Introduction

The boundary between electronics and biosystems is an increasingly important research area1. On one hand, biosystems provide enormous inspiration for advancing electronics through functional emulations2,3,4; on the other hand, bio-emulated electronics are often used to interface with biosystems to gain further biological understanding or improve biological functions5,6,7. These two directions often benefit each other mutually. A notable example is the development of memristors and associated neuromorphic electronics. The analog conductance modulation in nonvolatile memristors was initially used to emulate synaptic plasticity8, which was later incorporated into neural computing networks9,10,11. Similarly, the spontaneous conductance relaxation in some volatile memristors has been employed for constructing basic integrate-and-fire neuronal functions12,13, with potential applications for spiking neural networks. Concurrently, the bio-emulated functions of these neuromorphic devices make them promising candidates for improving signal translation in bioelectronic interfaces14,15,16,17,18,19.

Developing artificial neurons with improved functionalities is of particular interest because neurons inherently possess rich computing capabilities12,13. Expanding the functionality of artificial neurons to more closely match their biological counterparts can lead to more efficient signal processing with reduced circuitry and energy consumption13,14, which is especially beneficial for bioelectronic interfaces. To achieve this, artificial neurons have been constructed using various devices to emulate basic neuronal functions, such as spiking generation and signal integration12,13,14,18,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35. The integration functions are further utilized to process bioelectronic signals from environmental, bodily, and physiological stimuli for bio-realistic interpretation14,15,16,17,18,31,32,33,34,35.

Despite advancements in functional emulation, a significant gap remains between artificial neurons and their biological counterparts. Specifically, biological neurons use ultralow signal amplitudes (e.g., action potentials of 70–130 mV)36, whereas demonstrated artificial neurons work with amplitudes ≥0.5 V12,18,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35. The ultralow amplitudes facilitate seamless signal flow between sensory and computing functions in biosystems, enabling exceptional processing efficiency. Therefore, achieving parameter matching is crucial for enhancing efficiency in bio-emulated/integrated systems, including improving signal translation in bioelectronic interfaces. Recently, memristors with ultralow operating voltages were used to create artificial neuronal components, demonstrating that bio-amplitude signals (e.g., <130 mV) could induce state changes16,25,32,37. The state change was further manifested by threshold event of a current spike mimicking neuronal firing32. However, the discontinued (one-time) current spike still differs from repeated voltage spikes seen in actual neuronal firing, limiting the potential for signal cascading and realistic bioelectronic interactions.

We demonstrate artificial neurons capable of integrating bio-amplitude signals and producing continuous voltage spikes that resemble action potentials. The artificial neurons are built from a type of memristor uniquely designed to operate with ultralow voltage and current signals. The construct incorporates components that can fundamentally emulate key dynamic processes involved in neuronal firing. As a result, these artificial neurons achieve not only close functional emulation but also parameter matching in crucial aspects such as signal amplitude, spiking energy, temporal features, frequency response, and dynamics tuning. Moreover, these artificial neurons can be integrated with chemical sensors to emulate neuromodulation by extracellular substances (e.g., ions and neurotransmitters) in a manner consistent with biological neurons. Furthermore, we demonstrate that an artificial neuron can connect to a biological cell to process cellular signals in real time and interpret cell states. These advancements enhance the potential for constructing bio-emulated electronics to improve bioelectronic interface and neuromorphic integration.

Results

The constituent bio-amplitude memristor

A biological neuron can be stimulated by injected excitatory postsynaptic currents (EPSCs) to raise its intracellular charge (Q), thus the membrane potential (Fig. 1a, top panel). The charge accumulation () is the competing result of EPSC injection (I) and membrane current leakage (I’), or . Upon a certain threshold, it triggers the broad opening of sodium channels for Na+ influx to quickly raise the membrane potential, forming the fast depolarization in an action potential36. Coincidently, the atomic accumulation () of the filament in a memristor can be similarly viewed as the competing result of the ionic current injection (IM+) and leaky current (I’M+) diffusing outward the filamentary volume (Fig. 1a, bottom panel), or . As a result, the atomic integration process in a memristor can mimic the charge integration in a neuron25. The eventual filament bridging is like triggered depolarization. At the peak of depolarization, the sodium channels are self-deactivated for entering into the repolarization phase36. Although self-deactivation is absent in a memristor, the instability of filament in some volatile memristors can be utilized for facilitation21. These inherent dynamics of filamentary volatile memristors make them promising candidates for emulating the integration function of biological neurons12,13.

