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Jean-jeacques Slotine

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3 papers

1 author row

NeurIPS Conference 2012 Conference Paper

Multiclass Learning with Simplex Coding

Youssef Mroueh
Tomaso Poggio
Lorenzo Rosasco
Jean-jeacques Slotine

In this paper we dicuss a novel framework for multiclass learning, defined by a suitable coding/decoding strategy, namely the simplex coding, that allows to generalize to multiple classes a relaxation approach commonly used in binary classification. In this framework a relaxation error analysis can be developed avoiding constraints on the considered hypotheses class. Moreover, we show that in this setting it is possible to derive the first provably consistent regularized methods with training/tuning complexity which is {\em independent} to the number of classes. Tools from convex analysis are introduced that can be used beyond the scope of this paper.

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NeurIPS Conference 2012 Conference Paper

Synchronization can Control Regularization in Neural Systems via Correlated Noise Processes

Jake Bouvrie
Jean-jeacques Slotine

To learn reliable rules that can generalize to novel situations, the brain must be capable of imposing some form of regularization. Here we suggest, through theoretical and computational arguments, that the combination of noise with synchronization provides a plausible mechanism for regularization in the nervous system. The functional role of regularization is considered in a general context in which coupled computational systems receive inputs corrupted by correlated noise. Noise on the inputs is shown to impose regularization, and when synchronization upstream induces time-varying correlations across noise variables, the degree of regularization can be calibrated over time. The resulting qualitative behavior matches experimental data from visual cortex.

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NeurIPS Conference 2007 Conference Paper

Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons

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

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.

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