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Complex Systems Neuroscience
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- 2. Reverse engineering complexsystems Observation of information exchange Disassembly Decompilation ?
- 3. Recording dimensions insystems neuroscience 32 channels Neuronexus commercial 128 channels Neuroseeker project IMEC design 384 channels Neuropixels project IMEC design 4 channels hand made 1344 channels Neuroseeker project IMEC design 1440 sites, 8 mm 100mm
- 4. Behavioural dimensions insystems neuroscience Behavioural variables = One Hypothesis driven Anne Marie Brady, Stan B. Floresco Behavioural variables = A few Hypothesis driven Ruth A. Wood et al. Behavioural variables = Many Exploratory driven Behavioural variables = Many Both hypothesis and exploratory driven Carsen Stringer et al.
- 5. Summary • High dimensionalrecordings: Design concepts and validation • High dimensional behavioural setup • Putting it all together • Speed correlations • Encoding salient features of a world model • Exploratory analysis
- 6. Summary • High dimensionalrecordings: Design concepts and validation • High dimensional behavioural setup • Putting it all together • Speed correlations • Encoding salient features of a world model • Exploratory analysis
- 7. Neuroseeker probe: Design •1344 ACTIVE electrodes • Scanning amplifier • 10 bit res. • 8mm x 100um x50um • Electrodes separated in 12 regions • 112 measuring • 8 reference electrodes • Each set to either LFP or AP band One region Reference 22 mm
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- 10. Neuroseeker probe: Lifespanand reuse
- 11. Summary • High dimensionalrecordings: Design concepts and validation • High dimensional behavioural setup • Putting it all together • Speed correlations • Encoding salient features of a world model • Exploratory analysis
- 12. Behavioural setup: Themachine
- 13. Behavioural setup: Theexperiment (training) Sound to reward availability Touching light to reward availability
- 14. Behavioural setup: Theexperiment (running) Touching light Moving light
- 15. Summary • High dimensionalrecordings: Design concepts and validation • High dimensional behavioural setup • Putting it all together • Speed correlations • Encoding salient features of a world model • Exploratory analysis
- 16. Speed correlations Mutual Information AlexanderKraskov et al. number of points with number of points with where Advantages • Corrects for bias due to sample size • Can be used with continuous data (does not require prior binning)
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- 20. Summary • High dimensionalrecordings: Design concepts and validation • High dimensional behavioural setup • Putting it all together • Speed correlations • Encoding salient features of a world model • Exploratory analysis
- 21. Encoding salient featuresof a world model Events definition
- 22. Encoding salient featuresof a world model Trial pokes
- 23. Encoding salient featuresof a world model Non trial pokes
- 24. Encoding salient featuresof a world model Positions of modulating neurons
- 25. Encoding salient featuresof a world model Activity modulating neurons in the hippocampus have place fields spanning the whole arena
- 26. Encoding salient featuresof a world model Normalised firing activity around salient events for all neurons
- 27. Encoding salient featuresof a world model Normalised firing activity around trial pokes events for all neurons over days
- 28. Summary • High dimensionalrecordings: Design concepts and validation • High dimensional behavioural setup • Putting it all together • Speed correlations • Encoding salient features of a world model • Exploratory analysis
- 29. t-Student nonlinear embedding(t-sne) Laurens Van Der Maaten, Geoffrey Hinton Exploratory analysis
- 30. Exploratory analysis t-sne ofspike rate vectors (top 40 primary components / explained Var = 0.22)
- 31. Exploratory analysis t-sne ofbehaviour video (top 100 primary components / explained Var = 0.98)
- 32. Exploratory analysis t-sne ofbehaviour video clustered with DBSCAN
- 33. Exploratory analysis Correspondence ofbehaviour clusters to the spike t-sne
- 34. Exploratory analysis Using NNsto predict the video: Architecture
- 35. Exploratory analysis Using NNsto predict the video: Results Image fed to the networks Target image to predict Image predicted by the Images Only network Image predicted by the Images and Spikes network
- 36. Acknowledgments ATLAS Neuroengineering Arno Aarts Tobias Holzhammer IMEC AndreiAlexandru Marco Ballini Nick Van Helleputte Chris van Hoof Carolina M. Lopez Silke Musa Bogdan Raducanu Jan Putzeys Shiwei Wang Marleen Welkenhuysen RADBOUD U. Francesco Battaglia Eric Maris Tim Schroeder Paul Tiesinga CNRL Francois David Luc J. Gentet ICNP Richárd Fiáth Domonkos Horváth Gergely Márton Domokos Meszéna Istvan Ulbert IMNS Srinjoy Mitra UPPSALA U. Hercules Neves U. OF PARMA Guy A. Orban IMTEK Frederick Pothof Patrick Ruther U. Of CAL. IRVINE Bruce L. McNaughton ESIN Wolf Singer Spyros Samothrakis Joana Neto Adam Kampff Atabak Dehban Lorenza Calcaterra Joana Nogueira Gonçalo Lopes Andre Marques-Smith
- 37. Joana Neto Adam Kampff Atabak Dehban Lorenza Calcaterra Joana Nogueira Gonçalo Lopes Acknowledgments Do not obstructknowledge. Adam R. Kampff 2018 Andre Marques-Smith
- 38. Acknowledgments Do not obstructknowledge. Adam R. Kampff 2018 The rule of reason: In order to learn you must desire to learn, and in so desiring not be satisfied with what you already incline to think. Corollary: Do not block the way of inquiry. Charles Sanders Peirce c.1890