Fig. 1: Constructing memristor working with biological parameters.
figure 1

a (Top) Schematic of an integrate-and-fire neuron model involving EPSC injection (I) and membrane current leakage (I’). The blue curve illustrates the evolution of membrane potential by charge integration from competing I and I’. Depending on whether the integration reaches the threshold (Vth) or not, it can either elicit an action potential or fade away (dashed lines). (Bottom) Schematic of the dynamics of a metal filament formation in a memristor. The dashed lines delineate the filamentary volume. The purple dots indicate metal ions (M+). b (Top) Schematic of the memristor structure involving protein nanowires. (Bottom) Transmission electron microscope (TEM) images of protein nanowires in a sparse (upper) and dense (lower) network. Scale bars, 100 nm. c 1000 I-V sweeps from a fabricated memristor connected with a resistor (inset). d Current (orange) response in a memristor applied with a voltage pulse (blue). The amplitude of the pulse switched from 120 mV to 10 mV (as reading voltage) at t = 0.2 s.

Full size image

However, biological neurons achieve the integrate-and-fire function with very low amplitudes in key parameters, including ultralow action potential amplitudes (e.g., <130 mV) that serve as the fundamental processing signal. The efficient charge selectivity across the cell membrane also ensures that an injected current as low as several nanoamperes (nA) can generate sufficient potential to elicit an action potential36. These parameters result in ultralow spiking energy (e.g., 0.3–100 pJ)38,39,40, which ensures biocomputational efficiency and maintains a safe, non-reactive electrochemical environment. To achieve parameter matching in an artificial neuron, one conceivable approach is to use a memristor with functional parameters (e.g., voltage, current) that fall within biological ranges. Nevertheless, most filamentary memristors operate at voltages >0.5 V. Among the few that achieved bio-amplitude voltages, the reported working currents were typically >1 µA14,41.

We previously demonstrated that thin films assembled from protein nanowires harvested from the microbe Geobacter sulfurreducens can be used in device applications due to their excellent stability25,42,43,44, which is attributed to their design as extracellular structures in natural environments. Their molecular size (e.g., 2–3 nm diameter) ensures that the assembled thin film has a dense structure similar to that of a conventional dielectric (Fig. 1b, bottom). Specifically, they are designed to facilitate the microbe’s charge exchange involved in redox processes42. Introducing the protein nanowires into an Ag-based memristor significantly reduced the functional voltage to bio-amplitude regime25, although the switching performance under ultralow current has not yet been studied. Therefore, we constructed memristors with a similar device structure (top, Fig. 1b; Supplementary Fig. 1) to study switching behavior with current injections at biological levels (e.g., 2 nA). To restrict the current, a series resistor (28 MΩ) was connected to the memristor. A continuous series of 1000 voltage sweeps (0→120 mV→0) were applied to the device (Fig. 1c). The recorded I-V curves reveal several key features desirable for constructing artificial neurons. First, the memristor consistently switched to On states at voltages of ~60 mV and current levels of ~1.7 nA. Both values fall within biological ranges36, supporting the feasibility of constructing artificial neurons with parameters that match biological ones. The Off states maintained a resistance ~200 MΩ, close to the high membrane resistance in cells (e.g., 50–300 MΩ)45. Second, the I-V curves consistently started with an Off state during the consecutive sweeps, showing characteristic volatile switching. The volatility can facilitate the emulation of sodium-channel closure during repolarization. Third, the continuous sweeps yielded narrow distributions of switching voltages (e.g., 60 ± 3 mV S.D.) and currents (e.g., 1.76 ± 0.06 nA S.D.), demonstrating a stability not achieved in other bio-amplitude memristors14. Statistics across different devices showed consistent narrow distributions of these parameters (Supplementary Fig. 2). The switching stability is also crucial for constructing artificial neurons with consistent firing characteristics.

We further examined the switching dynamics of the memristor using pulsed signals (Fig. 1d). Note that the current compliance was lifted due to reduced current resolution in pulsed measurements. When a 120-mV input was applied, the memristor exhibited an integration period before transitioning to the On state (t ~ 0.13 s). As discussed before, this integration period reflects the atomic accumulation in the forming filament, which can emulate the charge integration process in a neuron (Fig. 1a). After the pulse ended (t ~ 0.2 s; followed by a 10-mV reading pulse), the memristor exhibited a delay before transitioning to the Off state (t ~ 0.21 s), indicating a spontaneous filament rupture. This filament rupture requires the cessation of external input, which does not fully replicate the self-regulated closure of the sodium channels during repolarization36. Consequently, previously constructed artificial neurons by directly employing the switching dynamics could not relax to a rest state for continuous firing21,25,32.

Bio-amplitude artificial neuron

To fully utilize the memristor’s multiple bio-amplitude functional parameters and overcome previous limitations, we constructed an artificial neuron by integrating the memristor with an RC circuit (Fig. 2a). Voltage pulses (120 mV, 5 ms) at physiologically relevant frequencies (10–100 Hz)46 were used as emulated action potential input. Unlike previous artificial neurons that could only produce a current output21,25,32, our design employs the voltage across the capacitor as output (Vo), enabling a voltage-to-voltage signal translation similar to that in biological neurons