# The Memristive Implementation of the Hippocampus: A Hypothesis

Dominik Caus, Andrzej Slawek, Tomasz Mazur, Piotr Zawal, Boguslaw Baś, Konrad Szacilowski, Max Talanov, Gisya Abdi

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Dominik Caus, Andrzej Slawek, Tomasz Mazur, Piotr Zawal, Boguslaw Baś, et al. The Memristive Implementation of the Hippocampus: A Hypothesis. BioNanoScience, 2025, 15, pp.498. 10.1007/s12668-025-02124-w. hal-05216367

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# The Memristive Implementation of the Hippocampus: A Hypothesis

Dominik Caus<fnref n="1" />,<fnref n="2" /> · Andrzej Sławek<fnref n="2" /> · Tomasz Mazur<fnref n="2" /> · Piotr Zawal<fnref n="2" /> · Bogusław Baś<fnref n="1" /> · Konrad Szacłowski<fnref n="2" />,<fnref n="3" />,<fnref n="4" /> · Max Talanov<fnref n="5" />,<fnref n="6" /> · Gisya Abdi<fnref n="2" />

Received: 4 May 2025 / Accepted: 7 August 2025

© The Author(s) 2025

## Abstract

We present a hippocampus-inspired neuromorphic system based on a stochastic polycrystalline nano-fiber mesh. This system takes advantage of the inherent randomness of the material structure to create a dynamically evolving connectivity pattern that mimics the probabilistic nature of biological synaptic networks. The memristive architecture is based on polycrystalline copper-based materials derived from acetylsalicylic acid (aspirin). Three different compounds were obtained by adding axial ligands to [Cu₂(asp)₄]: benzimidazole (bimi), pyridine (py), and 1,4-diazabicyclo[2.2.2]octane (DABCO). The results showed that the memristive properties of devices based on copper complexes can be modulated with various axial ligands. Moreover, theoretical analysis predicts the possibility of mimicking hippocampal architecture via co-crystallization of hydrophobic organic molecular semiconductors with selected copper complexes. Additionally, we demonstrated that controlling the crystallization temperature and solvent composition allows precise tuning of resistive switching in methylammonium lead iodide perovskites. Together, these approaches provide a versatile foundation for implementing dynamic, brain-like memory functions in hardware.

**Keywords** Memristive implementation · Hippocampus · Memristive architecture · Stochastic polycrystalline nano-fiber mesh

## 1 Introduction

Why do we need to sleep? Humans spend on average one third of their lives sleeping. Most known animals sleep, with

rare examples of animals which are extremely resistant to sleep deprivation (like fruit fly *Drosophila melanogaster*) or undergo a long-term dormant state with metabolism slowed down (American bullfrog *Lithobates catesbeianus*). Other animals, such as narwhals (*Monodon monoceros*), dolphins (e.g., *Tursiops truncates*), and orcas (*Orcinus orca*), have developed unihemispheric sleep, which allows them to sleep with one-half of their brain at a time, while the other half remains awake [1]. In this way, they can continue to swim, breathe, and monitor their surroundings. The only animals that can survive without any form of sleep are jellyfish, sponges, and sea urchins; however, they do not have brains. Sleep-like phenomena are possible in their primitive nervous systems, but have not yet been identified. Therefore, an important question arises: Why do all mammals and other animals with complex nervous systems require sleep? Sleep serves essential cognitive functions, particularly in memory consolidation through bidirectional replay, synaptic weight up-and-down scaling, and neuromodulatory highlighting of memories. On the other hand, any of the known artificial intelligence systems does not sleep at all. Here, we can hypothesize that the implementation of the sleep function in artificial intelligence systems could result in a significant reduction in energy consumption. This should happen not on

<footnote>✉ Konrad Szacłowski
szacilow@agh.edu.pl; konrad.szacilowski@uwe.ac.uk</footnote>

<footnote>✉ Max Talanov
max.talanov@gmail.com</footnote>

<footnote>✉ Gisya Abdi
agisya@agh.edu.pl</footnote>

<footnote n="1">¹ Faculty of Materials Science and Ceramics, AGH University of Krakow, Mickiewicza 30, 30-059 Kraków, Poland</footnote>

