Imaging the neural basis of locomotion

Emergent dynamics in a robotic model based on the Caenorhabditis elegans connectome

We analyze the neural dynamics and their relation with the emergent actions of a robotic vehicle that is controlled by a neural network numerical simulation based on the nervous system of the nematode Caenorhabditis elegans. The robot interacts with the environment through a sensor that transmits the information to sensory neurons, while motor neurons outputs are connected to wheels. This is enough to allow emergent robot actions in complex environments, such as avoiding collisions with obstacles. Working with robotic models makes it possible to simultaneously keep track of the dynamics of all the neurons and also register the actions of the robot in the environment in real time, while avoiding the complex technicalities of simulating a real environment. This allowed us to identify several relevant features of the neural dynamics associated with the emergent actions of the robot, some of which have already been observed in biological worms. These results suggest that some basic aspects of behaviors observed in living beings are determined by the underlying structure of the associated neural network.

An improved inverse-type Ca2+ indicator can detect putative neuronal inhibition in Caenorhabditis elegans by increasing signal intensity upon Ca2+ decrease

Sensory processing is regulated by the coordinated excitation and inhibition of neurons in neuronal circuits. The analysis of neuronal activities has greatly benefited from the recent development of genetically encoded Ca²⁺ indicators (GECIs). These molecules change their fluorescence intensities or colours in response to changing levels of Ca²⁺ and can, therefore, be used to sensitively monitor intracellular Ca²⁺ concentration, which enables the detection of neuronal excitation, including action potentials. These GECIs were developed to monitor increases in Ca²⁺ concentration; therefore, neuronal inhibition cannot be sensitively detected by these GECIs. To overcome this difficulty, we hypothesised that an inverse-type of GECI, whose fluorescence intensity increases as Ca²⁺ levels decrease, could sensitively monitor reducing intracellular Ca²⁺ concentrations. We, therefore, developed a Ca²⁺ indicator named inverse-pericam 2.0 (IP2.0) whose fluorescent intensity decreases 25-fold upon Ca²⁺ binding in vitro. Using IP2.0, we successfully detected putative neuronal inhibition by monitoring the decrease in intracellular Ca²⁺ concentration in AWC^ON and ASEL neurons in Caenorhabditis elegans. Therefore, IP2.0 is a useful tool for studying neuronal inhibition and for the detailed analysis of neuronal activities in vivo.