Modeling habits as self-sustaining patterns of sensorimotor behavior

In the recent history of psychology and cognitive neuroscience, the notion of habit has been reduced to a stimulus-triggered response probability correlation. In this paper we use a computational model to present an alternative theoretical view (with some philosophical implications), where habits are seen as self-maintaining patterns of behavior that share properties in common with self-maintaining biological processes, and that inhabit a complex ecological context, including the presence and influence of other habits. Far from mechanical automatisms, this organismic and self-organizing concept of habit can overcome the dominating atomistic and statistical conceptions, and the high temporal resolution effects of situatedness, embodiment and sensorimotor loops emerge as playing a more central, subtle and complex role in the organization of behavior. The model is based on a novel "iterant deformable sensorimotor medium (IDSM)," designed such that trajectories taken through sensorimotor-space increase the likelihood that in the future, similar trajectories will be taken. We couple the IDSM to sensors and motors of a simulated robot, and show that under certain conditions, the IDSM conditions, the IDSM forms self-maintaining patterns of activity that operate across the IDSM, the robot's body, and the environment. We present various environments and the resulting habits that form in them. The model acts as an abstraction of habits at a much needed sensorimotor "meso-scale" between microscopic neuron-based models and macroscopic descriptions of behavior. Finally, we discuss how this model and extensions of it can help us understand aspects of behavioral self-organization, historicity and autonomy that remain out of the scope of contemporary representationalist frameworks.

Fluorescence cross-correlation spectroscopy to study hemodynamics

[EN] In the recent years a series of optical correlation techniques have been developed in order to be able to measure flow velocity with high spatial resolution while being non-invasive in order to be employed in-vivo on biological organisms. The technique employed in my thesis work, scanning laser image correlation (SLIC), is a powerful approach for the detection of flow motions because it overcomes some limitations of the classical spectroscopy techniques. SLIC method consists in repeated laser scans over a linear pattern and on the cross correlation of the signal emitted by the excited fluorophores in different positions along the scan line. Therefore, the resulting measurements for flow velocity are really accurate.