FULL PAPER Artificial Synapses
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Flexible Artificial Synaptic Devices Based on Collagen from Fish Protein with Spike-Timing-Dependent Plasticity Niloufar Raeis-Hosseini, Youngjun Park, and Jang-Sik Lee* computation.[2,5–7] A fundamental step in construction of an artificial intelligence is to design synthetic synapses in a physical device that have the functionality of a biological neural system. Recently, synaptic activity has been emulated by using hardware-based neural networks such as complementary metal-oxide semiconductor (CMOS) analogue circuits.[8,9] Various devices have been investigated to emulate synapses based on memristors,[10–12] resistive switching memories (ReRAMs),[13–15] atomic switches,[16] transistors,[5,17,18] and hybrid memristors-transistors.[19] Memristors are nonlinear and dynamic nanoelectronics; they remember their interior resistance condition in accordance with the history of applied voltage and current.[20,21] They have the desirable properties of fast operation, good endurance, scalability to the atomic level, and the capacity to be stacked in 3D nonvolatile nanoelectronics.[22] In addition, their compatible structure with higher density leads to having lower power consumption and low energy acquisition compared to current CMOS implementations.[23,24] Among a wide range of materials utilized as components of memristive devices, natural organics are biocompatible and are candidates to supersede rigid silicon-based nanoelectronics.[25–28] Viable neuromorphic devices have emulated the brain’s plasticity and capability to recognize patterns.[29–31] Development of devices with bioresorbable materials is an essential fundamental for advanced biointegrated and implantable nanoelectronics;[32] obeying this purpose, we offer an artificial synaptic device that is compatible with biological systems. A long lasting electronic device is desirable and we opt to pave the way to make a synaptic device which is skin-compatible and works in a prescribed time to avoid infections and reoperation in implantable and surgical diagnosis electronics. Thus, a flexible and natural material-based device with a comparable performance to its rigid and permanent counterpart is favorable.[33] Building on previous successful studies of bioresorbable and decomposable electronics,[33–36] we used biocompatible electrodes and a renewable solid electrolyte as main components of the synthetic synapse. By using bioresorbable Mg electrodes, health risk, environmental pollution, and surgery cost would be eliminated.[34] We represent a reliable bioinspired neuromorphic device which is derived from a natural and waste material. The introduced biomemristor is capable to compete with its inorganic counterparts. By applying an extremely thin
Neuromorphic and cognitive computing with a capability of analyzing complicated information is explored as a new paradigm of intelligent systems. An implementation of a renewable material as an essential building block of an artificial synaptic device is suggested and a flexible and transparent synaptic device based on collagen extracted from fish skin is demonstrated. This device exhibits essential synaptic behaviors including analog memory characteristics, excitatory postsynaptic current, and pairedpulse facilitation as short-term plasticity. The brain-inspired electronic synapse undergoes incremental potentiation and depression when flat or bent. The device emulates spike-timing-dependent plasticity when stimulated by engineered pre- and post-neuron spikes with the appropriate time difference between the imposed pulses. The proposed synaptic device has the advantage of being biocompatible owing to use of Mg electrodes and collagen as a naturally abundant protein. This device has a potential to be used in flexible and implantable neuromorphic systems in the future.
1. Introduction The human brain performs comprehension, determination, recognition, and learning concurrently and with extreme energy-efficiency. It can identify patterns and process data more robustly and fault-tolerantly than any digital computer. This procedure is achieved by using an enormous amount of neurons and their synaptic connections. Neural circuits contact each other via thousands of synapses; this structure is responsible for the immense parallelism and structural flexibility of the brain.[1] Input signals (presynaptic spikes) to pre-neurons trigger multiple synapses and are transmitted by neurotransmitters to stimulate a post-neuronal output signal (post-synaptic spike). Research into emulation of human brain’s functionality in synaptic devices and neuromorphic systems aims to achieve massive neural-network parallelism.[2] Brain-inspired artificial synapses achieve highly efficient information processing.[3,4] Thus, acquiring an artificial synapse network is the main task for the hardware implementation of neuromorphic
Dr. N. Raeis-Hosseini, Y. Park, Prof. J.-S. Lee Department of Materials Science and Engineering Pohang University of Science and Technology (POSTECH) Pohang 37673, Korea E-mail:
[email protected] The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adfm.201800553.
