Sensory integration in the peripheral nervous system of the mollusks

Many mollusks have an extensive peripheral nervous system (PNS) with partly autonomous sensory-motor networks. In the sea slug Pleurobranchaea, a subepithelial network (SeN) of neurons is embedded in the animal’s oral veil and integrates chemotactile stimuli to output incentive and stimulus location to the central nervous system (CNS). In contrast, in octopus’ arms olfactory information at the suckers is integrated at the of sucker and associated brachial ganglia. Do sea slug SeN and octopus integrate with functionally analogous mechanisms? To explore function in the octopus’s arm, we train the animal with different odors paired with positive or negative rewards, and then record the isolated arm’s behavioral and electrophysiological responses to those stimuli. This experiment may tell whether the arm ganglia can locally store the memory.

Image

Credit

Photo by Tracy Clark

Sensory-motor control in the octopus’s arms

Octopuses’ arms are impressive in their flexibility, agility, and independence in motor control, which can inspire the design of efficient soft body robotic arms. However, neuronal mechanisms for single-arm control and multi-arm coordination are still unclear. One hypothesis for their arm control mechanism is that the PNS in the arm has independent abilities to precept and react to environmental stimuli and learn from them through a network between the brachial and sucker ganglia that integrates sensory inputs and motor control, as mentioned previously.

The second plan of this project is study of the control of arm movements by the brain. Compared with Pleurobranchaea, the octopus has a more complex brain highly subdivided into lobes with different functions. However, a similar common design between the two suggests that the octopus’s lobes may express significant functional analogies of Pleurobranchaea CNS.

Simulation of the octopus arm network

Related to the above projects, I am developing a computational model for the sensory-motor network of the octopuses’ arms in a simulated environment. This network is embedded into the peripheral nervous system on the arms and suckers so they can integrate sensory information from the surrounding environment and control the motor response directly without command from a higher unit. This de-centralized model will be able to coordinate the movement of different parts in the arm. To further develop this model, I will add learning abilities derived from short-term and long-term memory formation mechanisms in sea slug Aplysia, so the arm model can learn to distinguish different stimuli and develop spatial recognition abilities based on that.