Dual pathway architecture underlying vocal learning in songbirds

International audienceSong acquisition and production in songbirds is governed by a dedicated neural circuitry that involves two parallel pathways: a motor pathway for the production and a basal ganglia (BG) pathway for the acquisition. Juveniles learn by imitating adult vocalizations and proceed by trial and error, errors being conveyed by a dopaminergic signal. The complex nature of the relationship between neural control and syrinx musculature makes song learning a complicated problem to solve. Reinforcement learning (RL) has been widely hypothesized to underlie such sensorimotor learning even though this can lead to sub-optimal solutions under uneven contours in continuous action spaces. In this article, we propose to re-interpret the role of a dual pathway architecture, underlying avian vocal learning, that helps overcome these limitations. We posit that the BG pathway conducts exploration by inducing large daily shifts in the vocal production while the motor pathway gradually consolidates this exploration. This process can be understood as a modified form of a simulated annealing process. Simulations on Gaussian performance landscapes and a syrinx-based performance landscape are demonstrated and compared with standard approaches. Taking behavioral constraints into account (60 days of learning, 1000 trials per day), the model allows to reach the global optimum in complex landscapes and thus provides a sound insight into the role of the dual pathway architecture underlying vocal learning

A bio-inspired model towards vocal gesture learning in songbird

Corresponding code at https://github.com/spagliarini/2018-ICDL-EPIROBInternational audienceThe paper proposes a bio-inspired model for an imitative sensorimotor learning, which aims at building a map between the sensory representations of gestures (sensory targets) and their underlying motor pattern through random exploration of the motor space. An example of such learning process occurs during vocal learning in humans or birds, when young subjects babble and learn to copy previously heard adult vocalizations. Previous work has suggested that a simple Hebbian learning rule allows perfect imitation when sensory feedback is a purely linear function of the motor pattern underlying movement production. We aim at generalizing this model to the more realistic case where sensory responses are sparse and non-linear. To this end, we explore the performance of various learning rules and nor-malizations and discuss their biological relevance. Importantly, the proposed model is robust whatever normalization is chosen. We show that both the imitation quality and the convergence time are highly dependent on the sensory selectivity and dimension of the motor representation

Learning an inverse model for vocal production: toward a bio-inspired model

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Vocal Imitation in Sensorimotor Learning Models: a Comparative Review

International audienceSensorimotor learning represents a challenging problem for natural and artificial systems. Several computational models have been proposed to explain the neural and cognitive mechanisms at play in the brain. In general, these models can be decomposed in three common components: a sensory system, a motor control device and a learning framework. The latter includes the architecture, the learning rule or optimisation method, and the exploration strategy used to guide learning. In this review, we focus on imitative vocal learning, that is exemplified in song learning in birds and speech acquisition in humans. We aim to synthesise, analyse and compare the various models of vocal learning that have been proposed, highlighting their common points and differences. We first introduce the biological context, including the behavioural and physiological hallmarks of vocal learning and sketch the neural circuits involved. Then, we detail the different components of a vocal learning model and how they are implemented in the reviewed models

Towards Biological Plausibility of Vocal Learning Models: a Short Review

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Canary Vocal Sensorimotor Model with RNN Decoder and Low-dimensional GAN Generator

International audienceSongbirds, like humans, learn to imitate sounds produced by adult conspecifics. Similarly, a complete vocal learning model should be able to produce, perceive and imitate realistic sounds. We propose (1) to use a low-dimensional generator model obtained from training WaveGAN on a canary vocalizations, (2) to use a RNN-classifier to model sensory processing. In this scenario, can a simple Hebbian learning rule drive the learning of the inverse model linking the perceptual space and the motor space? First, we study how the motor latent space topology affects the learning process. We then investigate the influence of the learning rate and of the motor latent space dimension. We observe that a simple Hebbian rule is able to drive the learning of realistic sounds produced via a low-dimensional GAN

Replication of Laje & Mindlin's model producing synthetic syllables

International audienceIn order to implement a realistic output in a song learning model, we investigated one of the models proposed in the long research quest of Minldin. We were looking for a model with few degrees of freedom, and replicated the RA model proposed in Laje & Mindlin (2002). The model describes three neural populations of RA receiving inputs from HVC: two of which are excitatory and control the syringeal muscles and respiratory muscles (xk and xp resp.); one which stands for inhibitory interneurons. This model is able to produce syllables sounds. We implemented this model in Python language and tried to reproduced several figures of the original paper: a bifurcation diagram and three synthetic syllables

A Natural History of Skills

International audienceThe dorsal pallium (a.k.a. the cortex in the mammals) makes a large loop circuit with the basal ganglia and the thalamus known to control and adapt behavior but the who's who of the functional roles of these structures is still debated. Influenced by the Triune brain theory that was proposed in the early sixties, many current theories propose a hierarchical organization on the top of which stands the cortex to which the subcortical structures are subordinated. In particular, habits formation has been proposed to reflect a switch from conscious on-line control of behavior by the cortex, to a fully automated subcortical control. In this review, we propose to revalue the function of the network in light of the current experimental evidence concerning the anatomy and physiology of the basal ganglia-cortical circuits in vertebrates. We briefly review the current theories and show that they could be encompassed in a broader framework of skill learning and performance. Then, after reminding the state of the art concerning the anatomical architecture of the network and the underlying dynamic processes, we summarize the evolution of the anatomical and physiological substrate of skill learning and performance among vertebrates. We then lay out our hypothesis that the development of automatized skills relies on the BG teaching cortical circuits and is actually a late feature linked with the development of a specialized cortex or pallium that evolved in parallel in different taxa. We finally propose a minimal computational framework where this hypothesis can be explicitly implemented and tested

[Re] A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity

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