DOI: 10.1002/adfm.201800553
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protection layer using atomic layer deposited (ALD) Al2O3 on the collagen thin film, the device passivates successfully to work as long as conventional memristors with high efficiency.[25,37] In this work, we investigated the possibility of using collagen as an active layer in a flexible neuromorphic device. We used an easy and inexpensive solution process to fabricate devices on a plastic substrate under ambient conditions. The electronic synapse is composed of collagen with magnesium electrodes that emulate synaptic functions by engineering the input pulses. Magnesium is the second most common intracellular cation which is involved in biochemical reactions. It is also the most abundant intracellular divalent cation. Mg is deposited in bones and muscle components. Moreover, 1% of total Mg in the human body is passed through the blood.[38] Therefore, having controlled amount of Mg in the human body is completely safe. On the other hand, Mg has good electrical conductivity as well as biocompatibility which motivated us to use it as a green electrode in our system. We selected magnesium which is a vital cation in body cells, due to its harmless effect as well as promising conductivity as electrode contact. The fabricated device is aimed to be renewable using magnesium electrodes and collagen layer. In our system, Mg as an electrode and collagen as a dielectric layer make a completely eco-friendly device. A natural material-based flexible artificial synapse is potentially applicable in medical systems, skin-attachable diagnostic electronics, and multifunctional biosensors.[39] For a commercial application, the synaptic device should have stable operation within a long period of time. On the other hand, to realize an eco-friendly wearable system which works in a prescribed time and then absorbs on the skin, biodegradable materials are considered as essential building blocks.[32,33] Bioresorbable materials as essential components of implantable devices offer surgical diagnostics and skin-compatible therapeutic electronic devices. These are adaptable to human tissue and prevent the secondary surgical operation by skin-absorbability.[40] Collagen occurs in the connective tissues of animals; it is abundant natural material and has exceptional fibril-forming properties.[41] Collagen has a triple helix structure that selfassembles by coiling of three polypeptide strands;[42] it contains numerous amino acids including glycine, proline, hydroxyproline, and alanine.[43] In this work, we investigate the feasibility of using collagenbased biomemristor as an artificial synaptic device. Four properties imply that collagen is an appropriate candidate for this application: (1) Collagen derived from fish protein is a ubiquitous and extremely low-cost biomaterial.[41] (2) Proton conduction mechanism of hydrated collagen has been elucidated by current–voltage characteristics,[44] therefore the water absorption to collagen due to solution-assisted process increases the conductivity of the film that is beneficial for use in an artificial synapse. In our system, the switching behavior is under control due to a neutral pH and proper water content. The collagen layer may be hydrated more under acidic condition by making a collagen solution in acetic acid instead of distilled water. The proton conduction mechanism occurs beyond a certain water content and protons are the main charge carriers at highly diluted collagen solutions.[44,45] (3) Collagen has the capability to form a constant, steady, and colorless thin film that is suitable for transparent organic electronics.
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(4) The fabrication cost (and therefore the overall production cost) can be reduced tremendously due to cost-effective thin film forming process.[26,27] The brain-inspired collagen-based neuromorphic device successfully emulates synaptic characteristics including potentiation and depression, excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP). The flexibility of the biomemristor makes it a candidate for use in flexible and transparent artificial nanodevices.