<footnote n="2">² Academic Centre for Materials and Nanotechnology, AGH University of Krakow, Mickiewicza 30, 30-059 Kraków, Poland</footnote>

<footnote n="3">³ Unconventional Computing Laboratory, University of the West of England, Coldharbour Lane, Bristol BS16 1QY, UK</footnote>

<footnote n="4">⁴ Institut de sciences exactes et appliquées (ISEA), Université de la Nouvelle-Calédonie, Campus de Nouville, 98851 Nouméa Cedex, France</footnote>

<footnote n="5">⁵ University of Messina, Messina, Italy</footnote>

<footnote n="6">⁶ Institute for Artificial Intelligence R&D of Serbia, Novi Sad, Serbia</footnote>
the trivial ground of devices “taking rest,” but from tremendous improvement of learning functions during the sleep phase. The concept of machine sleep will also bridge the gap between biological and artificial intelligence.

## 1.1 The Role of the Hippocampus in Sleep and Memory Consolidation

Some of the hippocampal functions that we focus on in this article and are activated during sleep are (1) memory consolidation, (2) bidirectional replay, and (3) up-and-down scaling of the hippocampal weights. The information stored during the wake phase using the classical Hebbian STDP, later during the sleep consolidation process, is transferred to the cortical neuronal structures. We see the bidirectional replay that is done using the sharp wave ripples — the high-frequency oscillations that are involved in the memory consolidation process [2, 3]. Thus, due to the bidirectional nature of the replay mechanisms and the reactivation of the neuronal circuitry of CA1 and CA3 areas, as well as the exploitation of the up-and-down scaling of the synaptic weights, we hypothesize the memory optimization that could be demonstrated by route optimization, for example, of the robotic system [4, 5]. Optimization of memory could play a key role in future autonomous robotic systems operating alone, exploiting synaptic up-and-downscaling due to synaptic tagging [6, 7]. In this work, we propose a hippocampus-inspired neuromorphic system based on a stochastic polycrystalline nanofiber mesh. This system takes advantage of the inherent randomness of the material structure to create a dynamically evolving connectivity pattern that mimics the probabilistic nature of biological synaptic networks.

We propose the architecture inspired by the CA1 and CA3 areas of the hippocampus (Fig. 1), consisting of excitatory (red) and inhibitory (blue) neurons, interconnected by a polycrystalline or other assembly that is joined by supramolecular interactions. Synaptic sites (green dots) emerge at junctions where signal transmission occurs, following a probability-driven formation process (Fig. 1B). In bioplausible simulations of the hippocampal circuitry, researchers use the connectivity matrix approach that identifies the probability of connection between different types of neurons in particular areas of the hippocampal [8, 9]. This approach allows for identifying the connectivity rules, thus self-organizing the circuitry of the hippocampal attractor network. Circuitry is organized using probabilistic rules: (1) high-probability connections (thick black lines) represent stable pathways frequently used, (2) low-probability connections (dashed lines) are transient and subject to activity-dependent plasticity (Fig. 1B). The memristive architecture is organized in three layers: (1) the self-organization probabilistic rules that identify how the excitatory and inhibitory neurons are self-

<figure><img src="image_2.png" /><figcaption>**Fig. 1** The conceptual schematic of the proposed polycrystalline memristive implementation of the hippocampus. **A** The mesh with integrated neurons inhibitory and excitatory, where: **red cycles** — excitatory neurons; **blue cycles** — inhibitory neurons; **green** — synapses. **B** The graphical representation of the connectivity rules, where green spirals indicate memristive synapses with high or low probability of the connection/organization of synapses</figcaption></figure>

organizing their connection in 3D space; (2) STDP implemented on top of the polycrystalline memristors; (3) memory consolidation functions that are implemented on top of the polycrystalline STDP. The probabilistic rules of connection identify the probability that the types of neurons organize synapses depicted in Fig. 1B, where the excitatory neuron has a high probability of organizing synapses with any other neuron, while the inhibitory neurons connect only to excitatory neurons. Through this probabilistic and self-organizing connectivity model, our aim is to develop a hardware-efficient neuromorphic architecture capable of autonomous learning and memory consolidation. The stochastic nature of the polycrystalline mesh provides a foundation for energy-efficient self-adaptive networks that can potentially advance the fields of neuromorphic computing, artificial intelligence, and biologically inspired cognitive systems.

