Hello and welcome to this lab course! You will learn the basic tools for running experiments on the BrainScaleS-2 platform. We invite you to read the following material before you start the lab course.
.. toctree:: :maxdepth: 2 :titlesonly: fp_biological-background fp_neuromorphic-computing fp_brainscales fp_pynn_introduction fp_lui fp_singleNeuron fp_synfireChain fp_sudoku
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Optional experiments
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.. toctree::
:maxdepth: 0
:titlesonly:
fp_calibration.rst
fp_synapticInput.rst
fp_adex_complex_dynamics.rst
fp_multicompartment.rst
fp_superspike.rst
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Optional experiments
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There are further experiments that are not part of the usual course, but can be both very instructive and helpful in case you want to do a long/short report.
They include working on the calibration, studying the synaptic input, more complex neuron models or training neural networks.
If you are interested, have a look online.
.. only:: not html References ~~~~~~~~~~
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References
If you are curious and want to learn more about the kind of work that has been done with the system, here are a few references that you can check out
- The BrainScaleS-2 Accelerated Neuromorphic System With Hybrid Plasticity
- Surrogate gradients for analog neuromorphic computing
- Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
- hxtorch: PyTorch for BrainScaleS-2 – Perceptrons on Analog Neuromorphic Hardware
- Control of criticality and computation in spiking neuromorphic networks with plasticity
- Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
- Fast and energy-efficient neuromorphic deep learning with first-spike times
- Inference with Artificial Neural Networks on Analog Neuromorphic Hardware
- Spiking neuromorphic chip learns entangled quantum states
- Structural plasticity on an accelerated analog neuromorphic hardware system
- Emulating dendritic computing paradigms on analog neuromorphic hardware