2. Results and Discussion The synaptic effect of the collagen-based biomemristor was studied using devices fabricated onto indium-tin-oxide (ITO)coated polyethylene terephthalate (PET) flexible substrates. A collagen solution was spin-coated on the substrate to form a solid layer with a thickness of ≈80 nm, then dried. Mg electrodes were deposited by thermal evaporation through shadow masks onto the film. An Mg/collagen layer/ITO structure was used to demonstrate the memristor with a two-terminal metal/insulator/metal (MIM) structure (Figure 1a). Helically structured collagen derived from fish skin was used as the active layer in the devices (Figure 1b). The resultant device was transparent and flexible (Figure 1c); transparency of the collagen solution and the spin-coated thin film was confirmed by light absorbance and transmittance, respectively (Figure S1, Supporting Information). Electrical pulses that are equivalent to presynaptic spikes were triggered on the top electrode (TE) to stimulate diffusion of Mg cations through the collagen helical strands and to change the conductance of the device monotonically. During electrical measurements, the presynaptic spikes were applied to the Mg-dot TEs that emulate the presynaptic layer, whereas the ITO bottom electrode (BE) was grounded to imitate operation of the neural dendrites of a post-neuron that senses transient signals via synaptic connections.[46]
2.1. Analogue Memory Characteristics To measure the analog memory characteristics, we used repeated incremental positive and negative voltage sweeps in DC mode. The results showed a pinched hysteresis loop (Figure 2a,b). A cross-sectional scanning electron microscope (SEM) image shows the well-coated active layer clearly (Figure S2, Supporting Information). The increased and reduced current in the current–voltage (I–V ) relationship represent memristive behavior in the collagen-based device and implies that the biomemristor has good ability to be potentiated and depressed, as occurs in biological synapses. Exposed to successively increasing sweep voltages, the artificial device displayed a gradual increase or decrease in current level depending on polarity of the applied voltage. Incremental increase in conductance (current) was obtained by successively increasing the positive voltages to emulate the set process of the memristive device. Repeated negative voltage sweeps caused the device to reset by a gradual decrease in the current level (Figure 2a,b).
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Figure 1. a) Schematic illustration of Mg/collagen/ITO synaptic device structures on the plastic substrate. b) Chemical structures of collagen derived from fish protein, containing glycine, proline, and hydroxyl-proline. c) Photograph of fabricated flexible artificial synaptic devices.
The “set” and “reset” processes both demonstrated a gradual change in the conductance; these changes are required for production of artificial synapses that require sequentially variable synaptic strength. Therefore, by adjusting the intensity of voltage spikes, the device current can be continuously tuned in a way that emulates dynamic change in synaptic weight of natural neurons. It worth to mention that replacing Mg top electrode with Al did not affect the device characteristics (Figure S3, Supporting Information). Majority of the works on synaptic devices focus on achieving an analog behavior that the device state is changed gradually. Memristive behavior is identified with a pinched hysteresis cycle of the I–V curve[31] and the difference between LRS and HRS affects synaptic emulation but it is not predictable due to different behaviors of the device under DC and AC bias. Synaptic conductance can be increased by applying potentiating AC pulses and decreased by applying depressing AC pulses. Due to the voltage and time reliance on ion migration, amplifying the pulse numbers in a progressive way is equivalent to programming the biomemristor at a higher voltage.[47] To potentiate the device, we applied 200 consecutive identical 2 V AC pulses to the synaptic device following a set of 200 pulses with amplitude of −3 V to depress the device; the recorded read current for potentiation and depression was 0.5 V. The normalized current response indicates that the biomemristor was monotonically regulated by a sequence of identical positive and negative pulses (Figure 2c). The gradual current tuning property denotes alteration in synaptic weight in response to a potentiating or depressing stimulus.[48] Accordingly, the synaptic weight was strengthened by positive pulses and weakened by negative pulses. The tuning of the conductance of the electronic synapse may occur because the engineered input pulses
