NICE #8

Talk videos

• Talk videos, which the speakers made available for public viewing are linked in the agenda. Some videos are accessible only for meeting attendants (from their personal page)
• Public videos are also available in the NICE YouTube channel in the NICE 2021 playlist

Chatserver

We have a chatserver for the workshop (please use the username / initial password from your meetings 'personal page' (URL in your email). Please ask talk-related questions in the respective talk-channel (linked here in the agenda).

"Proceedings"

The proceedings for NICE 2021 are actually papers for the postponed 2020 NICE. You can find papers for many of the presentations here: https://dl.acm.org/doi/proceedings/10.1145/3381755

Welcome to NICE #8

Link to chat channel

Organizer Round

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Keynote: Lessons from Loihi for the Future of Neuromorphic Computing

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Link to chat channel

Why is Neuromorphic Event-based Engineering the future of AI?

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Link to chat channel

Exploring the possibilities of analog neuromorphic computing with BrainScaleS

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Link to chat channel

(break)

Group photo (zoom screenshots)

Note: we want to publish the group photo publicly = please switch on you camera, if you agree to appear in the photo.

Evaluating complexity and resilience trade-offs in emerging memory inference machines

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Lightning talk: From clean room to machine room: towards accelerated cortical simulations on the BrainScaleS wafer-scale system

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Link to chat channel

Closed-loop experiments on the BrainScaleS-2 architecture

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Link to chat channel

Batch << 1: Why Neuromorphic Computing Architectures Suit Real-Time Workloads

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Link to chat channel

Neuromorphic and AI research at BCAI (Bosch Center for Artificial Intelligence)

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Link to chat channel

Mapping Deep Neural Networks on SpiNNaker2

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Link to chat channel

Open mic / discussion

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End of day I

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Tutorials: BrainScaleS and DYNAP-SE

Two tutorials/hands on in parallel:

• BrainScaleS (note: the same BrainScaleS hands on tutorial is also offered on Thursday, 10:00-13:00h CET). Please use the "Join Main" dial in from your personal info page to access this tutorial.
  • about 30 min introduction
  • hands-on usage of the BrainScaleS system (via web browser). Limited number of participants.
    Link to tutorial BrainScaleS chat channel
    
    For talk videos see further down in the agenda (18 March)
    
    
• DYNAP-SE (note:the same DYNAP-SE hands on tutorial is also offered on Wednesday and Friday morning) Please use the "Join Parallel meeting" dial in from your personal info page to access this tutorial.
  • 1-hour live/interactive Dynapse demo: demo on a real Dynapse, take questions and implementing small changes from the audience. (this part of the tutorial is accessible for all NICE registered attendants)
  • 2-hour guided session where participants run a Jupyter notebook with simulations modelling Dynapse. This part is limited to 15 people per session.
    Link to Tutorial DYNAP-SE chat channel
    For talk videos see further down in the agenda (19 March)

For a description please see the tutorials page.

Tutorials: SpiNNaker and DYNAP-SE

Two tutorials in parallel:

• Starting at 10:30h CET: SpiNNaker (note: the same SpiNNaker hands on tutorial is also offered on Thursday, 21:00 - 22:30h CET) Please use the "Join Main" dial in from your personal info page to access this tutorial.
  • Introduction presentation slides.pdf
  • Link to Tutorial SpiNNaker chat channel
    
    
• Starting at 10:00h CET: DYNAP-SE (note:the same DYNAP-SE hands on tutorial is also offered on Tuesday evening and Friday morning) Please use the "Join Parallel meeting" dial in from your personal info page to access this tutorial.
  • 1-hour live/interactive Dynapse demo: demo on a real Dynapse, take questions and implementing small changes from the audience. (this part of the tutorial is accessible for all NICE registered attendants)
  • 2-hour guided session where participants run a Jupyter notebook with simulations modelling Dynapse. This part is limited to 15 people per session.
    Link to Tutorial DYNAP-SE chat channel
    For the Dynap-se tutorial talk videos see further down in the agenda (19 March)

For a description please see the tutorials page.

