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Bridging Scales from Neural Activity to Learning Machines

The past few years have seen a rapid convergence of neuroscience and computer science. On the one hand, neuroscience increasingly profits from digitalization. Neuroscientists now have access to reproducible toolchains, opening up new opportunities for every neuroscience student. On the other hand, artificial intelligence is advancing at a breathtaking pace, and recent neuroscientific insights are continuously flowing into the field.

This coordinated sequence of three workshops covers all levels from the detailed analysis of neuronal activity over the construction and simulation of full-scale models to the application of abstract learning machines-

In addition, with the completion of the following 8-hour workshop pathway, you will receive a training certificate:

Course 1: Investigating neuronal interactions in the brain

Tuesday April 9th, 13:00 – 16:00 pm. 

Presented by Sonja Grün and Jonas Orbeste – Frielinghaus

In this workshop, we introduce how the activity of many individual neurons from the cortex can be recorded and how this data can be analyzed. The cortex is the part of the brain where higher-order brain functions are computed, e.g., language and cognition. It is composed of a high density of neurons (ca. 40.000 neurons per mm3 ), of which each projects to about 10.000 other neurons, and each gets inputs from about the same number of neurons. Thus, the cortex is a highly connected network, and assemblies of interacting neurons are assumed to perform computation. Interactions (and their dynamics) are investigated with correlation analyses of the neuronal activities. In exercises, you may work with Python notebooks using different analysis methods supported by the community code Elephant, which could also be applied to data from network simulations. 

Course 2: Simulated dynamics of spiking full-scale network models  

Tuesday April 9th, 16:00 – 17:00 pm and Wednesday, April 10th, 13:00 – 14:00 pm.

Presented by Johanna Senk

The second part of the workshop gives an introduction and demonstration into modeling of the dynamics and plasticity of spiking neuronal networks. The tutorial explains graphical as well as programmatic approaches on the basis of the simulation code NEST. We emphasize that full-scale models representing all the neurons and all the synapses of a circuit are within reach, removing the uncertainties of downscaling. Examples and exercises make use of the graphical user interface NEST Desktop and Jupyter notebooks for PyNEST code.

Course 3: Modelling neuronal learning

Tuesday, April 9th, 14:00 – 17:00 pm. 

Presented by Eilif Muller et al.

TA: Tugce Gurbuz

The final part of the pathway will go into deep learning and its limitations, using hands-on tools. More information coming soon.