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induce redistribution of Mg ions inside the collagen film. Another possibility is that a thin buffer layer may form above the collagen film to provide a reservoir that traps active ions and leads to sequential modification of the conductance of the collagen thin film. To have an energy efficient operation of an implantable artificial synapse with limited power supply, low power consumption is very important.[49] Neuromorphic computing with an ultralow energy consumption is essential to reach an enormously analogous information processing comparable with a natural brain. Ionic transport-based ReRAM with a small operating current (including programming, reading, and erasing currents) is a paradigm for this goal. Hybrid mixed ionic-electronic transport materials as building blocks of ReRAM-based artificial synapses are suitable to dominate electronic transport like leakage current and therefore moderate the power consumption.[50] Another effective method to reduce the power consumption is inserting a buffer layer in memory structure to limit the ion injection from top electrode during the set process and confine the Joule heating energy in the reset process.[51] A feasible method to reduce the power consumption of our suggested collagen-based neuromorphic device (the power consumption of current device is about 5 × 10−4 W) is utilizing a metal-doped collagen in the device structure. It has been reported that an Ag-doped biopolymer-based ReRAM showed improved properties and lower voltage than undoped material and therefore consumes lower energy than its undoped counterpart.[27] Another cost-effective technique is scaling down the device size using photolithography to minimize the effective area. Also refabricating the device with crossbar structure would be helpful. Producing a new bias scheme for a crossbar array would also improve the power consumption and
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read margin.[52] A mixture of collagen with another biodegradable material such as chitosan or starch or having bilayer device will be beneficial to decrease the energy consumption.[26] Moreover, the pulse mode-based power consumption can be reduced by decreasing the applied set and reset bias while switching the bio-ReRAM is another way to reach an extremely low energy consumed system.[49]
2.2. Excitatory Postsynaptic Current A presynaptic neuron spike causes ions to flow into the postsynaptic neuron and thereby provides a temporary current that is represented as synaptic strength or ionic excitatory postsynaptic potential (EPSP).[53] Similar to biological synapses, the TE and BE are regarded as the presynaptic and postsynaptic terminals, whereas the collagen thin film and ions (Mg2+) are considered as the synaptic cleft and neurotransmitters, respectively. To completely emulate the neural network, the response current is defined as the EPSC. To simplify the measurement, we replaced the EPSP with EPSC to illustrate the synaptic strength.[54] Analogous to the spike-moderated migration of the neurotransmitters in natural synapses, the migration of Mg cations in response to a presynaptic spike causes a change in the current through the synaptic device. Total EPSC (G) is determined as G = G0 + ∆G(1)
Figure 2. Current–voltage characteristics of the Mg/collagen/ITO/ PET biosynaptic device showing pinched hysteresis and gradual nonlinear transmission behavior similar to that of a biological synapse with replicated incremental positive and negative current–voltage (I–V) sweeps in a) semilogarithmic and b) linear scales. c) Device current in response to a series of positive and negative voltage stimulations showing the respective potentiation and depression of the synaptic weight. The
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where G0 is the conductance without triggering any presynaptic spike, and ΔG is the conductance change stimulated by a presynaptic spike.[55,56] To measure the EPSC, the presynaptic spike was applied on the Mg electrode with a width of 10 ms and amplitude of 2 V following a reading bias of 0.5 V. The postsynaptic current showed an abrupt leap followed by a fast decay to reach a steady state. Hence, the artificial biomemristor emulates the important operation of a biological synapse (Figure 3a). The EPSC is caused by ion migration and capacitive effects; similar to a capacitor, the artificial synapse stores the charges which are generated by migration of ions due to the applied pulses. After removing the pulse, the stored charge was declined in a short time to reach a stable state. Electrical pulses drive ion migration, but after the pulse voltage passes, the ions continue to migrate due to inertia effect. Because the ions move only a short distance, they rapidly return to their original positions. This phenomenon triggers a sharp increase of conductance with a quick decay that results in ion-facilitated EPSC.[57] Positive pulses prompted accumulation of a nonvolatile enhancement of positive charge in the biomemristor. We attribute the continuing discharge procedure and the remaining nonvolatile charge in the artificial synaptic device to ionic charges in the collagen biopolymer electrolyte.[58]
amplitude and duration of the positive voltage pulse and the negative voltage pulse are +2 V, 10 ms and −3 V, 10 ms, respectively. The current responses are read with a voltage of 0.5 V.