SpiNNaker tutorial introduction

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NICE - day II

Keynote: From Brains to Silicon -- Applying lessons from neuroscience to machine learning

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Link to chat channel

A Neuromorphic Future for Classic Computing Tasks

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Link to chat channel

Lightning talk: Benchmarking of Neuromorphic Hardware Systems

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Link to chat channel

Natural density cortical models as benchmarks for universal neuromorphic computers

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Poster Lightning Talks

1 min - 1 slide poster appetizers

• Astrocyte-modulated neuromorphic Central Pattern Generator for Robot Locomotion on Intel's Loihi (Ioannis Polykretis)
• Bio-inspired few-shot learning with spiking neural networks (Garibaldi Pineda Garcia)
• BrainScaleS Large Scale Spike Communication using Extoll (Tobias Thommes)
• Critical Limits in a Bump Attractor Network of Spiking Neurons (Alberto Vergani)
• Deep reinforcement learning for time-continuous substrates (Akos Ferenc Kungl)
• Energy Constraints Improve Liquid State Machine Performance (Andrew Fountain)
• Machine Perception: Similarity Representation while Learning in the Wild (Ayon Borthakur)
• Structural plasticity on spiking neuromorphic hardware (Benjamin Cramer)
• Training Delays in Spiking Neural Networks (Pau Vilimelis Aceituno)
• IOP Publishing: new open access journal Neuromorphic Computing and Engineering

Talk-posters:

• Neuroevolution with Scaleup (J. David Schaffer)

Poster session A (and break)

• For posters:
  • Astrocyte-modulated neuromorphic Central Pattern Generator for Robot Locomotion on Intel's Loihi (Ioannis Polykretis)
    poster.pdf and poster chat channel
  • Bio-inspired few-shot learning with spiking neural networks (Garibaldi Pineda Garcia)
    poster.pdf and poster chat channel
  • BrainScaleS Large Scale Spike Communication using Extoll (Tobias Thommes)
    poster.pdf and poster chat channel
  • Critical Limits in a Bump Attractor Network of Spiking Neurons (Alberto Vergani)
    poster.pdf and poster chat channel
  • Deep reinforcement learning for time-continuous substrates (Akos Ferenc Kungl)
    poster.pdf and poster chat channel
• talk posters
  • Christoph Ostrau
    poster.pdf and chat channel
• sponsor
  • IOP Publishing presents Neuromorphic Computing and Engineering™ -- see the sponsored psf

Platform-Agnostic Neural Algorithm Composition using Fugu

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Link to chat channel

Lightning talk: Implementing Backpropagation for Learning on Neuromorphic Spiking Hardware

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Link to chat channel

Inductive bias transfer between brains and machines

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Link to chat channel

Lightning talk: Spike Latency Reduction generates Efficient Predictive Coding

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Link to chat channel

Lightning talk: Cognitive Domain Ontologies: HPCs to Ultra Low Power Neuromorphic Platforms

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Link to chat channel

Open mic / discussion

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(break)

Tutorial: Loihi

We’ll cover a few new “advanced” topics of interest to the community. These are

• Characterizing energy and performance of Loihi workloads
• Solving and constraint satisfaction problems on Loihi and benchmarking them to a state-of-the-art CPU solver
• Training deep SNNs for Loihi with SLAYER

We’ll present on these topics and show some code running. Anyone who has access to Loihi will be able to find and run the code themselves, but we’d like to clarify that access to Loihi is not required for attending the tutorials. (If you want to get access to Loihi, please email inrc_interest@intel.com to get legal access to the Loihi cloud systems.)

Link to chat channel

Intel Loihi's NxSDK: Introduction and overview

A Fast and Efficient Constraint Satisfaction Solver on Loihi

SLAYER for Loihi

Performance Characterization on Loihi

Tutorial: BrainScaleS hands-on

Please use the "Join_Main" dial in on your personal page to attend.

• about 30 min introduction
• hands-on usage of the BrainScaleS system (via web browser). Limited number of participants.

For a description of the pre-requirements, please see the tutorials page.

Link to chat channel

BrainScaleS-2 hands-on introduction, part I: Spiking mode

BrainScaleS-2 hands-on introduction, part II: Matrix multiplication mode

BrainScaleS-2 hands-on introduction, part III: Matrix multiplication mode II

(hands-on work with the BSS-2 single chip system)

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NICE - day III

Please use the "Join_Main" dial in on your personal page to attend.

