Synthetic Heterosynaptic Plasticity Enhances the Versatility of Memristive Systems Emulating Bio-synapse Structure and Function

Memristive systems occur in nature and are hallmarked via pinched hysteresis, the difference in the forward and reverse pathways for a given phenomenon. For example, neurons of the human brain are composed of synapses which apply the properties of memristance for neuronal communication, learning, and memory consolidation. Modern technology has much to gain from the characteristics of memristive systems, including lower power operation, on-chip memory, and bio-inspired computing. What is more, a relationship between memristive systems and synaptic plasticity exists and can be investigated focusing on homosynaptic and heterosynaptic plasticity. Where homosynaptic plasticity applies to interactions between neurons at a synapse, heterosynaptic plasticity applies to an interneuron, a neuron that is not a part of the synapse, that modulates the neuronal interactions of synapses located elsewhere. Here, a synthetic synapse was used to study the heterosynaptic modulatory effects of osmotic stress via macromolecular crowding in the aqueous environment, membrane defects introduced from pH-sensitive secondary membrane species, and oxidative stress via oxidation of lipid species present in the membrane. Osmotic stress lowers the voltage threshold for alamethicin ion channels via depletion interactions and transmembrane water gradients. Secondary membrane species lowered the voltage threshold for alamethicin and lower pH environments enhanced the self-interaction between alamethicin monomers in a pore upon dissolution from the membrane. Oxidative stress created lipid species that compete for space in the polar-apolar interface of the lipid bilayer, leading to pore formation extending cell-free gene expression reactions. These findings help reveal how to environmentally modulate the synthetic synapse. Harnessing the power of memristive systems to create a biological computer enables the creation of new computers capable of adaptation, self-repair, and low-power operation while maintaining powerful computing and memory storage schemes.

Portions of this dissertation have been previously published in the Journal of Physical Chemistry B.