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2.3. Memristive Mechanism To explore the conduction mechanism of the collagen-based device, we replotted the I–V curve as logI–logV at positive and negative voltage sweep regions. The fitted curves on doublelogarithmic plots represent that the space-charge-limited conduction (SCLC) and Ohmic conduction are the main governing conduction mechanism for both of the high resistance state (HRS) and low resistance state (LRS). The HRS represented quadratic behavior (I ∼ V2), while the LRS showed a linear current response (I ∼ V) (Figure S4a,b, Supporting Information). The SCLC traps are attributed to the defects of collagen structure; these defects shape trap sites below the conduction band and the charge carriers are trapped. Occurrence of Ohmic conduction at low bias voltage is because of the inadequate electric field passing among the device; therefore, the number of inserted charge carriers are less than thermally produced charge carriers.[26,59] By increasing the voltage, trap centers are captured by abundant charge carriers and the conduction mechanism obeys the square-law dependency on voltage.[25] In the same fashion, at the backward voltage for both regions, the double logarithmic curves present an angle altering from ≈1.8 to ≈1, which designates a change from SCLC to Ohmic characteristic as the applied bias is reduced. Sufficient amount of hydrogen bonds at the amine and carboxyl groups of proline, hydroxyproline, and glycine makes helical collagen structure. The strength of the hydrogen bonds and charge distribution affect the dynamic properties of the device.[60] Randomly scattered defects assemble inside the thin film and the Mg can migrate through the defects. We speculate that the incremental increase in current during the positive voltage sweep is due to the gradual increase of ions concentration during the set process. In contrast, the gradual decrease in current is related to the progressive expansion of the Mg-depleted region during the reset process. The switching phenomena enhance the formation of highly conductive regions, which may be a result of local enrichment of Mg.[60] The charge-controlled memristor model[61] suggests that the switching layer of the proposed synaptic device contains a conducting layer of collagen film with Mg and an insulator layer without Mg. Concentrations of Mg in the biopolymer thin film were changed considerably by the voltage biases; the ionic adjustment resulted from difference of Mg concentration through the collagen. The movement of Mg in response to the electric field modified the thickness of the Mg-rich and Mg-deficient layers. Taking into account the migration and diffusion of Mg, the mechanism of device operation can be described by the previously reported model.[61]
Figure 3. Response of artificial synapse to the applied pulse. a) Excitatory postsynaptic current (EPSC); inset: EPSC triggered by a presynaptic spike (2 V, 10 ms) at reading bias 0.5 V. b) Schematic illustration of paired pulse facilitation (PPF) behavior using a pair of presynaptic spikes. c) Synaptic improvement attained by two sequentially applied pulses, imitating a biological process of PPF; the EPSC triggered by two spikes with an interspike interval time of 10 ms. A0 and A represent the amplitudes of the first and second EPSCs, respectively.
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2.4. Paired Pulse Facilitation Synaptic plasticity is the basis of learning and is a result of short-term plasticity (STP) and long-term plasticity (LTP).[62] LTP controls neuronal plasticity, circuit reorganization, and even learning and memory;[63,64] whereas, STP governs the synaptic calculations and data processing. In biological systems, PPF is a type of STP that is responsible for decoding temporal data. PPF is defined as an advancement in that response to a
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second pulse when a pair of pulses arrive in rapid sequence.[65] Therefore, during PPF, the stimulated EPSC increases if the second spike pursues the first one immediately.[5,64,66] EPSC that is evoked by a pair of sequential presynaptic spikes is schematically illustrated in the collagen-based biomemristor (Figure 3b). We applied a pair of successive presynaptic spikes (2 V, 10 ms) to the Mg electrode separated by interspike interval tpre = 10 ms. The EPSC triggered by the succeeding presynaptic spike attains a higher peak than the first presynaptic spike; this response is similar to those of biological neurons (Figure 3c). During measurement of PPF, the enhanced current is attributed to insufficient time between the stimulation pulses applied to the device. Due to the short time between successive spikes, the biomemristor cannot relax to its initial state; as a result, the observed current is amplified.[67] In neurobiology, stimulation of the presynapse by an action potential is assumed to enrich synaptic transmission due to release of neurotransmitters; this procedure improves the synaptic conduction momentarily.[68] The altered stimulus provides a limited time for the remaining calcium ions to decline to an equilibrium state.[1] Thereafter, arrival of a second identical stimulus soon after the major one augments the synaptic response as in PPF.[67] Analogously to a biological system, in the collagen-based synaptic device the EPSC does not vanish completely before the second pulse arrives; their overlap speeds up ion migration and increases the change in conductivity.[57] The further investigation on the variation of synaptic strength with different pulse intervals is conducted with ten pulse stimulations (Figure S5, Supporting Information). The Mg2+ concentration in this synaptic biomemristor shows effects that resemble the residual Ca2+ ions in biology.[69] When the subsequent spike is applied soon after the former one with a small interspike interval, the Mg2+ excited by the first spike moderately remain in the synaptic device and the subsequent spike increases the number of triggered cations and induces PPF and STP in the proposed biomemristor.
