Utilizing Neurons for Digital Logic Circuits: A Molecular Communications Analysis

Recent progress in nanomaterial-based bioelectronic devices for biocomputing system

Bioelectronic devices have received the massive attention because of their huge potential to develop the core electronic components for biocomputing system. Up to now, numerous bioelectronic devices have been reported such as biomemory and biologic gate by employment of biomolecules including metalloproteins and nucleic acids. However, the intrinsic limitations of biomolecules such as instability and low signal production hinder the development of novel bioelectronic devices capable of performing various novel computing functions. As a way to overcome these limitations, nanomaterials have the great potential and wide applicability to grant and extend the electronic functions, and improve the inherent properties from biomolecules. Accordingly, lots of nanomaterials including the conductive metal, graphene, and transition metal dichalcogenide nanomaterials are being used to develop the remarkable functional bioelectronic devices like the multi-bit biomemory and resistive random-access biomemory. This review discusses the nanomaterial-based superb bioelectronic devices including the biomemory, biologic gates, and bioprocessors. In conclusion, this review will provide the interdisciplinary information about utilization of various novel nanomaterials applicable for biocomputing system.

Analysis of the Information Capacity of Neuronal Molecular Communications under Demyelination and Remyelination

Demyelination of neurons can compromise the communication performance between the cells as the absence of myelin attenuates the action potential propagated through the axonal pathway. In this work, we propose a hybrid experimental and simulation model for analyzing the demyelination effects on neuron communication. The experiment involves locally induced demyelination using Lysolecithin and from this, a myelination index is empirically estimated from analysis of cell images. This index is then coupled with a modified Hodgkin-Huxley computational model to simulate the resulting impact that the de/myelination processes has on the signal propagation along the axon. The effects of signal degradation and transfer of neuronal information are simulated and quantified at multiple levels, and this includes (1) compartment per compartment of a single neuron, (2) bipartite synapse and the effects on the excitatory post-synaptic potential, and (3) a small network of neurons to understand how the impact of de/myelination has on the whole network. By using the myelination index in the simulation model, we can determine the level of attenuation of the action potential concerning the myelin quantity, as well as the analysis of internal signalling functions of the neurons and their impact on the overall spike firing rate. We believe that this hybrid experimental and in silico simulation model can result in a new analysis tool that can predict the gravity of the degeneration through the estimation of the spiking activity and vice-versa, which can minimize the need for specialised laboratory equipment needed for single-cell communication analysis.

LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm

LogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to this paradigm, six kinds of basic SNN binary logical operation modules and three kinds of combined logical networks based on these basic modules are implemented. Through these experiments, the rationality, cascading characteristics and the potential of building large-scale network of this paradigm are verified. This study fills in the blanks of the logical operation of SNN and provides a possible way to realize more complex machine learning capabilities.

Reconfigurable Filtering of Neuro-Spike Communications Using Synthetically Engineered Logic Circuits

High-frequency firing activity can be induced either naturally in a healthy brain as a result of the processing of sensory stimuli or as an uncontrolled synchronous activity characterizing epileptic seizures. As part of this work, we investigate how logic circuits that are engineered in neurons can be used to design spike filters, attenuating high-frequency activity in a neuronal network that can be used to minimize the effects of neurodegenerative disorders such as epilepsy. We propose a reconfigurable filter design built from small neuronal networks that behave as digital logic circuits. We developed a mathematical framework to obtain a transfer function derived from a linearization process of the Hodgkin-Huxley model. Our results suggest that individual gates working as the output of the logic circuits can be used as a reconfigurable filtering technique. Also, as part of the analysis, the analytical model showed similar levels of attenuation in the frequency domain when compared to computational simulations by fine-tuning the synaptic weight. The proposed approach can potentially lead to precise and tunable treatments for neurological conditions that are inspired by communication theory.