## 2 Analogous Dynamics of Hippocampal Memory Consolidation and Stochastic Heterogeneous Conductive Domains of Materials

Just as the hippocampus transfers and refines memory traces during sleep-through spike-timing-dependent plasticity (STDP) and circuit reactivation between the CA1 and CA3 regions — networks composed of heterogeneous conductive domains can emulate these dynamics via probabilistic connectivity and memristive plasticity. Materials engineered
with deliberate disorder (e.g., blends of metal complexes and polymers or networks of nanowires) exhibit emergent behaviors such as nonlinear signal propagation, percolation-induced switching, and dynamic conductance modulation. These behaviors closely resemble the encoding of temporal and spatial patterns in the hippocampal through distributed, noisy synapses.

## 2.1 Design of Materials

Replicating hippocampal functionality *in materia* requires the use of carefully engineered materials. Although it is nearly impossible to reproduce the exact physical architecture and connectivity of the hippocampus in a synthetic system, structural and functional analogs can be designed. Noisy connections to emulate spatial and temporal memories in the hippocampus can be simulated in the random junction network, recognizable in disordered 1D nanomaterials and polycrystalline (or co-crystalline) systems that are capable of introducing functional diversity and conductivity changes into memristive networks (Fig. 2). Co-crystallized materials usually exhibit emergent properties, new spectral and electrical properties resulting from intermolecular interaction [10]. When implemented with memristors, these stochastic materials can be self-organized into structures that dynamically adapt, store, and recall information in a way that mimics biological systems, particularly in the hippocampus, a brain region where memory formation and learning are governed by distributed, adaptive, and stochastic synaptic activity. The other approach may involve the addition of conductive filamentary materials (nanowires, nanotubes) to the semiconducting matrix. Harnessing the natural randomness of these materials and their domain-dependent conductivity offers a compelling strategy to physically realize hippocampal-like

computation. Embracing these disorders and defects instead of suppressing them is a new approach to providing a reasonable link between material science and brain-inspired engineering. In the following sections, we try to elaborate on this idea with more examples reported in the literature and present some experimental verification.

## 2.2 Filament Formation and Atomic-Scale Junctions in Materials

In recent years, the exploration of stochastic systems constructed from random networks of nanomaterials of one dimension (1D) (such as nanotubes, nanowires, or molecular junctions) has gained an increasing number of researchers for unconventional and neuromorphic computing [12]. These systems, unlike highly ordered circuits, rely on the inherent disorders and emergent electrical properties of randomly assembled junctions to produce complex, non-linear dynamics. The key physical principle lies in the formation of a percolating network, where conduction paths are not determined but evolve under electrical bias through local changes (e.g., ionic migration, redox activity, or capacitive charging). This randomness gives rise to unique spatio-temporal responses and noise characteristics that cannot be easily predicted but are rich with computational potential. A compelling example of such emergent behavior is seen in Ag–Ag₂S nanoparticle networks, where a dense two-dimensional mesh of silver nanowires creates up to 10⁵ atomic-scale junctions across the device area [11], (Fig. 2a). When a voltage is applied, these junctions exhibit dynamic conductance changes due to the migration of Ag⁺ ions and the reversible formation of metallic filaments. As each junction stochastically switches between the conductive and resistive states, the global network exhibits a rich non-linear

<figure><img src="image_3.png" /><figcaption>**Fig. 2** a Random junctions forming electrochemically in metal–metal complex networks, redraw with permission from ref. [11], b random junction in 1D nanowires. The tentative image of metal complex (yellow) and organic semiconductor (blue) composite before c and just after d percolation threshold</figcaption></figure>
response, including volatile memory and waveform transformation. These non-linear properties have been harnessed in physical reservoir computing systems that perform temporal pattern recognition and digit classification, mimicking key aspects of hippocampal processing such as temporal integration and recurrent dynamics. A parallel example is found in the hybrid network of single-walled carbon nanotubes (SWNTs) functionalized with porphyrin polyoxometalate complexes (Por-POM). This system, studied by Banerjee et al. [13], represents a similarly emergent behaviors: when a DC or AC voltage is applied throughout the network, non-linear current-voltage characteristics emerge, such as time-dependent current fluctuations, driven by redox-mediated charge storage in Por-POM molecules (Fig. 2). The network output exhibits 1/f<sup>γ</sup> noise and high-dimensional responses across multiple terminals, which are used to reconstruct complex waveforms or classify tactile input signals. The stochasticity and variability of junctions in the SWNT/Por-POM network lead to rich harmonic generation, mirroring how the hippocampus converts incoming neural activity into distributed, temporally encoded patterns for memory encoding and recognition. Together, these physical systems embody a core principle of hippocampal function: the transformation of low-dimensional, time-dependent inputs into high-dimensional, recurrently connected internal states via stochastic, non-linear, and dynamic mechanisms. These nanomaterial networks modulate their conductance pathways in response to voltage stimuli, yielding transient memory and strengthening or weakening their connections.

