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Two neural networks that are trained on their mutual output synchronize to an identical time dependant weight vector. This novel phenomenon can be used for creation of a secure cryptographic secret-key using a public channel. Several models for this cryptographic system have been suggested, and have been tested for their security under different… (More)

- Andreas Ruttor
- 2006

Neural networks can synchronize by learning from each other. For that purpose they receive common inputs and exchange their outputs. Adjusting discrete weights according to a suitable learning rule then leads to full synchronization in a finite number of steps. It is also possible to train additional neural networks by using the inputs and outputs generated… (More)

- Andreas Ruttor, Wolfgang Kinzel, Lanir Shacham, Ido Kanter
- Physical review. E, Statistical, nonlinear, and…
- 2004

Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which… (More)

- Guido Sanguinetti, Andreas Ruttor, Manfred Opper, Cédric Archambeau
- Bioinformatics
- 2009

MOTIVATION
Stress response in cells is often mediated by quick activation of transcription factors (TFs). Given the difficulty in experimentally assaying TF activities, several statistical approaches have been proposed to infer them from microarray time courses. However, these approaches often rely on prior assumptions which rule out the rapid responses… (More)

- Andreas Ruttor, Wolfgang Kinzel, Rivka Naeh, Ido Kanter
- Physical review. E, Statistical, nonlinear, and…
- 2006

Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the… (More)

- Andreas Ruttor, Wolfgang Kinzel, Ido Kanter
- Physical review. E, Statistical, nonlinear, and…
- 2007

Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating… (More)

- Manfred Opper, Andreas Ruttor, Guido Sanguinetti
- NIPS
- 2010

We present a novel approach to inference in conditionally Gaussian continuous time stochastic processes, where the latent process is a Markovian jump process. We first consider the case of jump-diffusion processes, where the drift of a linear stochastic differential equation can jump at arbitrary time points. We derive partial differential equations for… (More)

- Andreas Ruttor, Manfred Opper
- Physical review letters
- 2009

We address the problem of estimating unknown model parameters and state variables in stochastic reaction processes when only sparse and noisy measurements are available. Using an asymptotic system size expansion for the backward equation, we derive an efficient approximation for this problem. We demonstrate the validity of our approach on model systems and… (More)

Neural cryptography is based on synchronization of tree parity machines by mutual learning. We extend previous key-exchange protocols by replacing random inputs with queries depending on the current state of the neural networks. The probability of a successful attack is calculated for different model parameters using numerical simulations. The results show… (More)

We consider the problem of Bayesian inference for continuous-time multi-stable stochastic systems which can change both their diffusion and drift parameters at discrete times. We propose exact inference and sampling methodologies for two specific cases where the discontinuous dynamics is given by a Poisson process and a two-state Markovian switch. We test… (More)