural control of external devices such as computer displays or prosthetic limbs requires the accurate decoding of neural activity representing continuous movement. We develop a real-time control system using the spiking activity of approximately 40 neurons recorded with an electrode array implanted in the arm area of primary motor cortex. In… (More)

- Michael Lamar, Yariv Maron, Mark Johnson, Elie Bienenstock
- ACL
- 2010

We revisit the algorithm of Schütze (1995) for unsupervised part-of-speech tagging. The algorithm uses reduced-rank singular value decomposition followed by clustering to extract latent features from context distributions. As implemented here, it achieves state-of-the-art tagging accuracy at considerably less cost than more recent methods. It can also… (More)

- Y Frégnac, D Shulz, S Thorpe, E Bienenstock
- Nature
- 1988

Neuronal activity plays an important role in the development of the visual pathway. The modulation of synaptic transmission by temporal correlation between pre- and postsynaptic activity is one mechanism which could underly visual cortical plasticity. We report here that functional changes in single neurons of area 17, analogous to those known to take place… (More)

Statistical learning and probabilistic inference techniques are used to infer the hand position of a subject from multi-electrode recordings of neu-ral activity in motor cortex. First, an array of electrodes provides training data of neural firing conditioned on hand kinematics. We learn a non-parametric representation of this firing activity using a… (More)

– Neocortical connectivity displays striking regularities that self-organize via epigenetic interactions with activity. We construe this self-structuration as a process of spatiotemporal pattern formation in a simple neural network model. Starting from random connections, ordered " synfire-chain " structures and wave-like correlations emerge simultaneously… (More)