Thus, these random junction networks of Ag nanowires and SWNT/Por-POM complexes offer not only a physical analogy to the hippocampus but also a promising route toward compact and energy-efficient neuromorphic systems grounded in the physics of disordered matter (Fig. 3).

## 2.3 Polycrystalline Materials

Other strategy involves blending a memristive metal complex with a dielectric polymer. Provided that both components are soluble in a common solvent, yet immiscible in the solid phase (a condition that is generally achievable), the result is a heterogeneous network of domains with distinct electrical properties [14, 15]. These domains exhibit percolation behavior, as shown in (Fig. 2c, d) [16, 17], and form a complex network of capacitive and resistive elements whose properties depend on the component concentration ratio. As the system approaches the percolation threshold, a sharp transition in electrical behavior is expected. Alternatively, incorporating a conductive phase, such as sub-threshold concentrations of carbon nanofibers, can further modulate transport properties [18]. In this way, precise control over the nanoscale or molecular level disorder can be translated into tunable device-level functionality. Similarly to 1D and quasi-1D systems like Ag–Ag₂S nanoparticle networks, in π-conjugated polymers or co-crystals, conduction pathways form through stochastically distributed junctions. In both polycrystalline semiconductors and organic materials with extended π-conjugation, such as oligoacenes and

<figure><img src="image_4.png" /><figcaption>**Fig. 3** a Full circuit implementation of SWNT/Por-POM complex network as a non-linear system and b the non-linear plot of V<sub>out</sub> with respect to V<sub>in</sub>, with permission from ref. [13]</figcaption></figure>
supramolecular assemblies, electronic behavior is highly sen-
sitive to domain structures and junctions between ordered
regions [10, 19–22]. For polycrystalline materials, grain
boundaries represent localized disruptions in crystallinity,
leading to variations in carrier mobility and trap-assisted
recombination, which can cause spatially random conduc-
tivity across the material. Organic co-crystals and donor–ac-
ceptor (DA) complexes, much like polycrystalline semicon-
ductors, often exhibit heterogeneous conductivity due to
variations in local crystalline domains and stacking arrange-
ments. These systems rely on intermolecular charge-transfer
interactions that are highly sensitive to packing modes (e.g.,
segregated vs. mixed stacking), the degree of charge trans-
fer, and local intermolecular geometry. This phenomenal
characteristic forms non-uniform charge mobility within the
same material, similar to the stochastic junction behavior
observed in 1D materials or polycrystalline films, where
grain boundaries disrupt uniform conduction. For example,
mixed donor-acceptor columnar structures can show variable
transfer and localized conductance due to minor changes in
the separation plane or stacking degrees [21]. Similarly, in
organic co-crystals, even with the same molecular units, dif-
ferent stoichiometries and intermolecular interactions result
in significantly different electrical properties, pointing to
disordered domains and embedded inherently random con-
ductive pathways formed in these structures [10]. Thus, the
emerging picture is one of domain-dependent conductivity
fluctuation—a phenomenon well aligned with the behavior
of stochastic 1D junctions in low-dimensional systems.

