23 research outputs found
Active Inference in Hebbian Learning Networks
This work studies how brain-inspired neural ensembles equipped with local
Hebbian plasticity can perform active inference (AIF) in order to control
dynamical agents. A generative model capturing the environment dynamics is
learned by a network composed of two distinct Hebbian ensembles: a posterior
network, which infers latent states given the observations, and a state
transition network, which predicts the next expected latent state given current
state-action pairs. Experimental studies are conducted using the Mountain Car
environment from the OpenAI gym suite, to study the effect of the various
Hebbian network parameters on the task performance. It is shown that the
proposed Hebbian AIF approach outperforms the use of Q-learning, while not
requiring any replay buffer, as in typical reinforcement learning systems.
These results motivate further investigations of Hebbian learning for the
design of AIF networks that can learn environment dynamics without the need for
revisiting past buffered experiences
The color phi phenomenon: Not so special, after all?
We show how anomalous time reversal of stimuli and their associated responses can exist in very small connectionist models. These networks are built from dynamical toy model neurons which adhere to a minimal set of biologically plausible properties. The appearance of a “ghost” response, temporally and spatially located in between responses caused by actual stimuli, as in the phi phenomenon, is demonstrated in a similar small network, where it is caused by priming and long-distance feedforward paths. We then demonstrate that the color phi phenomenon can be present in an echo state network, a recurrent neural network, without explicitly training for the presence of the effect, such that it emerges as an artifact of the dynamical processing. Our results suggest that the color phi phenomenon might simply be a feature of the inherent dynamical and nonlinear sensory processing in the brain and in and of itself is not related to consciousness.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
Analytical and experimental study of two coupled FitzHugh-Nagumo oscillators
info:eu-repo/semantics/nonPublishe
Etude analytique et expérimentale de deux oscillateurs couplés de type FitzHugh-Nagumo
info:eu-repo/semantics/nonPublishe
Analytical and experimental study of two delay-coupled excitable units
We investigate the onset of time-periodic oscillations for a system of two identical delay-coupled excitable (nonoscillatory) units. We first analyze these solutions by using asymptotic methods. The oscillations are described as relaxation oscillations exhibiting successive slow and fast changes. The analysis highlights the determinant role of the delay during the fast transition layers. We then study experimentally a system of two coupled electronic circuits that is modeled mathematically by the same delay differential equations. We obtain quantitative agreements between analytical and experimental bifurcation diagrams. © 2014 American Physical Society.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
A low-complexity radar detector outperforming OS-CFAR for indoor drone obstacle avoidance
As radar sensors are being miniaturized, there is a growing interest for using them in indoor sensing applications such as indoor drone obstacle avoidance. In those novel scenarios, radars must perform well in dense scenes with a large number of neighboring scatterers. Central to radar performance is the detection algorithm used to separate targets from the background noise and clutter. Traditionally, most radar systems use conventional constant false alarm rate (CFAR) detectors but their performance degrades in indoor scenarios with many reflectors. Inspired by the advances in nonlinear target detection, In this article, we propose a novel high performance, yet low-complexity target detector and we experimentally validate our algorithm on a dataset acquired using a radar mounted on a drone. We experimentally show that our proposed algorithm drastically outperforms ordered statistics CFAR (OS-CFAR) (standard detector used in automotive systems) for our specific task of indoor drone navigation with more than 19% higher probability of detection for a given probability of false alarm. We also benchmark our proposed detector against a number of recently proposed multitarget CFAR detectors and show an improvement of 16% in probability of detection compared to censored harmonic averaging CFAR, with even larger improvements compared to both outlier-robust CFAR and truncated statistics log-normal CFAR in our particular indoor scenario. To the best of authors' knowledge, this article improves the state-of-the-art for high-performance yet low-complexity radar detection in critical indoor sensing applications
Slow/fast dynamics for time-delay problems: theory and experiments
info:eu-repo/semantics/nonPublishe
Learning to be conscious
Consciousness remains a formidable challenge. Different theories of consciousness have proposed vastly different mechanisms to account for phenomenal experience. Here, appealing to aspects of global workspace theory, higher-order theories, social theories, and predictive processing, we introduce a novel framework: the self-organizing metarerpresentational account (SOMA), in which consciousness is viewed as something that the brain learns to do. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of metarepresentations that qualify target first-order representations. Thus, experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. In this sense, consciousness is the brain’s (unconscious, embodied, enactive, nonconceptual) theory about itself.info:eu-repo/semantics/publishe