Neuromorphic Bioelectronics

  • Bio-Inspired electronics 

     : Synaptic electronic (Neuromorphic electronics)

A human brain is composed of neuronal networks with ~1012 neurons connected by about 1 quadrillion (1015) synapses. Synapses conduct signals between neurons in an ever-changing manner. The effect of a signal transmitted synaptically from one neuron to another can vary enormously, depending on the recent history of activity at either or both sides of the synapse, and such variations can last from milliseconds to months. Activity-dependent changes in synaptic trans-mission arise from a large number of mechanisms known collectively as synaptic plasticity.



Plasticity of synapse is key idea of human-brain memory formation and learning. Long-term changes in the transmission properties of synapses provide a physiological substrate for learning and memory, whereas short-term changes support a variety of computations. 



We are interested in developing synaptic electronic devices with low-power consumption, low-cost, and extremely high flexibility that mimics the soft human organisms.

  1. Organic core-sheath nanowire artificial synapses with femtojoule energy consumption









In this study, a long-length one-dimensional polymer nanowire was formed in a desired position and direction by using an electrohydrodynamic nanowire printing system, and the resulting nanowire was used as a postsynaptic neuron, the artificial synapse device having a similar structure and function to the vital neuron was implemented.


Because one-dimensional nanowire-based artificial synapse devices have very small diameters of nanometers and structures similar to nerve fibers (very long lengths at the meter level), the implementation of highly integrated neuro- It is easy.


An ionic gel consisting of an ionic liquid and a polymer structure is used as a synapse connecting each neuron and an ionic molecule is used as a neurotransmitter to transmit a nerve signal on a principle similar to a biological synapse.


In addition, we report the low power dissipation similar to that of biological synapses through the formation of electrical double layer structures in ionic gels, but also the typical functions of synapses, such as excitatory post-synaptic current (EPSC), paired pulse facilitation spike-voltage dependent plasticity, spike-duration dependent plasticity, and spike-timing dependent plasticity (STDP).


It achieves a very low power consumption of 1.23 fJ per synaptic stimulus, which is one third of the power consumption of synaptic devices driven to date.

2. Organometal Halide Perovskite Artificial Synapses (Neuromorphic Electronics)


Herein, we fabricate and characterize an artificial synapse made from a bromine-containing OHP, CH3NH3PbBr3. This work represents the first attempt to apply OHP to an artificial synapse. The artificial synapse emulates important synaptic characteristics in a single electronic device.


Artificial synapses in a two-terminal structure of substrate/buffer-capped conducting polymer (BCCP) electrode/OHP/top electrode were fabricated to emulate important working principles of biological synapses. Metal-dot top electrodes emulate the presynaptic membrane at which presynaptic spikes are applied. Electrical pulses that are analogous to presynaptic spikes are applied to the top electrodes to induce ion migration in the OHP matrix to modulate the conductance of the thin film. Conductive paths form in the ion-rich OHP matrix to provide paths for ion migration and charge-carrier transportation; these emulate the synaptic cleft that allows transmission of neurotransmitters. The BCCP thin film and the conductive sublayer together work as a bottom electrode, which emulate the functions of the dendrites of a postneuron to receive transient signals through the synaptic connection.

The conductance of OHP can be temporarily or persistently tuned by pulse-induced ion redistribution across the thin film, or ion injection into the BCCP layer to leave more vacancies. The BCCP layer could serve as a reservoir to trap mobile ions. These processes consecutively modulate the conductance of OHP thin film to realize multilevel state memory and thereby emulate the tunable synaptic response of natural systems


When a strong pulse or numerous pulses are applied, a fraction of ions can travel far enough to be trapped at the OHP/BCCP interface or even be injected into the BCCP and become trapped there. After the pulses, some ions drift back to their equilibrium positions, but some remain trapped at the interface and in the BCCP; consequently some of the halide sites in the OHP are vacant to form conductive paths.

The increase in conductance can be maintained for much longer time. Therefore, after the pulses, EPSC first decays due to drifting back of partial anions, but then the current level after this decay maintained for long time, due to the increased number of defect sites. This process emulates the long-term potentiation in human memory. Due to the relatively low Ea of Br− (≈0.2 eV), it migrates easily under external pulses. Even though MA+ ions have much larger Ea (≈0.8 eV) than Br−, their possible migration cannot be fully excluded so that they might also contribute to this mechanism.

The synaptic characteristics were realized by the consecutive modulation of electronic conductance that is a result of ionic migration mechanism in the organometal halide perovskite thin film. This is the first organic–inorganic hybrid perovskite artificial synapse. These properties present new resources for development of neuromorphic electronics.

3. Organic Artificial Nerve System


The distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information.We used flexible organic electronics to mimic the functions of a sensory nerve. Our artificial afferent nerve collects pressure information (1 to 80 kilopascals) from clusters of pressure sensors, converts the pressure information into action potentials (0 to 100 hertz) by using ring oscillators, and integrates the action potentials from multiple ring oscillators with a synaptic transistor. Biomimetic hierarchical structures can detect movement of an object, combine simultaneous pressure inputs, and distinguish braille characters. Furthermore, we connected our artificial afferent nerve to motor nerves to construct a hybrid bioelectronic reflex arc to actuate muscles. Our system has potential applications in neurorobotics
and neuroprosthetics.


We connected our artificial afferent nerve to biological efferent nerves of a discoid cockroach (Blaberus discoidalis) to emulate a biological reflex arc. We used
this hybrid system to demonstrate the flow of information from multiple pressure sensors
through a neuromorphic circuit to deliver biomimetic postsynaptic oscillating signals into the biological efferent nerves in a detached cockroach leg, leading to the actuation of
the tibial extensor muscle in the leg. The oscillating signals from our artificial afferent nerve elicit action potentials in nerves better than constant voltages. An increase in the amplitude and frequency of stimulation signals increases the number of activated muscle fibers and the forces generated by each muscle fiber, respectively. When we increased the intensity and duration of the stimulus application on the artificial afferent nerve, the maximum isometric contraction force of the tibial extensor muscle increased accordingly.


Inspired by state-of-the-art understanding of biological afferent nerves, we fabricated an artificial
afferent nerve based on organic devices that have multiple hotspots in the receptive field,
generate action potentials depending on the combined pressure inputs, and integrate action
potentials at a synaptic transistor. The biomimetic hierarchical structures were used to detect
the shape and movement of an object in simple cases and to distinguish braille characters. Finally,
our artificial afferent nerve was connected to biological efferent nerves to demonstrate a hybrid
bioelectronic reflex arc and control biological muscles.


1. Science Advances (2016), 2, e1501326

2. Advanced Materials (2016), 28, 5916

3. Science (2108), 360, 998