These can be best achieved by mixing, in common sol-
vent, various species which will have incompatible crystal
structures in the solid phase, hence will not form solid solu-
tions. Furthermore, they need to be equipped with appropriate
functional groups that will secure the desired interaction
patterns. Metal (bismuth, tin, and lead) halide perovskites
with organic cations (especially aromatic ones, like tropy-
lium or substituted pyridinium cations), polycyclic aromatic
hydrocarbons, tetraaza[14]annulene complexes of divalent
transition metals seem to be ideal candidates [23–26]. Fur-
thermore, molecules and moieties of distinct electron donors
(triphenylene, coronene, as well as thiophene derivatives) and
electron acceptors (tetracyanoquinodimethanes, fullerenes,
aromatic nitriles) and their co-crystallized combination will
provide both tunable conductivity and band gap of organic
domains in such supramolecular composite assemblies. Each
neuromorphic device needs a source of energy. For living
systems chemical energy source is produced ATP in mito-
chondria, and electronic devices use electrical energy. Due
to the complexity and operational characteristics of the sys-
tem, electrical energy as the sole power source does not seem
feasible. The main reason is the spiking character of the net-
work and the fact that memristors (except for the case of
telegraphic noise) cannot generate spikes *per se* (but can

easily process them). This obstacle can be solved by light-
sensitive semiconducting nanoparticles, which (stimulated
by light pulses) can mimic the generation of action potential
spikes. Moreover, they can be modified at the surface with
appropriate anchoring ligands (i) providing supramolecular
integration with other components of the system, (ii) desired
special distribution in the material, and (iii) spectral-selective
responsiveness due to tunability of their band gaps and pho-
tosensitization by surface ligands.

**3 Experimental Verification**

In the ideal case, experimental verification of the “artificial
hippocampus *in materia*” should involve three aspects elabo-
rated above: (i) supramolecular assembly/co-crystallization,
(ii) control of crystallinity, and (iii) incorporation of fila-
ments in a single material. It is not an easy task; therefore,
preliminary studies have been performed in two different
molecular systems: lead halide perovskites and copper aspiri-
nate derivatives. The choice of perovskites was justified by
the popularity of these materials in memristive studies [27–
29]. Due to their highly polar character as well as lack of
conjugated aromatic moieties within the classical perovskite
structure, they have not been considered as good candidates
for precursors of supramolecular self-assembly. The polar-
ity of the lattice itself may be beneficial, as it will prevent
any formation of mixed crystals and solid solutions (crys-
talline or amorphous) with, e.g., organic semiconductors.
The lack of conjugated moieties (or other units, capable of
supramolecular assembly and at the same time contribut-
ing to transport phenomena) is a serious obstacle. Therefore,
instead of designing new types of perovskites containing
extended organic moieties with conjugated bond systems, we
referred to a new type of material, dimeric copper carboxylate
complexes, that have not been studied so far in the context
of memristive phenomena. Our attention has been focused
on aspirinate complexes which bear four aromatic rings in a
single molecule (cf inset in Fig. 4a); furthermore, they can
further bind ligands (also aromatic ones) in an axial posi-
tion. Such material should fulfill all requirements – relative
polarity, possibility of supramolecular interactions between
copper complex molecules and organic semiconductors via
π-stacking interactions. Moreover, these materials possibly
can form metallic filaments or exhibit other filament-like
behavior [23, 30] as well as may be doped with carbon nan-
otubes in an appropriate solvent system.

**3.1 Preparation of Polycrystalline Cu-Based**
**Materials**

Preliminary verification of this approach was performed
using the copper bis-aspirinate complex and its axially coor-
dinated derivatives. This complex appears to be an ideal
<figure><img src="image_5.png" /><figcaption>**Fig. 4** Absorption spectra recorded in DFM solutions of [Cu₂(asp)₄] during titration with diluted DMF solutions of pyridine (a) benzimidazole (b) and DABCO (c). Ball and stick model of molecular structure of copper aspirinate is shown as an insert in (a)</figcaption></figure>

