Controlling a mobile robot with a biological brain

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Some recent research is ongoing in which biological neurons are being cultured and trained to act as the brain of an interactive real world robot�thereby either completely replacing, or operating in a cooperative fashion with, a computer system. Studying such hybrid systems can provide distinct insights into the operation of biological neural structures, and therefore, such research has immediate medical implications as well as enormous potential in robotics. The main aim of the research is to assess the computational and learning capacity of dissociated cultured neuronal networks. A hybrid system incorporating closed-loop control of a mobile robot by a dissociated culture of neurons has been created. The system is flexible and allows for closed-loop operation, either with hardware robot or its software simulation. The paper provides an overview of the problem area, gives an idea of the breadth of present ongoing research, establises a new system architecture and, as an example, reports on the results of conducted experiments with real-life robots

Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models

In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli

Spatio-temporal dependencies in functional connectivity in rodent cortical cultures

Models of functional connectivity in cortical cultures on multi-electrodes arrays may aid in understanding how cognitive pathways form and improve techniques that aim to interface with neuronal systems. To enable research on such models, this study uses both data- and model-driven approaches to determine what dependencies are present in and between functional connectivity networks derived from bursts of extracellularly recorded activity. Properties of excitation in bursts were analysed using correlative techniques to assess the degree of linear dependence and then two parallel techniques were used to assess functional connectivity. Three models presenting increasing levels of spatio-temporal dependency were used to capture the dynamics of individual functional connections and their consistencies were verified using surrogate data. By comparing network-wide properties between model generated networks and functional networks from data, complex interdependencies were revealed. This indicates the persistent co-activation of neuronal pathways in spontaneous bursts, as can be found in whole brain structures

Emergence of a Small-World Functional Network in Cultured Neurons

The functional networks of cultured neurons exhibit complex network properties similar to those found in vivo. Starting from random seeding, cultures undergo significant reorganization during the initial period in vitro, yet despite providing an ideal platform for observing developmental changes in neuronal connectivity, little is known about how a complex functional network evolves from isolated neurons. In the present study, evolution of functional connectivity was estimated from correlations of spontaneous activity. Network properties were quantified using complex measures from graph theory and used to compare cultures at different stages of development during the first 5 weeks in vitro. Networks obtained from young cultures (14 days in vitro) exhibited a random topology, which evolved to a small-world topology during maturation. The topology change was accompanied by an increased presence of highly connected areas (hubs) and network efficiency increased with age. The small-world topology balances integration of network areas with segregation of specialized processing units. The emergence of such network structure in cultured neurons, despite a lack of external input, points to complex intrinsic biological mechanisms. Moreover, the functional network of cultures at mature ages is efficient and highly suited to complex processing tasks

Architecture for Living Neuronal Network Control of a mobile robot

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Research is however now ongoing in which biological neural networks are being cultured and trained to act as the brain of an interactive real world robot – thereby either completely replacing or operating in a cooperative fashion with a computer system. Studying such neural systems can give a distinct insight into biological neural structures and therefore such research has immediate medical implications. The principal aims of the present research are to assess the computational and learning capacity of dissociated cultured neuronal networks with a view to advancing network level processing of artificial neural networks. This will be approached by the creation of an artificial hybrid system (animat) involving closed loop control of a mobile robot by a dissociated culture of rat neurons. This paper details the components of the overall animat closed loop system architecture and reports on the evaluation of the results from preliminary real-life and simulated robot experiments

Modelling Emphatic Events from Non-Speech Aware Documents in Speech Based User Interfaces

Most of the every day documents we come across have been composed without any information of how to be rendered in a speech-based user interface. As a result, visual formations that might imply emphasis are being ignored by Text-to-Speech systems or text-adapting applications (screen readers) and furthermore, complex structures, such as tables, are usually being vocalized in a rough linearized form, which leads to a confusing provision of information. In this work we accommodate both cases, by altering segments of emphasis in the content text, leaving a prosodic space for the vocalization of meta-information as well. We present a model for locating emphatic events and assigning to them a custom prosodic behaviour. Events are being divided in implicit and explicit ones. We concluded that the latter requires insertions of text to the linear form of structures in order to be properly realized. A script-based framework (e-TSA Composer) that supports the manipulation of prosodic elements in response of specific meta-information has been used. Finally, a model of table vocalization using our approach shows the significant improvement of the information provision compared to commercial applications

Building Prosodic Structures in a Concept-to-Speech System

The prosodic structure of utterances in terms of breaks and tones is a significant problem in speech synthesis. In this work we present the results from models used to predict accurate and realistic prosodic structures within the context of a Concept-to-Speech system for a virtual museum guide. We have used a Natural Language Generator system for providing error-free enriched linguistic information, such as syntax and Part-of-Speech, to a Speech Synthesizer. An XML annotation has been used as a means for this transfer of linguistic data. The annotated data was used to build classification trees for the prediction of prosodic phrase breaks, pitch accents and endtones (phrase accents and boundary tones). The annotation of utterances included segmental information, ToBI marks, syntax, grammar and some domain specific features such as new/given and phrase subject/object information. The linguistic nature of the domain allowed us to carefully select the set of features and the training conditions and also to utilize speech-oriented information from the written language produced by the Natural Language Generator component, such as evidence of stress and intonational focus. A speech corpus of 516 utterances has been used for training and evaluation purposes. To optimize the generated models, we used exhaustive training upon the domain data, achieving a correlation between the observed and the predicted elements of 97.286% for phrase breaks, 99.349% for pitch accents and 99.992% for endtones

Prosody Prediction from Linguistically Enriched Documents Based on a Machine Learning Algorithm

Abstract: One of the main aspects in text-to-speech synthesis is the successful prediction of prosodic events. In this work we deal with the prediction of prosodic phrase breaks, accent tones and boundary tones from a linguistically XML-based enriched input (SOLE-ML) produced by a Natural Language Generator (NLG) system. We first extended the original specification of SOLE-ML in order for the NLG to produce a more spoken aware output providing evidence of stress and intonational focus. We then used a machine learning approach (CART) to statistically analyze documents as sequences of Part-of-Speech (POS), already given or new information, object-subject information and other domain features, in order to predict prosodic phrase breaks, accent tones and boundary tones. We applied this approach on a specific domain of Greek descriptions of museum exhibits. An important task of this work was the optimization of the set of features used for training, after which the correlation between the observed and the predicted aforementioned prosodic elements became 97,72%, 96,77 % and 100,00 % respectively. The large amount (48.03%) of untagged text in the above corpus shows that the produced trained models can be applied to plain text of the same domain as well with success