Session: 06-08: Bioinspired Networks and Neurons

Paper Number: 110904

110904 - Brain-Inspired Biomolecular Networks for Adaptive Sensing and Reservoir Computing 

RC is an algorithm that uses specific device properties such as nonlinearity and fading short-term plasticity (STP) to simplify classification of dynamic signals. We have previously demonstrated that individual Mz-doped DIB biomolecular synapses (BS) are suitable for use in RC classification and dynamic function learning, but little research has been done into forming smart materials made from BS networks for use in RC. An advantage of this approach is the ability to combine sensing and information processing into elements of the same network. In this work we present methods for rapid assembly and characterization of BS networks and schema for use in an RC framework, as well as hypothetical architectures for multimodal sensing.

Biomolecular structures assembled using the droplet interface bilayer (DIB) technique of lipid coated aqueous droplets in a hydrophobic medium allow construction of modular aqueous compartments separated by artificial lipid membranes. These membranes can be functionalized with active biomolecules, including but not limited to transmembrane proteins, to enable smart materials for sensing and information processing. When doped with alamethicin (Alm) or monazomycin (Mz), DIBs become volatile memristors that exhibit voltage-dependent short-term synaptic plasticity (STP). This includes sensory adaptation (SA), a form of STP crucial to neurosensory processing wherein a stimulus of constant strength elicits an increase followed by a decrease in response strength. This behavior is rare in neuromorphic devices and has led to their use as artificial synapses for neuromorphic hardware implementations of artificial intelligence algorithms, such as reservoir computing (RC).

For assembly of BS networks, we develop a multi-welled “egg crate” style structure to hold droplets stationary to both maintain contact with neighboring droplets and electrodes in the bottom of each well. These electrodes are controlled and measured by multichannel NI-DAQs. Tested network structures will include linear networks, multilayer perceptrons of linear layers, and hexagonally packed clusters. To test the RC capability of each network, we use the UCI Epileptic Seizure Recognition Data Set to stimulate the reservoir. Additionally, using real world sensor data from a morphing airfoil in a wind tunnel, we input these signals as voltages into the droplet network and measure the conductance states of each node to classify and predict gusting behavior. For a visual task, we classify a custom set of 5x5 digits. We hypothesize that using a combination of Alm and Mz will yield higher accuracy than either alone in most cases.

The above cases represent an electrical task, a mechanosensitive task, and a light sensitive task. Due to the customizability of individual compartment contents, sensing functions can be assigned to specific network locations by including thermoreceptors, mechanoreceptors, etc. into those droplets, but not others. In the future, these signals can be directly processed and classified in materio by including the relevant transmembrane sensing channels used in organisms. Ongoing work seeks to stabilize these networks into devices that may be used in-device.

Presenting Author: Joshua Maraj University of Tennessee

Presenting Author Biography: Bioinspired Materials and Transduction Laboratory
Mechanical, Aerospace, and Biomedical Engineering
University of Tennessee Knoxville