Ángel Poc López

Zaragoza, Aragón, España Información de contacto
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PhD student in computational neuroscience and machine learning at BCAM and…

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  • BCAM - Basque Center for Applied Mathematics

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Publicaciones

  • Knitting a Markov blanket is hard when you are out-of-equilibrium: two examples in canonical nonequilibrium models

    Accepted in International Workshop on Active Inference. arXiv (Pre-print).

    Bayesian theories of biological and brain function speculate that Markov blankets (a conditional independence separating a system from external states) play a key role for facilitating inference-like behaviour in living systems. Although it has been suggested that Markov blankets are commonplace in sparsely connected, nonequilibrium complex systems, this has not been studied in detail. Here, we show in two different examples (a pair of coupled Lorenz systems and a nonequilibrium Ising model)…

    Bayesian theories of biological and brain function speculate that Markov blankets (a conditional independence separating a system from external states) play a key role for facilitating inference-like behaviour in living systems. Although it has been suggested that Markov blankets are commonplace in sparsely connected, nonequilibrium complex systems, this has not been studied in detail. Here, we show in two different examples (a pair of coupled Lorenz systems and a nonequilibrium Ising model) that sparse connectivity does not guarantee Markov blankets in the steady-state density of nonequilibrium systems. Conversely, in the nonequilibrium Ising model explored, the more distant from equilibrium the system appears to be correlated with the distance from displaying a Markov blanket. These result suggests that further assumptions might be needed in order to assume the presence of Markov blankets in the kind of nonequilibrium processes describing the activity of living systems.

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  • Inference in Neural Networks Using Conditional Mean-Field Methods

    Springer International Publishing

    We extend previous mean-field approaches for non-equilibrium neural network models to estimate correlations in the system. This offers a powerful tool for approximating the system dynamics, as well as a fast method for inferring network parameters from observations. We develop our method for the asymmetric kinetic Ising model and test its performance on 1) synthetic data generated by an asymmetric Sherrington Kirkpatrick model and 2) recordings of in vitro neuron spiking activity from the mouse…

    We extend previous mean-field approaches for non-equilibrium neural network models to estimate correlations in the system. This offers a powerful tool for approximating the system dynamics, as well as a fast method for inferring network parameters from observations. We develop our method for the asymmetric kinetic Ising model and test its performance on 1) synthetic data generated by an asymmetric Sherrington Kirkpatrick model and 2) recordings of in vitro neuron spiking activity from the mouse somatosensory cortex. We find that our mean-field method outperforms previous ones in estimating networks correlations and successfully reconstructs network dynamics from data near a phase transition showing large fluctuations.

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