Section: Overall Objectives

Overall Objectives

The goal of our research is to study the properties and computational capacities of distributed, numerical and adaptative networks, as observed in neuronal systems. In this context, we aim to understand how complex high level properties may emerge from such complex systems including their dynamical aspects. Three main scales of description of neural mechanisms are usually studied in Neuroscience, namely neurons, populations and behaviors.

1. Neurons: At the microscopic level, precise and realistic models of neurons and of the related dynamics are defined, analyzing the neural code in small networks of spiking neurons (cf. §  3.2 ).

2. Population of neurons: At the mesoscopic level, the characteristics of a local circuit are integrated in a high level unit of computation, i.e. a dynamic neural field (cf. §  3.3 ). This level of description allows to study larger neuronal systems, such as cerebral maps, as observed in sensori-motor loops.

3. Higher level functions: At the macroscopic level, the analysis of physiological signals and psychometric data is to be linked to more cognitive and behavioral hints.

Previously involved in the study of neural computations at these different levels of description around four major research lines (spiking neurons, dynamic neural fields, higher level functions, embodied and embedded neural systems), the Cortex team has recently drastically evolved by splitting in three parts. The Mnemosyne team has been created at Inria Bordeaux with three former members of Cortex, now focusing on modeling the brain as a set of situated active memories. Two other former members of Cortex have initiated the Neurosys team in LORIA, that targets multi-level modeling of neural mechanisms. Following these team creations, the Cortex team now gathers five researchers, one of them having joined the team at the end of the year (and another one having been involved in a project to build a small company). The scientific activity of the team now focuses on the previous transversal axis “Embodied and embedded neural systems”, while being still involved in the study of microscopic and mesoscopic aspects of neural computations that we use in our systems.

Our research is linked to several scientific domains described in the next section. In the domain of computer science, we generate neuromimetic paradigms of distributed spatial computation and we aim at explaining their properties, intrinsic (e.g. robustness) as well as functional (e.g. self-organization). From a cognitive science point of view, our models are used to emulate various functions (e.g. attention, olfaction, sensori-motor coordination) which are consequently fully explained by purely distributed asynchronous computations.

In order to really explore such bio-inspired computations, the key point is to remain consistent with biological and ecological constraints. Among computational constraints, computations have to be really distributed, without central clock or common memory. The emerging cognition has to be situated (cf. §  3.5 ), i.e. resulting from a real interaction in the long term with the environment. As a consequence, our models are particularly well validated with parallel architectures of computations (e.g. FPGA, clusters, cf. §  3.4 ) and embodied in systems (robots) that interact with their environment (cf. §  3.5 ).

Accordingly, two topics of research have been carried out this year.

• Understanding embodied neural systems: bio-physical modeling and embodied olfaction; somato-sensory cortex; K-cells in visuomotor tasks,

• Neuro-inspired computational models: motion detection; multimodal learning through joint dynamic neural fields; randomaly spiking dynamic neural fields.