2.5. Spike-Time-Dependent Plasticity In neurobiology, memory is correlated with synaptic weights, which are strengths of synaptic connections (Figure 4, inset).[70] STDP as a biological learning process in neuromorphic learning is based on the Hebbian rule.[1] STDP is a modification of the synaptic weight in response to timing between successive action potentials.[69,71] The corresponding synapse is defined as potentiated or depressed by the Δt between pre- and postsynaptic spikes;[1] the learning function is determined by the shape of the pre- and postsynaptic action potentials.[72] Persistent augmentation of synaptic strength (long-term potentiation) happens when a presynaptic spike (pre–post pair) causes postsynaptic depolarization (Δt < 0). In contrary, a decrease of synaptic strength or long-term depression (LTD) takes place when the postsynaptic firing is applied before the presynaptic spikes (post–pre pairs) (Δt > 0).[73] The greatest change in synaptic plasticity happens in the ±10 ms scale, whereas the smallest weight modification happens when the time interval between the pre- and postsynaptic spikes is relatively long.[69,74] To illustrate the possibility of implementing STDP behavior in the proposed biomemristor, the prespike was sent to the Mg electrode, and the postspike
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Figure 4. Spike-timing-dependent plasticity. STDP of the collagen-based biomemristor; change of the synaptic weight with the relative timing Δt of the presynaptic and post–presynaptic spikes application; left inset: artificial synapse, right inset: biological synapse.
was sent to the ITO electrode. The programming voltage (Vpre – Vpost) was sent through the synaptic device at positive or negative time delay. Since the shape of the action potentials governs the STDP function, by changing the pulse scheme one can achieve a reliable STDP comparable with a biological neural system based on the Hebbian rule.[72] There are several schemes to realize the STDP; a simple way to achieve this property is applying single pulse[12] or gradually increased nonidentical pulses;[30] however, some reports use a pair of pulses to trigger the device.[75,76] To implement STDP, we imposed a pair of stimuli that consisted of a positive pulse (2 V, 10 ms) following a negative pulse (−3 V, 10 ms) on both of the pre- and postsynaptic parts of the biomemristor (Figure S6, Supporting Information). Because a synapse can be strengthened or weakened by similar spike trains, we assume that synaptic plasticity is the experience-dependent behavior. We measured the conductance of the biomemeristor after the programmed pulses and made a comparison with conductance amount before applying the pulses. After bringing back the device to the beginning circumstance we repeated the measurement with differently designed pulses. The conductance change Δω is defined as[36]
∆ω = (Gt − G0 ) /G0 (2) where G0 is the device conductance before applying the pair pulses and Gt is the conductance of the biomemristor 2 s after forcing the pulses. The relation between Δt and Δω is[74]
∆ω = λ exp( −K | ∆t |)(3) where λ and κ are fitted parameters. LTP and LTD are obtained by measuring the conductance change of the device in response to the applied potentiation and depression pulses. Consequently, the memristor stores the timing information as a
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Figure 5. Measurement of electrical characteristics under the bent state. I–V characteristics of Mg/collagen/ITO/PET in a) flat condition and b) under tensile bending with a radius of curvature of 7 mm; inset: photograph of the flat and bent device used for measurement, respectively.
