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Through a blend of scientific and social elements, paired with ample time, the participants had the opportunity to shed formalities and extensively discuss. Make sure to watch the lectures on the YouTube and check out the photos on flickr. Over a lifetime the brain performs hundreds of thousands of individual cognitive acts of a variety of kinds, including the formation of new associations and other kinds of learning.

Each such act depends on past experience, and, in turn, can have long lasting effects on future behavior. It is difficult to reconcile such large scale capabilities Berlin orientals with the known resource constraints on cortex, such as low connectivity.

Here we shall describe an approach to this problem, in terms of concrete functions, representations, and algorithms, that seeks to explain these phenomena in terms that are faithful to the basic quantitative resources available.

Until recently an algorithmic understanding of cognition has been regarded as an overambitious goal for experimental neuroscience.

As we Heideberg explain, with current experimental techniques this view is no longer justified, and we should expect algorithmic theories to be experimentally testable, and tested. Download link to the slides of Valiant's lecture. In mammals, space hardd mapped by specialized position-coding cell types in entorhinal cortex and hippocampus, including entorhinal grid cells, which are active only when animals are at locations that tile environments in a periodic hexagonal pattern.

I will first show how space-coding neurons in the medial entorhinal cortex MEC collectively form a low-dimensional representation that persists across behavioral tasks and activity states. Escorts east Erkrath

This low-dimensionality points to network architecture as harr determinant of firing patterns and continuous attractor models have been proposed to account for the dynamics.

Current models do not easily account for the representation of the unidirectional flow of experience. In the second part of my talk, I will thus ask how entorhinal networks are organized in time.

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Which trajectories in high-dimensional state space do cell ensembles take during experience? To determine how activity is self-organized in the MEC Heirelberg, we tested mice in a spontaneous locomotion task under sensory-deprived conditions, when activity is determined primarily by the intrinsic structure of the network.

Mice were head-fixed and ran on a spherical cylinder in darkness. Using 2-photon calcium imaging, we monitored the activity of several hundreds of MEC layer-2 neurons.

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The Mathematics of the Heart Beat. Boundaries and Risks. Download link to the slides of Tarjan's lecture. It is difficult to reconcile such large scale capabilities quantitatively with the known resource constraints on cortex, such as low connectivity.

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