Invasive spike-based Brain-Computer Interfaces (BCIs) based on implantable neural interfaces have shown great potential for neural prostheses1,2,3. Currently, spike processing is typically managed by digital Von Neumann-based hardware running statistical algorithms. However, neuromorphic electronic devices and architectures represent a fascinating computational alternative, by virtue of relying on near-biological spike signals and processing strategies4,5,6. In this context, recent findings that nanoscale memristors can emulate plasticity properties of synapses7,8 have, on the one hand, boosted hopes of delivering computing systems that are closer to the brain circuits in terms of computation capacity and power efficiency9,10. On the other hand, they created the premise for BCIs where spikes are seamlessly processed by nanoscale physical elements, as recently demonstrated for the encoding and sorting of spikes recorded by large-scale multielectrode arrays from neurons in culture11. Thus, in perspective, neuroelectronic systems with memristors are promising to ultimately deliver neuromorphic BCIs where silicon and brain neurons are intertwined, sharing signal transmission and processing rules with application in neuroprosthetics5 and bioelectronic medicines12.

We hereby demonstrate two memristive connections that link silicon spiking neurons and brain neurons in both directions. The connections emulate synaptic function. In the silicon-to-brain path, a TiO x memristor was coupled to a metal-thin film TiO 2 microelectrode to connect a very-large-scale-integration (VLSI) spiking neuron to a biological neuron from a rat hippocampus in culture (Fig. 1a). The link, referred to as artificial-to-biological synaptor (AB syn ), was conceived to emulate both the spike transmission and plasticity processing of a brain synapse. The memristor MR1 stores synaptic weights as resistive states. The thin film capacitive microelectrode13 CME delivers stimuli to the biological neuron (BN) that are adjusted by the memristive weights (Fig. 1b). Thus, in analogy with a native synapse, AB syn operates by injecting in the BN an excitatory current, which reflects a plasticity-dependent synaptic strength. To emulate plasticity, the memristor MR1 is operated as a two-terminal device through a control system that receives pre- and post-synaptic depolarisations from one silicon neuron (AN pre ) and one biological neuron (BN), respectively. The plasticity rule is implemented in software, and programming pulses are delivered to change the internal resistance of the device. Resistive states (weights) are translated into adjustable voltage stimuli that, through CME, produce postsynaptic depolarisations in the biological neuron (Fig. 1b and Supplementary Fig. 1). Notably, these capacitively-induced depolarisations resemble native excitatory-postsynaptic potentials (EPSPs), eventually leading to spike firing when the biological cell threshold is exceeded (Supplementary Fig. 1). We conceived the biological-to-artificial synaptor (BA syn ) following a similar approach. BN spikes are recorded by a patch-clamp microelectrode, then processed by the plasticity-driven memristor MR2, and finally transmitted to a second silicon neuron, AN post , via current injection. This configuration comprises a representative example of hybrid circuit connecting silicon spiking neurons to a biological neuron and illustrates how an artificial neuron can influence the firing of another artificial neuron through a biological intermediary without any externally forced signals along the route. In summary, along the forward pathway, the artificial ‘presynaptic’ neuron AN pre excited BN through AB syn . Through the return branch, BN stimulated the ‘postsynaptic’ silicon neuron AN post through BA syn (Fig. 1).

Figure 1 Synaptors connect silicon and brain neurons in hybrid network. (a) Sketch of the main components of the hybrid circuit and of the synaptors. AN pre and AN post are silicon spiking neurons of a VLSI network28,35 (SNN), while MR1 and MR2 are Pt/TiO x /Pt memristors36. The capacitive Al/TiO 2 electrode, CME, is an element of the multi electrode array, CMEA (Supplementary Fig. 1) where rat hippocampal neurons are cultured on the functionalized surface of the TiO 2 thin film. One neuron is contacted by a patch-clamp pipette, P, for intracellular whole-cell recording. The two synaptors, AB syn and BA syn , connect the ‘presynaptic’ silicon neuron (AN pre ) to the brain neuron (BN), and BN to the ‘postsynaptic’ silicon neuron, AN post . The two memristors, MR1 and MR2, emulate plasticity in the two synaptors, whereas electronics-to-BN and BN-to-electronics signal transmission are mediated by the CME and the patch-clamp electrode. (b) Operational scheme. In AB syn , changes in MR1 resistive states, R(t), are driven by AN pre and BN depolarisations rates according to an approximated BCM plasticity rule (Supplementary Table 1 and Supplementary Fig. 3) resulting in either LTP (red), LTD (blue) or no change. MR1 resistive states are translated into weighted voltage stimuli. These are delivered to BN through the CME capacitance (C CME ) causing EPSP-like depolarisations, in turn leading to action potential firing (Supplementary Fig. 1). Similarly, in BA syn , BN spikes are recorded by the patch-clamp electrode through its resistance, Rp, threshold-detected and then transmitted to AN post as current injections that are adjusted via MR2 weights. Full size image

An intriguing method for implementing the synaptors involves using the standardised interface of the Internet, which has been previously trialled for non-synaptic network communications14,15,16. We thus instantiated our example of synaptor-linked circuit in a geographically-distributed manner. Three set-ups were connected via user datagram protocol (UDP): a neuromorphic chip hosting silicon spiking neurons (located in Zurich, Switzerland), a memristor handling instrument (Southampton, UK) and a capacitive multi-electrode array with neurons of the rat hippocampus (Padova, Italy) (Supplementary Fig. 2). Notably, the artificial and biological neuron set-ups communicated exclusively via the memristor set-up (see also Supplementary Note 2), thereby univocally establishing the two synaptors. The central position of the memristor set-up within the network rendered it the de facto control centre of the entire system.