for LTD, where A is the amplitude and τ is the decay constant; the positive and negative upper signs represent parameters for potentiation and depression, respectively. Stimulation circumstances strongly affect the amplitude and sign of the change in synaptic weight.[78] Correlated pre- and postspikes moderate the stored ions in the collagen chains, and thereby cause nonvolatile intensification or deterioration of the artificial synaptic device and yield STDP. The temporal properties of the biomemristor are affected by the ionic mobility in the collagenbased biopolymer by temporally correlated pre- and postspikes. To confirm the LTP characteristic, we applied two consecutive identical pulses (2 V and 2 V) in the same way of PPF measurement and two consecutive different pulses (2 V and 3 V) with 10 ms interval, respectively. When identical pulses (2 V and 2 V) were applied, the current was slightly increased and decayed back to its initial state. However, when different pulses (2 V and 3 V) were applied, the current increased and did not recover to its initial state, which demonstrates a nonvolatile resistance change in the device.
and learning behaviors in the human brain.[1,67] SRDP as a crucial characteristic of synaptic plasticity indicates that intensity of synaptic plasticity depends on the presynaptic spiking rate.[79] By using stimulations with different frequencies, potentiation and depression of the synapses in the neural network can get acquired. Therefore, tuning the stimulations frequency results in modification of the synaptic weight.[80] Because synapse is capable to become intense or fragile by similar spike trains, we assume that synaptic plasticity is experience-dependent behavior. It has been proved that stimulation circumstances strongly affect amplitude and sign of the synaptic weight change.[78] To demonstrate the probability of employing SDRP on the suggested device and verify the spiking rate effect on a synapse, ten identical pulses (2 V, 10 ms) with various frequencies (from 1 Hz to 50 Hz) were applied on the device following a reading pulse of 0.5 V, 10 ms after each stimulation (Figure S7a, Supporting Information). The first pulse trains with 1 Hz stimulation frequency showed a minor enhancement in the current. However, by increasing the frequency the repetitive pulses made an obvious current increase with a maximum intensification for 50 Hz pulses (Figure S7b, Supporting Information). The results validate the dependency of the strength of synaptic plasticity to the presynaptic spiking rate. This phenomenon is ascribed to the temporal collaboration between the EPSC and the spike. The elevated spike rate causes higher overlap between the EPSC and the spike which facilitates migration of Mg ions; consequently, an extreme variation in conductance level appears and the synaptic weight is successfully modified by varying the presynaptic spiking rate.[79] A comparison between previous synaptic devices and the device presented in this study is made (Table S1, Supporting Information).
2.6. Spike-Rate-Dependent Plasticity
2.7. Mechanical Flexibility
In neurobiology, memory is correlated with synaptic weights which is the strength of synaptic connections.[70] It is believed that concentrations of various ions like Ca2+, Na+, Mg2+, and K+ regulate the consolidation and deteriorating of synaptic weight.[1] Ionic fluxes throughout the ion channel in synapses interchange the neurotransmitters. Consequently, regulation of ion flows in the neurons and synapses produces signal processing
To verify the mechanical flexibility of the electronic synapse, the device was bent under tensile stress with a 7 mm radius of curvature. Analogue memory characteristics were analyzed under incremental DC voltage sweeps. The device exhibited reliable synaptic characteristics under positive and negative I–V sweeps in DC mode that were comparable with those of the flat device (Figure 5a,b). To further confirm the mechanical flexibility, we
relative change of conductance, which represents the long-term nonvolatile variation of the synaptic ability. The experimental data have been fitted to an exponential function (Figure 4). An exponentially decaying function represents the synaptic strength based on STDP of biological systems[77]
ω ( t ) = A + exp( −∆t /τ + )(4) for LTP, and
ω ( t ) = − A − exp( ∆t /τ − ) (5)
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measured PPF characteristics under bent condition, resulting in similar electrical property with a device under the flat state (Figure S8, Supporting Information).
