Modeling Nonlinear Synaptic Dynamics: A Laguerre-Volterra Network Framework for Improved Computational Efficiency in Large Scale Simulations

Bridging Hierarchies in Multi-Scale Models of Neural Systems: Look-Up Tables Enable Computationally Efficient Simulations of Non-linear Synaptic Dynamics

Synapses are critical actors of neuronal transmission as they form the basis of chemical communication between neurons. Accurate computational models of synaptic dynamics may prove important in elucidating emergent properties across hierarchical scales. Yet, in large-scale neuronal network simulations, synapses are often modeled as highly simplified linear exponential functions due to their small computational footprint. However, these models cannot capture the complex non-linear dynamics that biological synapses exhibit and thus, are insufficient in representing synaptic behavior accurately. Existing detailed mechanistic synapse models can replicate these non-linear dynamics by modeling the underlying kinetics of biological synapses, but their high complexity prevents them from being a suitable option in large-scale models due to long simulation times. This motivates the development of more parsimonious models that can capture the complex non-linear dynamics of synapses accurately while maintaining a minimal computational cost. We propose a look-up table approach that stores precomputed values thereby circumventing most computations at runtime and enabling extremely fast simulations for glutamatergic receptors AMPAr and NMDAr. Our results demonstrate that this methodology is capable of replicating the dynamics of biological synapses as accurately as the mechanistic synapse models while offering up to a 56-fold increase in speed. This powerful approach allows for multi-scale neuronal networks to be simulated at large scales, enabling the investigation of how low-level synaptic activity may lead to changes in high-level phenomena, such as memory and learning.

A 3D analytical ion transport model for ionic polymer metal composite actuators in large bending deformations

Ionic polymer metal composites (IPMCs) are a kind of soft electroactive polymer composites. An IPMC strip commonly has a thin polymer membrane coated with a noble metal as electrodes on both sides. Whenever an electric voltage is applied to the IPMC, it bends and whenever it is deformed, a low voltage is measurable between its electrodes, hence IPMC is an actuator as well as a sensor. They are well known for their promising features like low density, lightness, high toughness and remarkable stimulus strain, also, they have the potential for low-voltage operation while exhibiting acceptable large bending deformation. In this paper, a three-dimensional (3D), dynamic and physics-based model is presented analytically and experimentally for IPMC actuators. The model combines the ion transport dynamics within the IPMC and the bending dynamics of it as a beam under an electrical stimulation. In particular, we present an analytical model to create a relation between the input voltage and the output tip displacement of an IPMC actuator for large bending deformations. Experimental results show that the proposed model captures well the tip displacement.