Quantum work statistics, Loschmidt echo and information scrambling.

A universal relation is established between the quantum work probability distribution of an isolated driven quantum system and the Loschmidt echo dynamics of a two-mode squeezed state. When the initial density matrix is canonical, the Loschmidt echo of the purified double thermofield state provides a direct measure of information scrambling and can be related to the analytic continuation of the partition function. Information scrambling is then described by the quantum work statistics associated with the time-reversal operation on a single copy, associated with the sudden negation of the system Hamiltonian.

A model for altered neural network dynamics related to prehension movements in Parkinson disease.

In this paper, we present a neural network model of the interactions between cortex and the basal ganglia during prehensile movements. Computational neuroscience methods are used to explore the hypothesis that the altered kinematic patterns observed in Parkinson's disease patients performing prehensile movements is mainly due to an altered neuronal activity located in the networks of cholinergic (ACh) interneurons of the striatum. These striatal cells, under a strong influence of the dopaminergic system, significantly contribute to the neural processing within the striatum and in the cortico-basal ganglia loops. In order to test this hypothesis, a large-scale model of neural interactions in the basal ganglia has been integrated with previous models accounting for the cortical organization of goal directed reaching and grasping movements in normal and perturbed conditions. We carry out a discussion of the model hypothesis validation by providing a control engineering analysis and by comparing results of real experiments with our simulation results in conditions resembling these original experiments.

A modular neural network architecture for step-wise learning of grasping tasks.

This paper proposes a neural network architecture for learning of grasping tasks. The multineural network model presented in this work, allows acquisition of different neural representations of the grasping task through a successive learning over two stages in a strategy that uses already learned representations for the acquisition of the subsequent knowledge. Systematic computer simulations have been carried out in order to test learning and generalization capabilities of the system. The neural activity at different subparts of the artificial neural network during its performance phase, is compared to the activity of populations of real neurons in areas AIP and F5 of the distributed parieto-frontal biological neural network involved in visual guidance of grasping. A more biologically plausible development of the model presented here is also discussed. The proposed model can be also used as a high level controller for a robotic dextrous hand during learning and execution of grasping tasks.