platform for tests due to hydrolytic stability, moderate solubility in organic solvents, and the presence of aromatic moieties capable of stacking interactions and free coordination sites at axial positions. Three derivatives have been prepared in situ by the reaction of copper aspirinate with pyridine, benzimidazole, and 1,4-diazabicyclo [2.2.2] octane (DABCO) in concentrated dimethylformamide solutions, and the final products have been isolated by solvent evaporation and used without further purification. In parallel, the degree of the reaction in diluted solutions was monitored by UV-vis spectroscopy. The copper aspirinate complex is characterized by a weak absorption band at 1.77 eV (720 nm), which can be attributed to the d-d transition within the d9 copper center with a square-planar ligand field. An additional weak band at ca. 3.1 eV (400 nm) may have a ligand-metal charge transfer (LMCT) character, as copper(II) centers are significantly strong electron acceptors. The addition of pyridine to the dimethylformamide solution of copper aspirinate results in a decrease in the intensity of this band, accompanied by a significant broadening and a minor hypochromic change to 1.75 eV (709 nm) (Fig. 4a). This indicates an increase of the coordination number of copper ions. At the same time, a small decrease in intensity is observed around 3.1 eV (400 nm). Reaction with benzimidazole results in similar changes in the d-d band: the broadening is accompanied by an even higher hypochromic shift to 1.90 eV (690 nm). At the same time, a new, very strong shoulder was located at ca. 2.75 eV (400–500 nm) increases (Fig. 4b). It may be attributed to a strongly allowed LMCT transition from benzimidazole to Cu(II). Reaction with DABCO results in very similar spectral changes; however, the new CT band is of much higher intensity and the d-d band shows a significant bathochromic shift to 1.70 eV (745 nm) (Fig. 4c). Spectral changes suggest the formation of square-pyramidal complexes with N-donating ligands occupying axial positions, i.e., [Cu₂(asp)₄(py)₂], [Cu₂(asp)₄(bimi)₂], and [Cu₂(asp)₄(DABCO)₂], respectively.

The observed spectral changes are consistent with changes in the geometry of the complexes and expected changes in the electronic structure of these complexes [31, 32]. Figure 5 shows an energy diagram for three different geometries: octahedral, square planar, and tetragonal pyramid. Significant energy changes of individual orbitals are clearly visible along with differences in orbital separation. In the square planar geometry, the HOMO orbital has mostly $d_{x^2-y^2}$ character; however, upon interaction with an axial ligand, the energy of this orbital decreases and HOMO assumes the $d_z^2$ character. This, in turn, may induce a stronger electronic coupling between two copper ions in the carboxylate cage. Although it is difficult to observe it spectroscopically (even using XAS, *vide infra*), it should have some influence on electrical conductivity.

Axial ligation with aromatic ligands opens a possibility of noncovalent interactions with other aromatic molecules, e.g., organic semiconductors. Rubrene (5,6,11,12-tetraphenyltetracene) has been selected as a test molecule. Computational chemistry modelling at the B3LYP/TZVP level indicates that rubrene forms a stable supramolecular complex with the pyridine complex of copper aspirinate ligated with pyridine, i.e., [Cu₂(asp)₄(py)₂] Fig. 6a. Density Functional Theory (DFT) quantum-chemical modelling at the B3LYP/TZVP level of theory indicates the formation of an adduct through a face-to-edge interaction between pyridine hydrogen atoms and the aromatic tetracene moiety in rubrene, which acts as a hydrogen bond acceptor, which is justified by the local negative charge at the central ring Fig. 6b [33]. This weak interaction brings about only small geometrical modifications of interacting counterparts. The formation energy of this adduct has been evaluated using the B3LYP/TZVP approach to be ca. -21 kJ/mol, which is comparable with moderately strong hydrogen bonds [34], also involving aromatic moieties [35]. This interaction, results, however, in a significant reorganization of electronic structure and formation of a donor-acceptor interaction. HOMO
<figure><img src="image_6.png" /><figcaption>**Fig. 5** Energy level arrangement for different geometries of copper complexes together with HOMO orbitals calculated for [Cu₂(asp)₄] and [Cu₂(asp)₄(py)₂] complexes using DFT method at the B3LYP/TZVP level of theory</figcaption></figure>

orbital of a supramolecular assembly is localized on the rubrene part, whereas LUMO at the copper aspirinate part. This leads to a significantly reduced HOMO-LUMO gap of the supramolecular complex (0.62 eV) as compared to rubrene (2.5 eV) and pyridine-copper aspirinate complex (1.37 eV). Distribution of electrostatic potential in a supramolecular assembly (Fig. 6d) indicates a significant contribution of electrostatic interactions between aromatic electron clouds

of rubrene with positively charged rim of piridine and phenyl rings of the aspirinate moiety (Fig. 6d).