We report a demonstrative experiment where synaptor plasticity was inspired by the Schaffer collateral - CA1 neuron glutamatergic synapse and the landmark demonstration of Dudek and Bear that high frequency presynaptic activity induces long-term potentiation (LTP) whereas low frequency firing causes long-term depression (LTD)17. This behaviour can be interpreted on the basis of the Bienenstock-Cooper-Munro (BCM) theory, modelling the change of synaptic strength as dependent on the product of the input presynaptic activity and a function of the postsynaptic response with a modification threshold accounting for the transition between the two plasticity polarities17,18 (Supplementary Fig. 3). For the sake of simplicity, we implemented an approximation of the BCM theory with a constant plasticity modification threshold (i.e. following a Cooper, Liberman and Oja approach18), and by splitting plasticity polarities across three frequency ranges (Supplementary Fig. 3 and Supplementary Table 1). During the experiment, the silicon neuron was acting as a pacemaker. Inspired by the Dudek and Bear experimental paradigm, we set AN pre firing at constant frequencies leading to plasticity changes that were driven by post-synaptic (i.e., BN) activity. In practice, postsynaptic activity was estimated, in terms of depolarisation frequency as measured within a time window immediately preceding each presynaptic spike. Memristor weights were then programmed accordingly (Supplementary Fig. 3b). At the start of the experiment, BN spiking was elicited through AB syn by setting the silicon neuron AN pre to fire at high-frequency, eventually leading to LTP of the synaptor. As such, the protocol emulated LTP induction in the Schaffer collateral-CA1 neuron synapse by high frequency discharge of the presynaptic CA3 neuron17. It should be noted that following a rate-coded – and not phase-coded– plasticity rule provided a certain degree of immunity against physical and location-dependent internet delays in this experiment, as the specific timing of spikes was secondary in importance to the overall rate.

Experimental results are summarized in Fig. 2. The pacemaker neuron AN pre was set to fire regularly at different rates during four subsequent phases of the experiment (i.e., at 10, 25, 10 and 4 Hz, lasting 20, 20, 20 and 40 seconds each). This protocol was designed to cause polarity changes at AB syn along the pattern ‘none/LTP/none/LTD’ as depicted in Fig. 2a and in accordance with the plasticity rule of Supplementary Table 1. BN spikes recorded by the patch-clamp pipette are shown in Fig. 2b (for amplitudes of subthreshold postsynaptic potentials see Supplementary Fig. 4). Spikes triggered in BN during the high rate 25 Hz discharge of the pacemaker neuron confirmed AB syn potentiation. Consistent with LTP induction, BN spiking activity persisted during the subsequent phase at 10 Hz presynaptic frequency, thus witnessing no change of plasticity polarity. The subsequent setting of the pacemaker to a low frequency (4 Hz) then caused first depotentiation and eventually LTD of AB syn . The resistance of the AB syn memristor, MR1, is plotted in Fig. 2 throughout the different phases of the experiment. The evolution of MR1 resistance during the experiment demonstrates the potentiation of synaptor weight (i.e. increase in resistance) during the LTP phase, its maintenance during the ‘none’ phase, and the depotentiation (return of resistance to baseline) and subsequent depression (below starting baseline) during the LTD phase.

Figure 2 AB syn plasticity in geographically distributed hybrid circuit. (a) Activity pattern of the pacemaker artificial neuron AN pre . Firing frequency is modulated in four phases, targeting the induction of plasticity as per the sequence: none/LTP/none/LTD, using the chosen plasticity rule. (b) BN firing response to AB syn inputs. After LTP induction, the origianl 10 Hz pacemaker stimulation becomes capable of eliciting BN action potentials, thus reflecting the increase of postsynaptic potential amplitudes to above therhold. Firing persists until the commencement of the depotentiation/depression phase. (c) MR2 weight evolution. Data points denote resistance values for the intended LTP (red), LTD (blue) or no polarity change (black) phases. The right vertical axis indicates the correpsonding weight. X-axis common to all panels. Full size image

Results from the return, biological-to-artificial branch of the circuit –where BN was connected through BA syn to its post-synaptic target, the silicon neuron AN post – are shown in Fig. 3. Importantly, in order to favour plasticity modulation at BA syn , the (artificial) spiking neural network (SNN) environment of AN post (i.e. the artificial neurons-on-chip that AN post connects to) was set to induce a stable background rate of spontaneous firing within AN post . Stable AN post spontaneous activity is visible during both LTD phases of BA syn (Fig. 3b). BN firing, induced by AB syn potentiation, then triggers additional AN post activity during the ‘no plasticity’ phase of the run, with BN and AN post becoming synchronized (Fig. 3b). The weight evolution of the MR2 memristor (Fig. 3c) is characterised by a dominant depression trend as the low-rate spontaneous activities of BN and AN post hampered LTP induction at BA syn . Only during the brief epochs of sustained BN firing the polarity of plasticity changed (black data points in Fig. 3c), thus favouring temporal summation of high-frequency BA syn inputs leading to spikes triggering and synchronization of the two neurons.