Hierarchical-Task Reservoir for Online Semantic Analysis From Continuous Speech.

In this article, we propose a novel architecture called hierarchical-task reservoir (HTR) suitable for real-time applications for which different levels of abstraction are available. We apply it to semantic role labeling (SRL) based on continuous speech recognition. Taking inspiration from the brain, this demonstrates the hierarchies of representations from perceptive to integrative areas, and we consider a hierarchy of four subtasks with increasing levels of abstraction (phone, word, part-of-speech (POS), and semantic role tags).

A Robust Model of Gated Working Memory.

Gated working memory is defined as the capacity of holding arbitrary information at any time in order to be used at a later time. Based on electrophysiological recordings, several computational models have tackled the problem using dedicated and explicit mechanisms. We propose instead to consider an implicit mechanism based on a random recurrent neural network.

Sustainable computational science: the ReScience initiative.

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results; however, computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true.

Corticostriatal response selection in sentence production: Insights from neural network simulation with reservoir computing.

Language production requires selection of the appropriate sentence structure to accommodate the communication goal of the speaker - the transmission of a particular meaning. Here we consider event meanings, in terms of predicates and thematic roles, and we address the problem that a given event can be described from multiple perspectives, which poses a problem of response selection. We present a model of response selection in sentence production that is inspired by the primate corticostriatal system.

Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks.

One of the principal functions of human language is to allow people to coordinate joint action. This includes the description of events, requests for action, and their organization in time. A crucial component of language acquisition is learning the grammatical structures that allow the expression of such complex meaning related to physical events.

Real-time parallel processing of grammatical structure in the fronto-striatal system: a recurrent network simulation study using reservoir computing.

Sentence processing takes place in real-time. Previous words in the sentence can influence the processing of the current word in the timescale of hundreds of milliseconds. Recent neurophysiological studies in humans suggest that the fronto-striatal system (frontal cortex, and striatum--the major input locus of the basal ganglia) plays a crucial role in this process.

A three-layered model of primate prefrontal cortex encodes identity and abstract categorical structure of behavioral sequences.

Categorical encoding is crucial for mastering large bodies of related sensory-motor experiences, but what is its neural substrate? In an effort to respond to this question, recent single-unit recording studies in the macaque lateral prefrontal cortex (LPFC) have demonstrated two characteristic forms of neural encoding of the sequential structure of the animal's sensory-motor experience. One population of neurons encodes the specific behavioral sequences. A second population of neurons encodes the sequence category (e.