3. Conclusion We have demonstrated use of collagen to fabricate flexible, transparent, and biocompatible artificial synapse with analogue conductance properties. The flexible artificial synaptic device with a simple structure of Mg/collagen/ITO indicates reliable neuromorphic functionality for future biocompatible and biodegradable device applications. The observed gradual conductance changes in the collagen-based biomemristor successfully emulate memristor characteristics with promising neuromorphic applications. Basic functions of biological natural synapse such as EPSC, PPF, potentiation and depression, STDP, and SRDP were examined in the fabricated artificial synaptic device based on natural materials. The operational similarities of the biomemristor with a real synapse alleviate the potential development of artificial synaptic devices. The stable operation of the device was also confirmed under bent. The electrical measurements confirm the feasibility of using collagen-based electronic synapse to emulate memory transitions for synaptic systems. The result is a significant advance toward efficient analogue hardware implementation with biomaterials. Due to the biodegradable Mg electrode and naturally available collagen the proposed bioinspired synaptic device has potential as a renewable device. This study shows the feasibility of realizing a transparent, flexible, and disposable bioinspired neuromorphic systems. It also has good potential to be used as an absorbable device on human skin with beneficial biocompatibility. By further optimization of the device and reducing the operating voltage by scaling down, the artificial synapse has possible applications as implantable medical devices and in disposable electronics.
current response of the device was recorded when the ITO electrode was used as post-neuron. Optical Characterizations: A Cary 100 UV–vis spectrophotometer (Agilent Technologies, CA, USA) equipped with a transmittance accessory was used to record the optical spectrum of the samples over the wavelength range 200–800 nm. The transmittance spectra were collected from collagen-based thin films on the transparent ITO-coated substrate.
Supporting Information Supporting Information is available from the Wiley Online Library or from the author.
Acknowledgements This work was supported by National Research Foundation of Korea (NRF-2016M3D1A1027663). This work was also supported by Future Semiconductor Device Technology Development Program (10045226) funded by the Ministry of Trade, Industry and Energy (MOTIE)/Korea Semiconductor Research Consortium (KSRC). In addition, this work was partially supported by Brain Korea 21 PLUS project (Center for Creative Industrial Materials). J.-S.L. conceived and directed the research. J.-S.L. and N.R.H. designed and planned the experiment. N.R.H. performed the experiment and acquired the data. Y.J.P. participated in revision of the manuscript. N.R.H. and J.-S.L. wrote the manuscript.
Conflict of Interest The authors declare no conflict of interest.
Keywords artificial synapses, biomemristor, flexible electronics, neuromorphic devices Received: January 22, 2018 Revised: April 26, 2018 Published online:
4. Experimental Section Device Fabrication: The synaptic devices with a structure of Mg/ collagen/ITO were fabricated on PET flexible substrates. The PET substrates were ultrasonically cleaned using acetone, 2-propanol, and distilled water for 15 min, then dried under blowing N2 gas. A fish-based collagen with a low molecular weight (Kenney & Ross Limited, Canada) was dissolved (8% w/v) in distilled water and mixed under ambient temperature and constant stirring at 100 rpm for 1 h. The solution was filtered through polyvinylidene fluoride syringe filters (0.2 µm pore size); before spin-coating, the substrate was treated using UV–ozone cleaner for 600 s. The filtered solution was spin-coated on the transparent and flexible substrate at 500 rpm for 5 s and 1200 rpm for 25 s. The films were preannealed at 60 °C on a hot plate for 5 min, then completely dried in a vacuum oven for 90 min. Mg electrodes were patterned using thermal evaporation to prepare an artificial synaptic device with a 100 µm × 100 µm electrode size. Electrical Characterization: Electrical characteristics of the fabricated devices were analyzed using a semiconductor parameter analyzer (Keithley 4200SCS, USA) under ambient conditions. During the electrical measurements, the biomemrisors were placed in a probe station; DC voltage sweep was applied to the Mg electrode while the ITO electrode was grounded. For synaptic measurements, bias voltages with 10 ms duration were applied to Mg electrode as the presynapse input and the
Adv. Funct. Mater. 2018, 1800553
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