Prenatal auditory experience and its sequelae

Marin Vogelsang, Lukas Vogelsang
WILEY, 2022
Article

Résumé

Towards the end of the second trimester of gestation, a human fetus is able to register environmental sounds. This in utero auditory experience is characterized by comprising strongly low-pass-filtered versions of sounds from the external world. Here, we present computational tests of the hypothesis that this early exposure to severely degraded auditory inputs serves an adaptive purpose-it may induce the neural development of extended temporal integration. Such integration can facilitate the detection of information carried by low-frequency variations in the auditory signal, including emotional or other prosodic content. To test this prediction, we characterized the impact of several training regimens, biomimetic and otherwise, on a computational model system trained and tested on the task of emotion recognition. We find that training with an auditory trajectory recapitulating that of a neurotypical infant in the pre-to-postnatal period results in temporally extended receptive field structures and yields the best subsequent accuracy and generalization performance on the task of emotion recognition. This strongly suggests that the progression from low-pass-filtered to full-frequency inputs is likely to be an adaptive feature of our development, conferring significant benefits to later auditory processing abilities relying on temporally extended analyses. Additionally, this finding can help explain some of the auditory impairments associated with preterm births, suggests guidelines for the design of auditory environments in neonatal care units, and points to enhanced training procedures for computational models.

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https://infoscience.epfl.ch/record/294667?ln=fr

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Marin Vogelsang, Lukas Vogelsang
WILEY, 2022
Article

Résumé

Official source

https://infoscience.epfl.ch/record/294667?ln=fr

À propos de ce résultat

Concepts associés (17)

Concepts associés

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Publications associées (15)

Publications associées

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Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same functional unit, the neuron, and develop through the same learning mechanism, synaptic plasticity. It motivates the conjecture of a unifying theory to explain cortical representational learning across sensory modalities. In this thesis we present theories and computational models of learning and optimization in neural networks, postulating functional properties of synaptic plasticity that support the apparent universal learning capacity of cortical networks. In the past decades, a variety of theories and models have been proposed to describe receptive field formation in sensory areas. They include normative models such as sparse coding, and bottom-up models such as spike-timing dependent plasticity. We bring together candidate explanations by demonstrating that in fact a single principle is sufficient to explain receptive field development. First, we show that many representative models of sensory development are in fact implementing variations of a common principle: nonlinear Hebbian learning. Second, we reveal that nonlinear Hebbian learning is sufficient for receptive field formation through sensory inputs. A surprising result is that our findings are independent of specific details, and allow for robust predictions of the learned receptive fields. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities. The Hebbian learning theory substantiates that synaptic plasticity can be interpreted as an optimization procedure, implementing stochastic gradient descent. In stochastic gradient descent inputs arrive sequentially, as in sensory streams. However, individual data samples have very little information about the correct learning signal, and it becomes a fundamental problem to know how many samples are required for reliable synaptic changes. Through estimation theory, we develop a novel adaptive learning rate model, that adapts the magnitude of synaptic changes based on the statistics of the learning signal, enabling an optimal use of data samples. Our model has a simple implementation and demonstrates improved learning speed, making this a promising candidate for large artificial neural network applications. The model also makes predictions on how cortical plasticity may modulate synaptic plasticity for optimal learning. The optimal sampling size for reliable learning allows us to estimate optimal learning times for a given model. We apply this theory to derive analytical bounds on times for the optimization of synaptic connections. First, we show this optimization problem to have exponentially many saddle-nodes, which lead to small gradients and slow learning. Second, we show that the number of input synapses to a neuron modulates the magnitude of the initial gradient, determining the duration of learning. Our final result reveals that the learning duration increases supra-linearly with the number of synapses, suggesting an effective limit on synaptic connections and receptive field sizes in developing neural networks.

