Neural engineering with photons as synaptic transmitters.

Neuronal computation is achieved through connections of individual neurons into a larger network. To expand the repertoire of endogenous cellular communication, we developed a synthetic, photon-assisted synaptic transmission (PhAST) system. PhAST is based on luciferases and channelrhodopsins that enable the transmission of a neuronal state across space, using photons as neurotransmitters. PhAST overcomes synaptic barriers and rescues the behavioral deficit of a glutamate mutant with conditional, calcium-triggered photon emission between two neurons of the Caenorhabditis elegans nociceptive avoidance circuit. To demonstrate versatility and flexibility, we generated de novo synaptic transmission between two unconnected cells in a sexually dimorphic neuronal circuit, suppressed endogenous nocifensive response through activation of an anion channelrhodopsin and switched attractive to aversive behavior in an olfactory circuit. Finally, we applied PhAST to dissect the calcium dynamics of the temporal pattern generator in a motor circuit for ovipositioning. In summary, we established photon-based synaptic transmission that facilitates the modification of animal behavior.

Mechanosensitive body-brain interactions in Caenorhabditis elegans.

Proprioception and visceral mechanosensation provide important information about the location and deformation of the body parts in space and time. These deformations arise from muscle contraction during locomotion, but also from volume changes in organs that are subjected to stresses as a part of their physiological function. These internal morphodynamics give rise to periodic contraction-relaxation cycles with surprisingly constant amplitudes and the maintenance of these optimal driving patterns is remarkably robust against external and internal perturbations. One of the underlying reason for this robustness is an internal feedback mechanism in which specialized sensory cells and neurons signal the mechanical deformation of the inner workings of our organs, from the body to the brain, which subsequently adjust the driver to a predetermined physiological setpoint. Here, we review recent progress in the field of visceral mechanosensation and proprioception in Caenorhabditis elegans and discuss how future studies with this model can be used to gain insight into mechanosensory body-brain interactions in mammals.

An asymmetric mechanical code ciphers curvature-dependent proprioceptor activity.

A repetitive gait cycle is an archetypical component within the behavioral repertoire of many animals including humans. It originates from mechanical feedback within proprioceptors to adjust the motor program during locomotion and thus leads to a periodic orbit in a low-dimensional space. Here, we investigate the mechanics, molecules, and neurons responsible for proprioception in Caenorhabditis elegans to gain insight into how mechanosensation shapes the orbital trajectory to a well-defined limit cycle. We used genome editing, force spectroscopy, and multiscale modeling and found that alternating tension and compression with the spectrin network of a single proprioceptor encodes body posture and informs TRP-4/NOMPC and TWK-16/TREK2 homologs of mechanosensitive ion channels during locomotion. In contrast to a widely accepted model of proprioceptive "stretch" reception, we found that proprioceptors activated locally under compressive stresses in-vivo and in-vitro and propose that this property leads to compartmentalized activity within long axons delimited by curvature-dependent mechanical stresses.

Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks.

Network oscillations across and within brain areas are critical for learning and performance of memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effect on neuronal populations' spiking activity and information encoding is less known. Here, we use computational modeling to demonstrate that a shift in resonance responses can interact with oscillating input to ensure that networks of neurons properly encode new information represented in external inputs to the weights of recurrent synaptic connections. Using a neuronal network model, we find that due to an input current-dependent shift in their resonance response, individual neurons in a network will arrange their phases of firing to represent varying strengths of their respective inputs. As networks encode information, neurons fire more synchronously, and this effect limits the extent to which further "learning" (in the form of changes in synaptic strength) can occur. We also demonstrate that sequential patterns of neuronal firing can be accurately stored in the network; these sequences are later reproduced without external input (in the context of subthreshold oscillations) in both the forward and reverse directions (as has been observed following learning in vivo). To test whether a similar mechanism could act in vivo, we show that periodic stimulation of hippocampal neurons coordinates network activity and functional connectivity in a frequency-dependent manner. We conclude that resonance with subthreshold oscillations provides a plausible network-level mechanism to accurately encode and retrieve information without overstrengthening connections between neurons.