Habituation: a non-associative learning rule design for spiking neurons and an autonomous mobile robots implementation

Cyr, André; Boukadoum, Mounir

doi:10.1088/1748-3182/8/1/016007

Cited by 4 publications

(2 citation statements)

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“…In neuroinspired systems, AI models have looked at learning from various levels of abstraction—focusing either on modeling the synaptic phenomenon of short‐term plasticity [ 24,41 ] or on modulating the excitability of neuronal dynamics. [ 42 ] Here, we focus on the neuron activity modulation scenario and showcase that the cellular‐like learning in NiO x can be used to implement homeostatic regulation in neurons, [ 43 ] essential for stability while learning. Homeostasis ensures a neuron, that has fired before, finds it harder to fire in the future (requires a greater input than previously).…”

Section: Proof‐of‐concept Application Of Nonassociative Learning In S...mentioning

confidence: 99%

All‐Electric Nonassociative Learning in Nickel Oxide

Mondal

Zhang

Islam

et al. 2022

Advanced Intelligent Systems

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Habituation and sensitization represent nonassociative learning mechanisms in both non‐neural and neural organisms. They are essential for a range of functions from survival to adaptation in dynamic environments. Design of hardware for neuroinspired computing strives to emulate such features driven by electric bias and can also be incorporated into neural network algorithms. Herein, cellular‐like learning in oxygen‐deficient NiOx devices is demonstrated. Both habituation learning and sensitization response can be achieved in a single device by simply controlling the magnitude of the electric field. Spontaneous memory relaxations and dynamic redistribution of oxygen vacancies under electric bias enable such learning behavior of NiOx under sequential training. These characteristics in simple device arrays are implemented to learn alphabets as well as demonstrate simulated algorithmic use cases in digit recognition. Transition metal oxides with carefully prepared defect concentrations can be highly sensitive to electronic structure perturbations under moderate electrical stimulus and serve as building blocks for next‐generation neuroinspired computing hardware.

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Section: Proof‐of‐concept Application Of Nonassociative Learning In S...mentioning

confidence: 99%

All‐Electric Nonassociative Learning in Nickel Oxide

Mondal

Zhang

Islam

et al. 2022

Advanced Intelligent Systems

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“…The justification to include this learning rule in our OC model is to avoid reflex behaviors from constant input (McSweeney et al, 1996), acting similarly to an intrinsic action selection mechanism. To this end, the original ASNN of SIMCOG was recently enhanced with a novel computational model of habituation (Cyr and Boukadoum, 2013), extended for temporal features.…”

Section: Introductionmentioning

confidence: 99%

Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired robot's controller

Cyr

Boukadoum

Thériault³

2014

Front. Neurorobot.

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In this paper, we investigate the operant conditioning (OC) learning process within a bio-inspired paradigm, using artificial spiking neural networks (ASNN) to act as robot brain controllers. In biological agents, OC results in behavioral changes learned from the consequences of previous actions, based on progressive prediction adjustment from rewarding or punishing signals. In a neurorobotics context, virtual and physical autonomous robots may benefit from a similar learning skill when facing unknown and unsupervised environments. In this work, we demonstrate that a simple invariant micro-circuit can sustain OC in multiple learning scenarios. The motivation for this new OC implementation model stems from the relatively complex alternatives that have been described in the computational literature and recent advances in neurobiology. Our elementary kernel includes only a few crucial neurons, synaptic links and originally from the integration of habituation and spike-timing dependent plasticity as learning rules. Using several tasks of incremental complexity, our results show that a minimal neural component set is sufficient to realize many OC procedures. Hence, with the proposed OC module, designing learning tasks with an ASNN and a bio-inspired robot context leads to simpler neural architectures for achieving complex behaviors.

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