2024 CCNW Capocaccia Neuromorphic Workshop

This final morning started in the lecture room with presentations of the final results of projects at the workshop. Giacomo started things off by reminding people to put stuff on the CCNW cloud for posterity. Chiara reminded everyone to give credit later to the workshop and to all the partners in the projects (and it is good to write down their names now so no one gets left out). Andreas talked about the specifically funded hardware neuromorphic group at the Telluride neuromorphic workshop (NIC) this year, to think about applying to participate in 2025. Jamie Knight showed GeNN running a simulation of the ellipsoid body which was done by Gabriel and XXX and im plemented in preliminary form on their robot with its impressive dome sky polarization sensor. Next Gabriel (Gabi) and Thomas talked about SPIXER (spiking event on robot XXXX) implemented on the robot and last night very late ran experiments under starlight on the tennis court (fueled in part by beer). Thomas showed laptop and

Today's discussion with Catherine Schuman, Dániel Barabási and Sebastian Risi was about the process that gave rise to the neural mechanisms that our community is so fond of: evolution. The first speaker is Catherine Schuman . Catherine talked to us on Day 8 about her projects on optimizing robots with neuromorphic hardware. Today she will give us some more insights into how you can use evolutionary algorithms to solve these kinds of problems. She first drew a block diagram of the main components of evolutionary search: initialization, fitness, selection, and reproduction. (Top) Catherine's block diagram of an evolutionary process (Below) Classification of evolutionary algorithms You initialize a population of solutions, you evaluate them on some tasks to compute their fitness, then you use some criterion to select who will reproduce and, during reproduction, you may implement cross-over and mutations. Not all algorithms use all these components but these are all the components

  From our Sunday boat trip around Capocaccia. Photo credit: Steve Kalik After yesterday's tour to the development of brains, we turn back to the most classical approach of looking at brains after they have formed. What features does this final form have and how do they contribute to computation and intelligence?  The first speaker is Nuno Maçarico da Costa from the Allen Institute, who has already talked to us during a discussion group a couple of days ago about their work on collecting and analysing large amounts of anatomical and functional data of the mouse virtual cortex . These data do not show transcriptomes, but just connectomes. The sketch above shows a slice of cortex with the thalamic input coming to layer 4, and typical layer 5 "output" neuron. This time, he will describe insights he has drawn from these data about how different cell types connect to each other. He started by discussing connectivity patterns of inhibitory cells (he indicates them with circle

   An afternoon discussion group How do you build a brain? Usually in this workshop we talk about how brains work, but in today's discussion we will talk about how they construct themselves. The first speaker is Anthony Zador from Cold Spring Harbor Laboratory who has spent his career studying this question. Tony said that he often finds molecular biology a bit cluttered in its terminology: due to their naming practises, papers have the feel of an alphanumeric soup. He is instead interested in finding what makes brain development interesting. He started by drawing a DNA strand on the left and a weight matrix that represents the connectome of the agent at birth. This task is demanding from an information perspective: the DNA has 3 billion nucleotides (each one can take one of the four bases A,T, G, C) while the human genome has about 10^10 neurons. This means that the matrix requires way more bits to specify than the genome can hold. This gives rise to what Anthony calls the genomi