Neural modeling of the cortical processes underlying imitative behaviors

Imitation is the ability to recognize, learn and reproduce the actions of others. In addition to facilitating the transmission of knowledge and skills, it has been suggested that this fundamental cognitive capacity is at the origin of other human faculties such as action understanding, empathy, mind-reading and language. Although the behavioral processes of imitation have been studied for a century in ethology and psychology, neuroscience has only started being interested in, stimulated by the discovery of a common neural substrate devoted to the recognition and production of actions, namely, the mirror neuron system. However, the existence of this system does not provide a comprehensive answer to all questions about imitation. For instance, as mirror neurons, which fire both during the observation and the execution of actions, were found in the monkey, how could the behavioral differences reported in humans be explained in terms of cortical functions and connectivity? Moreover, because of the existence of this common neural substrate, which is activated irrespectively of who is performing an action, how do humans not confuse their own actions with those of others? And finally, how could different imitative strategies and imitation levels be produced by the brain according to behavioral and neurophysiological constraints? In order to shed some light on these key questions, the approach adopted in this work is to apply the principles of computational neuroscience by developing neural models of the cortical networks involved in imitation processes. In this thesis, the use of artificial neural networks is considered a fundamental methodology. More specifically, neural fields were chosen to be the central modeling tools since this class of neural networks have been shown to be endowed with many computational abilities, most of which were actually observed experimentally in cortical regions. Although several computational models addressing imitation have already been proposed, rare are those which consider the previously mentioned issues and especially by means of neural modeling. To fill this gap, this thesis addresses the neural mechanisms underlying several important cognitive processes. They are: a) the principle of ideomotor compatibility, by which one's own performance is influenced by observing others; b) the problem of frames of reference transformations that make it possible to put oneself into someone else's place and to simplify the sensorimotor mappings required for imitation; and c) the ability to distinguish oneself from the others to make the perceived visual signals related to oneself or to others less ambiguous. Importantly, in order to confirm or refute the neural models that are developed in this work, behavioral studies are proposed along with the predictions of the models. Finally, this thesis also provides a global model of cortical organization in which the brain pathways of imitation are highlighted. Through this synthesis, the neural processes of imitation in humans are suggested to not involve specific cortical areas only as usually believed, but instead, the sensorimotor cortex in its whole. The contributions of this thesis are basically twofold. First, the developed neural models of the cortical networks involved in imitation contribute to a better understanding of the neural mechanisms underlying this human ability. As such, it suggests several new directions and hypotheses for research in the field of both experimental psychology and neuroscience. Secondly, this thesis also contributes directly to the field of artificial neural networks by providing new technical developments on the current knowledge of the computational power of the neural field approach.

Agile in-litero experiments

Renaud Luc Richardet

In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles in peer-reviewed journals. One challenge for modern neuroinformatics is to design methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and its integration into computational models. In this thesis, we introduce novel natural language processing (NLP) models and systems to mine the neuroscientific literature. In addition to in vivo, in vitro or in silico experiments, we coin the NLP methods developed in this thesis as in litero experiments, aiming at analyzing and making accessible the extended body of neuroscientific literature. In particular, we focus on two important neuroscientific entities: brain regions and neural cells. An integrated NLP model is designed to automatically extract brain region connectivity statements from very large corpora. This system is applied to a large corpus of 25M PubMed abstracts and 600K full-text articles. Central to this system is the creation of a searchable database of brain region connectivity statements, allowing neuroscientists to gain an overview of all brain regions connected to a given region of interest. More importantly, the database enables researcher to provide feedback on connectivity results and links back to the original article sentence to provide the relevant context. The database is evaluated by neuroanatomists on real connectomics tasks (targets of Nucleus Accumbens) and results in significant effort reduction in comparison to previous manual methods (from 1 week to 2h). Subsequently, we introduce neuroNER to identify, normalize and compare instances of identify neuronsneurons in the scientific literature. Our method relies on identifying and analyzing each of the domain features used to annotate a specific neuron mention, like the morphological term 'basket' or brain region 'hippocampus'. We apply our method to the same corpus of 25M PubMed abstracts and 600K full-text articles and find over 500K unique neuron type mentions. To demonstrate the utility of our approach, we also apply our method towards cross-comparing the NeuroLex and Human Brain Project (HBP) cell type ontologies. By decoupling a neuron mention's identity into its specific compositional features, our method can successfully identify specific neuron types even if they are not explicitly listed within a predefined neuron type lexicon, thus greatly facilitating cross-laboratory studies. In order to build such large databases, several tools and infrastructureslarge-scale NLP were developed: a robust pipeline to preprocess full-text PDF articles, as well as bluima, an NLP processing pipeline specialized on neuroscience to perform text-mining at PubMed scale. During the development of those two NLP systems, we acknowledged the need for novel NLP approaches to rapidly develop custom text mining solutions. This led to the formalization of the agile text miningagile text-mining methodology to improve the communication and collaboration between subject matter experts and text miners. Agile text mining is characterized by short development cycles, frequent tasks redefinition and continuous performance monitoring through integration tests. To support our approach, we developed Sherlok, an NLP framework designed for the development of agile text mining applications.