Keynote: Biological inspiration for improving computing and learning in spiking neural networks

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Link to chat channel

On the computational power and complexity of Spiking Neural Networks

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Evolutionary Optimization for Neuromorphic Systems

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An event-based gas sensing device that resolves fast transients in a turbulent environment

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Sequence learning, prediction, and generation in networks of spiking neurons

Sequence learning, prediction and generation has been proposed to be the universal computation performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes this form of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context-specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms.

Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns non-Markovian sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific feedforward subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context-specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences.

By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities. The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role of sequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effect of the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay.

Link to chat channel

Poster session b (and break)

For posters:

• Energy Constraints Improve Liquid State Machine Performance (Andrew Fountain)
  poster.pdf and poster chat channel
• Machine Perception: Similarity Representation while Learning in the Wild (Ayon Borthakur)
  poster.pdf and poster chat channel
• Structural plasticity on spiking neuromorphic hardware (Benjamin Cramer)
  poster.pdf and poster chat channel
• Training Delays in Spiking Neural Networks (Pau Vilimelis Aceituno)
  poster.pdf and poster chat channel

And for talk posters by:

• Younes Bouhadjar: "Sequence learning, prediction, and generation in networks of spiking neurons"
  talk chat channel
• Dylan Paiton: "Subspace Locally Competitive Algorithms"
  poster.pdf and the talk chat channel
• J. David Schaffer: Evolving Spiking Neural Networks for Robot Sensory-motor Decision Tasks of Varying Difficulty
  poster.pdf and chat channel
• Michael Schmuker / Damien Drix: An event-based gas sensing device that resolves fast transients in a turbulent environment
  poster.pdf and talk chat channel

Lessons from neurobiology and physics: pseudobackprop & more

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Link to chat channel

Conductance-based dendrites perform reliability-weighted opinion pooling

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Link to chat channel

Lightning talk: Natural gradient learning for spiking neurons

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Link to chat channel

Making spiking neurons more succinct with multi-compartment models

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Lightning talk: The Computational Capacity of Mem-LRC Reservoirs

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Link to chat channel

Open mic / discussion

Link to chat channel

(break)

Tutorial: SpiNNaker hands-on

For a recording of the introduction, please see the agenda entry for 17 March in the morning.

For a description please see the tutorials page.

Link to chat channel

Tutorial:: DYNAP-SE

Please use the "Join_Main" dial in on your personal page to attend.

• 1-hour live/interactive Dynapse demo: demo on a real Dynapse, take questions and implementing small changes from the audience. (this part of the tutorial is accessible for all NICE registered attendants)
• 2-hour guided session where participants run a Jupyter notebook with simulations modelling Dynapse. This part is limited to 15 people per session.

Link to chat channel

Dynap-SE1 Demo Session

Remote demo of the Dynap-SE board

DYNAP-SE tutorial session 2: Simulating Dynap-SE1

(break)

NICE - day IV

Please use the "Join_Main" dial in on your personal page to attend.

Keynote: Bottom-up and top-down neuromorphic processor design: Unveiling roads to embedded cognition

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Link to chat channel

Lightning talk: Subspace Locally Competitive Algorithms

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Link to chat channel

Programming neuromorphic computers: PyNN and beyond

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Lightning talk: Neuromorphic Graph Algorithms : Cycle Detection, Odd Cycle Detection, and Max Flow

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Link to chat channel

BrainScaleS: Development Methodologies and Operating System

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Link to chat channel

Lightning talk: Evolving Spiking Neural Networks for Robot Sensory-motor Decision Tasks of Varying Difficulty

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Link to chat channel

(break)

Relational Neurogenesis for Lifelong Learning Agents

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Link to chat channel

Lightning talk: Fast and deep neuromorphic learning with first-spike coding

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Link to chat channel

Lightning talk: Neuromorphic Computing for Spacecraft’s Terrain Relative Navigation: A Case of Event-Based Crater Classification Task

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Beyond Backprop: Different Approaches to Credit Assignment in Neural Nets

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Lightning talk: Comparing Neural Accelerators & Neuromorphic Architectures The False Idol of Operations

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Real-time Mapping on a Neuromorphic Processor

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Opn mic / Wrap up

• Best talk awards (by the NEUROTECH project):
  • Johannes Leugering (Fraunhofer)
  • Julian Göltz (Heidelberg University)
  • Charlotte Frenkel (Institute of Neuroinformatics)
  • Jakob Jordan (University of Bern)

Farewell .... and See you next year...

End of NICE 2021