Neural Comput November 2002 PMID 12433288

Real-time computing without stable states: a new framework for neural computation based on perturbations.

Wolfgang Maass, Thomas Natschläger, Henry Markram

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Peak: 3 in Nov 2020

across 4 sources since Jul 2019

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The discussions include references to influential researchers like Wolfgang Maass and mentions of discovering new work, which shows moderate interest and recognition of the article’s relevance. The tone indicates curiosity and acknowledgment of its contribution without strong agreement or in-depth critique, reflecting a moderate overall impact and engagement.

AgreementNeither agree nor disagree

The discussions display mixed reactions with some curiosity but no clear consensus supporting or opposing the publication.

InterestModerate interest

There is a moderate level of curiosity, especially with mentions of discovering related work and recognizing classics, indicating some engagement but not deep enthusiasm.

EngagementNeutral engagement

Posts touch upon the significance of the work, like noting classic papers and recent advances, yet lack detailed analysis or critique, reflecting a surface-level engagement.

ImpactModerate impact

Comments suggest the research is noteworthy and potentially influential in the field, but there is no direct indication of groundbreaking impact or widespread debate.

Abstract in brief

The paper introduces a novel model for real-time neural computation that relies on the transient dynamics of high-dimensional neural circuits rather than stable internal states, allowing for processing continuously changing input streams without task-specific circuit design.
This model, based on principles of dynamical systems and statistical learning, uses the concept of a 'liquid state machine' where readout neurons extract relevant information from the evolving neural activity to produce stable outputs, even without internal stability.
Unlike traditional Turing-based models requiring discrete, sequential states, this approach emphasizes the use of high-dimensional, transient neural states, offering new insights into neural coding, experimental design, and real-time processing in biological neural systems.

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All mentions

Reservoir CPG for Real-Time Neural Computation on Loihi

This video demonstrates a lamprey robot simulation controlled by reservoir-based CPG on an Intel Loihi board, showcasing novel neural computation principles based on transient dynamics rather than stable states…

Master Thesis · Nov 15, 2020 44 views

Neural Computation Using Transient Dynamics in Reservoir CPGs

This video demonstrates a lamprey robot controlled via a reservoir-based CPG on a SpiNNaker board, utilizing neural computation principles based on dynamic, transient neural states rather than stable ones, enabling…

Master Thesis · Nov 15, 2020 18 views

Reservoir CPG for Real-Time Neural Control of Robots on Skylake CPU

This simulation showcases a lamprey robot controlled by a reservoir-based CPG, executed on an Intel Skylake CPU using Nengo. The model leverages transient neural dynamics for real-time processing, inspired by recent…

Master Thesis · Nov 15, 2020 11 views

Kirito (e/acc)

@bronzeagepapi · X

RT @drmichaellevin: After Luis F. Seoane's excellent talk at SFI, am discovering the work of Wolfgang Maass (https://t.co/62GRdKdTIn) - res…

Mar 29, 2024 · 2 shares · 4,536 followers View post →

(Sichu Lu(Sichu.Lu218@proton.me)

@lu_sichu · X

https://t.co/BbmQIyn04n wow really

Oct 12, 2023 · 3,393 followers View post →

ed fernandez

@efernandez · X

RT @drmichaellevin: After Luis F. Seoane's excellent talk at SFI, am discovering the work of Wolfgang Maass (https://t.co/62GRdKdTIn) - res…

Apr 8, 2020 · 2 shares · 5,620 followers View post →

Dan Levenstein

@dlevenstein · X

@neuropoetic @neuromeditate @ldallap1 The classic: https://t.co/j7YVKQwYDY The recent classic: https://t.co/G5Ur21SHMR The classic classic: https://t.co/LVaZ8THDi6

Jul 10, 2019 · 2 likes · 4,147 followers View post →