The physical implementation of today's technologies (very much including neuromorphic electronics) depends almost entirely on external (to the instantiated object) factories. By contrast, biological systems construct, repair, and evolve, themselves.
This discussion will consider issues such as:
Logistics (Giacomo, Committe, Organizers)
History (Rodney, Tobi, Andre, Giacomo)
Map of Interests (all):
1. [BBICS] Building Brain-Inspired Computing Systems (20)
2. [GBARS] Generating behavior in autonomous robotic systems (18)
3. [UNCBB] Understanding Natural Computation by Building (17)
4. [DNP&A] Develop new Products and Applications (14)
5. [DSCCS] Developmental Self-Constructing Computing …
We have a fully implemented baseline pipeline for Human Pose Estimation with Event Cameras in Real time that, then, feeds into a full body Fruit Ninja application. The system is ANN based and implemented modularly. A webpage will be made available shortly. If you would like to try seeing …
The goal of this workshop is to investigate how to make the best use of the unique properties of neuromorphic processing solutions to enable natural interactions with social humanoid robots. We would like to collaborate with workshop participants in order to find the most suitable applications for neuromorphic substrate to …
Event-based learning of delays in SNNs
In a collaboration between Melika Payvand's EIS Lab and Mihai Petrovici's NeuroTMA group, we've been developing a fully event-driven training algorithm for learning delays and weights in SNNs. In the established Fast&Deep algorithm, spike times are treated as the quantity central for information propagation. …
In this workgroup we will investigate the computational capabilities of DYNAP-SE heterogeneous neurons for compressing time series data. Our focus is on exploring how these neurons can efficiently compress both periodic and aperiodic signals using few spikes per neuron, enabling low-latency pattern detection. We will optimize the DYNAP-SE neuromorphic chip …
Fancy running your SNNs 10x faster? Our GPU enhanced Neuronal Networks (GeNN) library is freely available from https://genn-team.github.io/ and provides an environment for GPU accelerated spiking neural network simulations. GeNN is capable of simulating large spiking neural network (SNN) models at competitive speeds on commodity and even embedded GPUs. In …
SynSense is a Nueromorphic startup founded in 2018 with offices in Zurich and China. We have two family of SNN processors: Xylo for low dimensional signal processing and Speck for vision processing. Both HWs come with open source and user friendly python-based librarys: Rockpool and sinabs that have been co …
We'd like to explore self-assembly in neural celluar automata and visualize their behavior in a control task.
Inspirations:
Robot:
Argo is a 65cm hobby sailboat outfitted with RPi computer, solid state wind sensor, GPS, and IMU. The aim is to learn how to get it to sail itself.
This is an umbrella workgroup which aims to organise the mass of related workgroups. We'll hold an initial session where there'll be a bit more time to introduce all the workgroups in this area than will be possible in the main session and then meet every few days to try …
Humans and animals seamlessly control their complex bodies, use new tools, and adapt to injuries.
A highly adaptive body model of some sort (body schema/body image, forward and inverse models, etc.) seems indispensable.
Using the Mujoco simulator we simulate infant-like full-body tactile agents such as iCub and MIMo environment. We …
Lu.i is an electronic neuron circuit mimicking and illustrating the basic dynamics of real, biological neurons. The printed circuit board features a configurable, fully analog implementation of the leaky integrate-and-fire model and visualizes the internal state, the membrane potential, through a VU-meter-style chain of LEDs. The neuron emits a short …
Drone attitude is generally estimated from IMUs containing accelerometers and gyroscopes. In some flying insects, sensors like these are missing, posing the question of how they estimate their attitude. One hypothesis is that they do so using vision and knowledge of their own body and control commands. Recent work has …
This workgroup is about robotics with accelerated neuromorphic hardware. In particular, we will provide multiple BrainScaleS-2 systems that feature emulated neurons with dynamics 1000 times faster than those found in biology.
All systems are equipped with multiple digital and analog real-time interfaces for robotic applications. We can inject and extract …
We bring along multiple BrainScales-2 systems and invite everyone to try some of our demos or implement their own neuromorphic experiments.
The most recent generation of BrainScaleS-2 ASICs features 512 analog multi-compartment neuron circuits with 256 plastic synapses each.
The analog circuits are tightly coupled to on-chip digital event routing …
How can robots continually learn in complex environments from limited amounts of data, both with and without supervision, similar to the way animals learn?
At Intel Neuromorphic Computing Lab, we have been developing the Continually Learning Prototypes (CLP) algorithm and its Loihi implementation to address this question [1,2]. We investigate …
The ability for learning models to be deployed in new and challenging conditions is a long-standing aspiration of modern AI. Models computing at the edge should be capable of both adaptation and knowledge-transfer, leveraging extensive (offline) pre-training while remaining nimble and avoiding catastrophic forgetting.
In this workshop we …
We will bring a Neuromorphic Drummer to Capo Caccia and launch a neuromorphic drumming control problem, as a low-cost and reproducible toy model for neuromorphic locomotion control.
The problem consists in controlling the tempo and velocity of swinging pendulum + drum-pad system through a fully neuromorphic sensing-control-acting sensori-motor loop.
The …
This is a 'fession session' open discussion - whose aim is share experience with the workshop.
No presentations, no company pitches. Just the lessons learned, for good and/or for bad.
Exact company affiiliations may (for reasons of NDAs) need to remain anonymous - this is a discussant's choice.
There have been investigations into developing standard protocols for connecting spike-based sensors & computational units (see refs) but these have so far remained disparate academic efforts. As we try to take SNNs & neuromorphics into the commercial arena, having an easily usable & agreed standard will make it easier …
Dendritic computation has become of great interest to the community as evidence from neuroscience has shown the importance of intra-neural mechanisms in creating realistic behaviour and computational experiments have demonstrated that these mechanisms (e.g. local learning within a synaptic cluster and homeostasis) may be of practical use in spike-based …
It seems as if the mainstream ANN & LLM models and techniques are moving inexorably towards sparser and lower-resolution encodings and representations. In this sense, they are converging on sparse 1-bit representations and learning mechanisms that have historically been inspired by computational neuroscience and which have at least a …
In the brain, synaptic learning is constrained to use spatio-temporal local information. However, the majority of the learning algorithms that we use to train neural networks violate spatial or temporal locality (such as RTRL and BPTT).
How can the brain efficiently assign credit using only local spatio-temporal information?
In this …
Gradient-based learning may not be what the brain is doing and there are lots of issues regarding data hunger etc but, lot of people are using gradient-based learning rules to train SNNs and other event-based models like eGRU to do really cool things. However, most of these event-based networks …
Follow-up on Tuesday's session, focused on non-monotone (range-localized) dendritic nonlinearities and a proposal for CMOS realization. https://www.science.org/doi/10.1126/science.aax6239
Walking meditation is an old meditative practice in which one focuses all their attention on the act of walking at extremely slow paces.
This simple practice can make us discover and become aware of the mechanisms that make us move in a direct, experential way.
Besides its recreational value, walking …
Meet your fellow partiipants