EPFL2016

Chargement

The complexity of today's autonomous robots poses a major challenge for Artificial Intelligence. These robots are equipped with sophisticated sensors and mechanical abilities that allow them to enter our homes and interact with humans. For example, today's robots are almost all equipped with vision and several of them can move over rough terrain with wheels or legs. The methods developed so far in Artificial Intelligence, however, are not yet ready to cope with the complexity of the information gathered through the robot sensors and the need for rapid action in partially unknown and dynamic environments. In this thesis, I will argue that the apparent complexity of the environment and of the robot brain can be significantly simplified if perception, behavior, and learning are allowed to co-develop on the same time scale. In doing so, robots become sensitive to, and actively exploit, characteristics of the environment that they can tackle within their own computational and physical constraints. This line of work is grounded on philosophical and psychological research showing that perception is an active process mediated by behavior. However, computational models of active vision are very rare and often rely on architectures that are preprogrammed to detect certain characteristics of the environment. Previous work have shown that complex visual tasks, such as position and size invariant shape recognition as well as navigation, can be tackled with remarkably simple neural architectures generated by a coevolutionary process of active vision and feature selection. Behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons were evolved while freely interacting with their environments. I proceed further on this line of investigation and describe the application of this methodology in three situations, namely car driving with an omnidirectional camera, goal-oriented navigation of a humanoid robot, and cooperative tasks by two agents. I will show that these systems develop sensitivity to a number of oriented, retinotopic, visual features – oriented edges, corners, height – and a behavioral repertoire to locate, bring, and keep these features in sensitive regions of the vision system that allow them to accomplish their goals. In a second set of experiments, I will show that active vision can be exploited by the robot to perform anticipatory exploration of the environment in a task that requires landmark-based navigation. Evolved robots exploit an internal expectation system that makes use of active exploration to check for expected events in the environment. I will describe a third set of experiments where, in addition to an evolutionary process, the visual system of the robot can develop receptive fields by means of unsupervised Hebbian learning and show that these receptive fields are significantly affected by the behavior of the system and differ from those predicted by most computational models of visual cortex. Finally, I will show that these robots replicate the performance deficiencies observed in experiments of motor deprivation with kitten when they are exposed to the same type of motor deprivations. Furthermore, the analyses of our robot brains suggest an explanation for the deficiencies observed in kitten that have not yet been fully understood.

EPFL2007

Chargement

Cognitive language engineering towards robust human-computer interaction

Vincenzo Pallotta

Intelligent information processing seems to be one of the most challenging task among those involved in human-computer interaction. A central issue is how to model the various types of interaction among artificial and natural entities at different levels of abstraction. On the one hand, models of interaction are required to better understand the communication phenomena. On the other, suitable languages and paradigms should provide powerful frameworks for developing computer-based applications. In this dissertation I focus on different aspects of the second problem, trying to develop a methodology for the design of interactive natural language applications (e.g. from question-answering to mixed-initiative dialogue). One of the main aspects I am concerned with in this work is the problem of their robustness. Several methods have been proposed for achieving robustness in natural language understanding, but these methods are sometimes hard to scale up or re-use in different applications. Moreover, they often concentrate on a single linguistic level of the processing rather than offering a global solution. I will set up a Language Engineering environment whose goal is to combine software engineering and cognitive aspects (e.g. aspects related to representation of a mental model of the speaker). Given its complexity, it is apparent that the problem can be solved only partially. I want to stress here that the main contribution of my work is a holistic perspective on the problem of natural language understanding. Rather than focusing on a particular aspect of natural language processing, I tried to benefit from the big amount of work that has been already done in Computational Linguistics and Computer Science merging different ideas and techniques. In the first part of the dissertation I explore the universe of Language Engineering in order to clarify how my contribution can be situated. After a survey on the state of the art on robust methods in analysis of natural language data, I focus on the role that Computational Logic plays in relating the syntactic and semantic analysis of natural language to its practical understanding within specific applications. Robustness is considered from two complementary perspectives, borrowing the terminology from modern software engineering: robustness "in the small" and robustness "in the large". The first perspective is discussed while presenting an application for the Interaction through Speech with Information Systems, where robust semantic parsing is used to extract queries from spoken natural language utterances.The second perspective is examplified by the re-engineering of an existing text analysis system using a new Language Engineering methodology: Agent-Oriented Language Engineering. In the second part of the thesis I discuss how cognitive aspects can be integrated into a Language Engineering environment leading to the notion of Cognitive Language Enginering. I tackle the difficult problem of robust dialogue management from both a cognitive and computational perspective. I propose two frameworks for the semantic representation and assimilation of information into the dialogue information state. The first framework allows us to represent and reason about the dynamic aspects of objects and events. The second framework is centered on the notion of mental space and it is used to build representations of the cognitive processing of information during communication.

EPFL2002

Chargement

EPFL2007

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

EPFL2016

Cognitive language engineering towards robust human-computer interaction

Vincenzo Pallotta

EPFL2002