Functional plasticity in neural circuits: from Cnidaria to excitable and Boolean models

• Applicants: Dr. Jan Bielecki, Ph.D.; Professor Dr. Claus Christian Hilgetag; Professorin Dr. Heike Siebert
• Subject Area: Electronic Semiconductors, Components and Circuits, Integrated Systems, Sensor Technology, Theoretical Electrical Engineering ; Cognitive, Systems and Behavioural Neurobiology
• Term: since 2026
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 567357102

This project investigates the function and plasticity of basic neural circuits by analyzing their network organization, activity and reorganization in response to sensory inputs. It complements the investigation of empirical neuronal networks in the cubozoan (box jellyfish) Tripedalia cystophora by systematic computational modeling of plasticity mechanisms in excitable and Boolean networks, with the aim of describing fundamental plasticity mechanisms that can also be utilized in the design of novel neuromorphic devices. We previously uncovered associative learning in T. cystophora by applying a classical conditioning paradigm. The basal neural circuitry in this species suggests that learning is a fundamental property of neurons or small neuronal circuits. Based on this finding, we will now more systematically explore visual information processing in the nervous system of T. cystophora, which is evolutionarily ancient, but features rich behavioral patterns and fast and specialized sensory processing. Concretely, the project will focus on the network organization, function, and plasticity of visual information processing in the T. cystophora rhopalial nervous system (RNS). We will analyze RNS network activity both at rest and in response to diverse elemental visual stimuli and explore the functional network reorganization during plastic adaptation. We will experimentally study the cubozoan nervous system using calcium imaging of neuronal activity combined with extracellular electrophysiological recordings of the RNS motor signal. We will also determine the temporal resolution of plastic changes in the RNS to understand how neural circuits, plasticity, and external sensory input interact to generate specific behaviors. Computational network reconstruction and excitable neuron models with synchronous or asynchronous updating will be pursued alongside the experimental work, to understand how network topology influences a network’s computational and functional properties, which in turn influence plasticity and learning. More generally, we aim to identify plasticity rules that drive network topology towards complex, nonregular features. Our central goal is to explore how comparatively simple neuronal networks can create fast and robust behavior. We will collaborate with the other projects in this package to leverage more detailed neural models and translate biological networks into circuit designs. Ultimately, we aim to identify design principles for functional neuronal networks to guide the engineering of novel neurotronic circuits.

• DFG Programme: Research Grants