Decoding Neural Representation of Navigation by Mathematical Modeling and Quantitative Data
ICERM - January 2024
By Yuki Tsukada
The navigational behavior of an animal consists of several steps of information processing, including the detection of the surrounding environment, integration of multi-modal sensation, and generation of actions. Whole steps are computed in a neural circuit, or a group of interconnected neurons that work together to carry out a specific function. Understanding how each navigation process is implemented in a neural circuit is a big challenge in neuroscience. Because ensembles of the neural dynamics are only partially observed due to technical limitations, a combination of theoretical and experimental approaches is necessary to tackle this challenge.
As a theoretical approach, identifying an input/output relationship of a system with mathematical representation constructs a solid basis for understanding a dynamic system like a sensory neural circuit. Such an approach called system identification has been particularly useful in sensory neuroscience since the strictly regulated input signal enables researchers to identify the response of the target sensory neuron.
From the experimental side, Caenorhabditis elegans (C. elegans) provides a compact neural circuit consisting of 302 neurons and simple behavioral experiments for dissecting neural code. Thermotaxis behavior, or directional movement of the conditioned animals on a thermal gradient, provides a simple navigational behavior model including environmental sensing, memory, learning, and decision-making. The authors of “Reconstruction of Spatial Thermal Gradient Encoded in Thermosensory Neuron AFD in Caenorhabditis elegans” conducted an integrating approach to uncover neural processing mechanisms during navigation by using the thermotaxis behavior [TYN+16].
Based on their quantitative data on neural activity and migrating trajectory during the navigational behavior, they identified the response function of a thermo-sensory neuron. The identified response function enabled the reconstruction of neural activity from input temperature (environmental information), and moreover, the reconstruction/prediction of temperature environment from the monitored neural activity. The reconstruction of the environmental information from the neural activity shows the reliable representation of the neural code for the outside temperature field.
Extension of the system identification approach to the whole brain neural activity data will be the next step. Furthermore, building on discussions during the ICERM workshop Neural Coding and Combinatorics, stochastic aspects of neural activity and behavior are the keys to fully involving behavioral regulation since behavior generally includes stochastic properties. Information theory is one potential tool for dealing with these aspects, and developing a pipeline applicable to the available whole brain activity will be necessary. Progress for the integrating approach to the simple animal will pave the way to uncovering the neural mechanism of higher-level animals like human beings.
To learn more about this work, you can watch Yuki Tsukada’s lecture “Decoding the neural circuit of Caenorhabditis elegans” from ICERM’s Fall 2023 Semester Program Math + Neuroscience: Strengthening the Interplay Between Theory and Mathematics.
[TYN+16] Y. Tsukada, M. Yamao, H. Naoki, T. Shimowada, N. Ohnishi, A. Kuhara, S. Ishii and I. Mori, “Reconstruction of Spatial Thermal Gradient Encoded in Thermosensory Neuron AFD in Caenorhabditis elegans,” Journal of Neuroscience 36 (9) (2016), 2571-2581, doi: https://doi.org/10.1523/JNEUROSCI.2837-15.2016