EPFL2016

Uncovering the neural encoding of behavior in the adult Drosophila motor system

Chin-Lin Chen

One of the most important goals in neuroscience research has always been to understand how animals control their behavior. However, the long focus on the role of brain neurons in behavioral control might be missing the full story. In fact, brain-wide fluctuations in neural activity are highly correlated to the behavioral state, suggesting a closed-loop system where the behavioral command and the body state interact to interpret sensory inputs and dynamically guide action selection. However, to date, little is known about how and where information about the behavioral state is generated. In principle, information about the behavioral state is a relatively high-level encoding of the body state derived from the integration of signals from primary sensors and the local network. Local motor circuits residing in the spinal cord of mammals or the ventral nerve cord (VNC) of insects may give rise to the behavioral state and pass to the brain via brain-projecting interneurons---termed ascending neurons (ANs). However, what information ANs carry and where this information originates is still a mystery due to the technical challenges in measuring these signals in behaving animals. Therefore, it is critical to develop tools to track neuron identity and record neuronal activity during behavior to crack the functional network. In this work, a method is introduced for imaging the motor circuit in the VNC of Drosophila melanogaster. This preparation demonstrates how to retain fly limb function after dissection and allows simultaneous recording of a tethered fly's behavior and its neural activity in the VNC. Incorporating this methodology with other approaches can verify the conclusions from the experiments such as those manipulating neuronal excitability, and motivate further investigation into neuronal function in specific types of behavior. Since it has long been theorized that ANs are the cellular basis of the behavioral state information sent in the brain, the technique introduced in this work was designed to resolve questions relating to what type of information ANs convey. Here, a large-scale functional screen was performed to relate neural activity, behavior, and neuronal morphology for 247 genetically identified ANs in the adult fly. This is the first large-scale screen of neural activity in a behaving animal, and this work uncovered three fundamental features of AN populations. First, rather than low-level limb movements, ANs encode high-level behaviors like walking, grooming, and resting. Second, ANs project nearly exclusively to an integrative sensory center (AVLP) and an action selection hub (GNG). Remarkably, the encodings projected to these distinct areas reflect their potential roles in contextualizing sensory cues (AVLP) and guiding future actions (GNG). Third, the behavioral encodings of ANs are closely linked to their motor system patterning. In conclusion, this work provides an indispensable tool to study the motor system beyond the brain network by overcoming a major technical barrier in in-vivo recording of the VNC of a behaving fly. This tool was used to help resolve what is encoded by ANs as a higher-level behavioral state, suggesting that the primary cellular origin of the behavioral information that is used to modulate brain function comes from the VNC. Overall, the technique and the findings on AN encoding should inspire follow-up studies to uncover additional neural mechanisms used in adaptive behavior generation.

EPFL2022

Simple Neuron Models and Adaptation

Skander Mensi

Predicting activity of single neuron is an important part of the computational neuroscience and a great challenge. Several mathematical models exist, from the simple (one compartment and few parameters, like the SRM or the IF-type models), to the more complex (biophysical model like the Hodgkin-Huxley model). All these models have their own advantages and limitations, but no one is able to reproduce the exact behavior of real neurons. Multiple projects try to simulate complex neural networks, or even the whole brain (i.e. the blue brain project or other big network simulation). To achieve this goal it is very important that simple neuron models simulate, for a low computational cost, the precise activities of all neuron classes. It is well-known that neurons exhibit a lot of different activity patterns (from an electro-physiological point of view). Here we have focused on pyramidal neurons from the layer 5 of the neocortex. They are classified as regular spiking-cells. This cell type shows adaptation and like other neurons, refractoriness. Adaptation and refractoriness are very common neuronal activity in the brain, and so it is important to have a simple model which can reproduce this kind of activity. This project deals with two classical simple neuron models: the adaptive exponential integrate-and-fire model (AdEx) and the spike response model (SRM). We deter- mined the parameters of these two models using data generated with a detailed model, the Destexhe's model which is a HH-like model for cortical pyramidal cells, stimulated with different current injection scenarios. In a second time the model parameters have been set using data from in-vitro recordings of 4 layer 5 pyramidal rat neurons, stimulated with a sinusoid in vivo-like protocol, injected somatically in current-clamp configuration. Then we show that this type of model can capture adaptation and can reproduce the activity of neuron with a high reliability

2008