Metaplasticity | Laurent Perrinet

Detection of precise spiking motifs using spike-time dependent weight and delay plasticity

Sun, 11 Sep 2022 00:00:00 +0000

see a follow-up as journal paper:
Antoine Grimaldi, Laurent U Perrinet (2023). Learning heterogeneous delays in a layer of spiking neurons for fast motion detection. Biological Cybernetics.
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A homeostatic gain control mechanism to improve event-driven object recognition

Thu, 24 Jun 2021 00:00:00 +0000

was presented at the Bio-inspired circuits, systems and algorithms for multimedia special session of the Content-Based Multimedia Indexing (CBMI) 2021 conference that you can watch on Youtube.
this proceedings paper follows up he poster presented in :
Antoine Grimaldi, Victor Boutin, Sio-Hoi Ieng, Laurent U Perrinet, Ryad Benosman (2021). A robust bio-inspired approach to event-driven object recognition. Computational and Systems Neuroscience (Cosyne) 2021.
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this proceedings paper was followed by the poster presented at CRS :
Antoine Grimaldi, Victor Boutin, Sio-Hoi Ieng, Ryad Benosman, Laurent U Perrinet (2021). From event-based computations to a bio-plausible Spiking Neural Network. Champalimaud Research Symposium (CRS21).
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read the follow-up paper :
Antoine Grimaldi, Victor Boutin, Sio-Hoi Ieng, Ryad Benosman, Laurent U Perrinet (2024). A Robust Event-Driven Approach to Always-on Object Recognition. Neural Networks.
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Antoine Grimaldi and Laurent Perrinet received funding from the European Union ERA-NET CHIST-ERA 2018 research and innovation program under grant agreement No ANR-19-CHR3-0008-03.

A robust bio-inspired approach to event-driven object recognition

Fri, 26 Feb 2021 00:00:00 +0000

see the poster online on the Hopin platform
see a follow-up in:
Antoine Grimaldi, Victor Boutin, Sio-Hoi Ieng, Laurent U Perrinet, Ryad Benosman (2021). A homeostatic gain control mechanism to improve event-driven object recognition. Content-Based Multimedia Indexing (CBMI) 2021.
PDF Cite DOI URL HAL Video
read also the follow-up paper :
Antoine Grimaldi, Victor Boutin, Sio-Hoi Ieng, Ryad Benosman, Laurent U Perrinet (2024). A Robust Event-Driven Approach to Always-on Object Recognition. Neural Networks.
PDF Cite DOI Press Hal Code URL
Antoine Grimaldi and Laurent Perrinet received funding from the European Union ERA-NET CHIST-ERA 2018 research and innovation program under grant agreement No ANR-19-CHR3-0008-03.

Learning dynamics in a neural network model of the primary visual cortex

Wed, 01 Jan 2020 00:00:00 +0000

See also Ladret and Perrinet, 2019

Efficient learning of sparse image representations using homeostatic regulation

Sun, 01 Jan 2017 00:00:00 +0000

This work is a followup of
Laurent U Perrinet (2010). Role of homeostasis in learning sparse representations. Neural Computation.
PDF Cite DOI Hal Code URL arXiv
the poster (PDF) will be presented Thursday, May 18 @ NeuroFrance, Bordeaux.
see a follow-up publication on
Laurent U Perrinet (2019). An adaptive homeostatic algorithm for the unsupervised learning of visual features. Vision.
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see a related work describing SDPC in:
Victor Boutin, Angelo Franciosini, Frédéric Chavane, Franck Ruffier, Laurent U Perrinet (2021). Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system. PLoS Computational Biology.
PDF Cite DOI Code URL arXiv

Efficient learning of sparse image representations using homeostatic regulation

Sun, 01 Jan 2017 00:00:00 +0000

This work is a followup of Perrinet, 2010, Neural Computation
code is available @ https://github.com/laurentperrinet/BoutinRuffierPerrinet17spars and heavily uses https://github.com/bicv/SparseHebbianLearning
the poster (PDF) will be presented Thursday, June 8 @ SPARS, Lisbon.
see a related work describing SDPC in:
Victor Boutin, Angelo Franciosini, Frédéric Chavane, Franck Ruffier, Laurent U Perrinet (2021). Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system. PLoS Computational Biology.
PDF Cite DOI Code URL arXiv

ANR CausaL (2018/2020)

Wed, 27 Apr 2016 00:00:00 +0000

With Andrea Brovelli (INT), Mateus Joffily (GATE)…

See https://anr.fr/Project-ANR-18-CE28-0016

Humans have an extraordinary capacity to infer cause-effect relations. In particular, we excel in forming beliefs about the causal effect of actions. Causal learning provides the basis for rational decision-making and allows people to engage in meaningful life and social interactions. Causal learning is a form of goal-directed learning, defined as the capacity to rapidly learn the consequence of actions and to select behaviours according to goals and motivational state. This ability is based on internal models of the consequence of our behaviors and relies on learning rules determined by the contingency between actions and outcomes. At a first approximation, contingency ΔP is operationalized as the difference between two conditional probabilities: i) P(O|A), the probability of outcome O given action A; ii) P(O|¬A), the probability of the outcome when the action is withheld. In everyday life, people perceive their actions as causing a given outcome if the contingency is positive, whereas they perceive them as preventing it if negative; when P(O|A) and P(O|¬A) are equal, people report no causal effect . Despite the centrality of causal learning, a clear understanding of both the internal computations and neural substrates (the so-called cognitive architectures) is currently missing. Our project will therefore address two key questions:

What are the key internal representations of causal beliefs and what are the computational processes that enable their formation during learning?
How are internal representations and computational processes implemented in the brain? CausaL will address these two objectives through two dedicated research work packages (WPs).

Acknowledgement : This work was supported by ANR project ANR-18-AAPG–“CAUSAL, Cognitive Architectures of Causal Learning”.

A dynamic model for decoding direction and orientation in macaque primary visual cortex

Fri, 01 Jan 2016 00:00:00 +0000

see a follow-up in this poster
This is a followup in Perrinet et al, 2012

A dynamic model for decoding direction and orientation in macaque primary visual cortex

Fri, 01 Jan 2016 00:00:00 +0000

Network of integrate-and-fire neurons using Rank Order Coding B: spike timing dependant plasticity and emergence of orientation selectivity

Mon, 01 Jan 2001 00:00:00 +0000