Day 9 - Ben Grewe and Pau Aceituno - Cortical neaning rules and dynamical states

 This morning at 930 we heard very interesting session on the beach from Ben Grewe and Pau Vicente, both from Zurich. The slowly increasing number of positive COVID cases forced cancellation of all indoor groupings, and so the beach event took place under brilliant blue sky and shimmering sea. Benni started off by making the bold statement that learning rules did not matter. What matters is what the cell wants to do, via a cost function that the cells try to optimize. In the end, via calcium second messengers, the cells ultimately seems to have two attractor states, resting and active, and cellular feedback mechanisms always make neurons move to these stable down and up states. After the coffee break (where participants were exhorted not to blow hot air at each other), Pau Aceituno took over. He looks at the problem in terms of Shannon information. The S sensor B brain and A action represent an agent (animal or artificial) that must process information. Just like in a Morse code where

Day 5 - Classification & Clustering

Chairs: Chiara and Germaine Speakers: Thomas Nowotny, Simon Thorpe (Single spike), Giacomo Vale, Enea  Self intros: Giacomo Vale, neuroprosthetics, 6ws?, Fields A, D Damien, memory technologies in neurosystems, 6ws?  Neuromorphic hardware is very very cool. A, B Fabian LeVarra, I am very happy to be here.  Moritz, member of scientific committee, It's ok to sometimes miss spikes. learning and network dynamics What is classification and clustering (Thomas Nowotny)? Shoot away - sorting - what is similar? - adding labels to your clusters? - feature extraction - dimensionality reduction - correlate and decorrelate - context understanding - use for control, unlocking phone - learning representations - images are not the only data available - language? - methods: deep learning, Hebbian plasticity, tracking objects, metrics, prediction, competition, temporal stability, generative models Sorting into a framework - make sense of some input - e.g. input: images, sense: labels (classification

Day 8 - Memory and Plasticity - Emre Neftci, Damien Querlioz, Elisa Vianello

 Sunday's day off was under sunny skies and was filled with an incredible variety of sporting activities including hiking, bike riding, sailing (including multiple capsizing and rescue of Matteo who did not properly set the Laser sailboat rudder) scuba diving, beach volleyball, tennis, and even interesting "Battle Line" card gaming after dinner.

Day 4 - Florian Engert, Georg Keller, Valerio Mante - Fish o-turn escape system and mouse escape pilotage, sensory motor prediction in cortex and cortical dynamics during and after decisions

 Day 4 of CCNW2022 After a rainy night and waking to wet  gray skies, we started off with Melika telling us about the important meetings in the afternoon about progress on the 4 themes expressed as interests by the participants. More self intros Emre Neftci - Aachen - 6 words "online in situ learning needs your help" B and C Eneo Ceolini  U Leiden "digital behavior reveals cognitive behavior"  B and C  Valerio Mante INI "computation through dynamics" B and C Florian: Fish o-turn escape system and mouse escape pilotage,  Florian started off with the aim of the morning to consider innate goals and when behavior is goal directed vs reflex. First Inborn goals, then learned goals. To get discussion going, Florian proposed a system, the "thermostat" as a basic system that all animals care about. Doesn't matter if artificial or natural or evolutionary but needs to work well and be implemented robustly to deal with delays and work with bang bang cont

Day 3 on Navigation: Barbara Webb, Julien Serres and Pavan Ramdya

The day started with an introduction from Florian Engert who posed the question " What problems are animal solving or evolved to solve?". Then he posed three specific questions that are interesting to ask: First: Identify what is the problem the animal is solving.   Second: how does it solve it? What is the nature of the input? What is the dynamics and what is the precise nature of the output? What is the algorithm through which the animal solve it?   Third: How is it implemented?   He mentioned that the day reovolves around these questions in the context of navigation. Naviagation in terms of Today is insect-heavy.     Baraba mentioned that you can never be insect heavy (she loves insect brain) :)   Although today's discussion is mostly about insects, Florian mentioned that he believes the same principles apply to all animals.     Barbara started by asking only students (and not faculty and navigation experts) to say what comes to their minds when they think about the na

Day 2 - Yves Fregnec and Michael Berry - Natural computtion

 After some early morning swims and breakfast under sunny and calm morning skies (but with the threat of rain for the next 2 days looming) we started off with some more self intros Florian "Fish feelings lovingly explained" but modified from 2018 Michael Schmucker "neuromorphic olfaction event based sensing XXX" Georg Keller "XXX" Yves Fregnec "Brains without synapses" (The six words are seriously mis-transcribed above) Florian Engert provocatively asserted that nearly all of so-called "Learning" is innate to the gene Then Yves started off. His job he started off was to focus on the relevance of biological computation to artificial computing. In 1956 there was a famous meeting where the Minsky asserted that "every aspect of thinking can be simulated by computers" and that a summer undergrad research project would be enough to conquer computer vision. XXX claimed that simple neurons that sum up input and threshold the resulting

Day 2 - Friedemann Zenke, Matthew Cook on Neural Computation

After the coffee break, Matthew Cook kicked off the 2nd part of the morning asking, "What is computation"? It is NOT the computer science view of algorithm with a halting condition. Is a bucket of water? Yes and no, in any case this argument about definitions is not fruitful but considering different ways to compute is useful. This generated an immense amount of discussion about the meaning of "computation" in the brain and in computer science. After this discussion was used up, Matt pointed out the continuing need (last made in his great 2019 talk at CCNW) for increasing the models of computation. There is always room to at least consider more models; most will not work for AI or not explain anything new, but there will occasionally be new insights from new ways of looking at computing. Among the models considered, Turing machines, cellular automata, neural networks, synchronous RTL logic circuits, various computer architectures, cortex. One definition of a computa