### 3.1.1 X-ray Absorption Spectroscopy (XAS)

X-ray absorption spectroscopy (XAS) is a powerful technique for probing the local electronic and structural environment of specific elements in complex materials. It provides

<figure><img src="image_7.png" /><figcaption>**Fig. 6** Geometry (a) and frontier orbitals (c) of the rubrene: [Cu₂(asp)₄(py)₂] supramolecular complex as calculated using the DFT approach at the B3LYP/TZVP level of theory. Electrostatic potential distribution within the rubrene molecule (b) and within the rubrene: [[Cu₂(asp)₄(py)₂] complex (d)]</figcaption></figure>
rich chemical information, including formal oxidation state, local coordination geometry, and subtle structural distortions. The region of X-ray absorption near the edge structure (XANES) (50–100 eV above the absorption edge) is especially sensitive to the electronic structure and ligand geometry around the absorbing atom. XANES also shows changes in oxidation state and coordination environment.

Figure 7 shows the Cu K-edge XANES for the studied copper(II) aspirinate complexes with piridine, benzimidazole, and DABCO compared to the reference acetate monohydrate. All of the spectra are almost identical. The absorption edges, determined from the maxima of the first derivative Fig. SI1, vary slightly from 8989.6 eV for $[Cu_2(asp)_4(py)_2]$ to 8990.8 eV for $[Cu_2(asp)_4(DABCO)_2]$, which is within the resolution limits of the monochromator. This implies that the local electron structure of the studied copper (II) aspirates is very similar to that of copper (II) acetate monohydrate. These compounds, despite variations in their organic ligand frameworks, exhibit notable structural similarities, particularly in the coordination geometry of copper and bridging ligand motifs, and are characterized by a distinctive paddle-wheel dimeric structure.

The experimental spectra of copper(II) acetate monohydrate and copper aspirinate were modeled using DFT calculations with the FDMNES code. As shown in Figs. SI2, SI3, the simulated spectra closely match the experimental data, showing the reliability of the computational approach. These calculations provide insight into how specific electronic transitions contribute to the XANES features by revealing the projected density of unoccupied electronic states (PDOS). In particular, both compounds exhibit similar spectral characteristics, reflecting their comparable local coordination environments and electronic structures.

<figure><img src="image_8.png" /><figcaption>**Fig. 7** Cu K-edge XANES spectra for the studied copper aspirinate complexes and copper acetate monohydrate reference</figcaption></figure>

To further investigate the structural similarities of the studied materials, we analyzed the extended X-ray absorption fine structure (EXAFS) region — the high energy (> 100 eV), oscillatory part of the spectrum — shown in E space, k space, and R space in Figs. SI4 and SI5. Even small differences in the crystal structure are usually clearly visible in EXAFS oscillations. However, the investigated materials exhibit highly similar spectral features in the high-energy region, characteristic of EXAFS contributions from photoelectron scattering by neighboring atoms. The first-shell Cu–O coordination distance is 1.78–1.81 Å in the R space (Fourier transformed EXAFS), which is slightly shorter than the actual interatomic distance of 1.96 to 1.98 Å due to the phase shift effects inherent in the EXAFS analysis.

### 3.1.2 Electrochemical Measurements

The four materials: parent $[Cu_2(asp)_4]$, as well as three ligated derivatives $[Cu_2(asp)_4(py)_2]$, $[Cu_2(asp)_4(bimi)_2]$, and $[Cu_2(asp)_4(DABCO)_2]$ have been used for the fabrication of thin-layer devices. Figure 8 shows the current-voltage characteristics of the devices made from layers of different compounds between the ITO and copper layers, where the ITO layer served as the counter electrode (CE) and the copper layer as the working electrode (WE). In the diagram, we did not show the device with $[Cu_2(asp)_4(DABCO)_2]$ as the middle layer because this complex does not show any electrical conductivity.

All devices show pinched hysteresis loops, which are characteristic of memristive devices. The best memristive behavior is exhibited by $[Cu_2(asp)_4(bimi)_2]$. The conductivity of each device is different: the highest conductivity is observed for the sample made from $[Cu_2(asp)_4(bimi)_2]$,

<figure><img src="image_9.png" /><figcaption>**Fig. 8** Current-voltage characteristics of devices based on $[Cu_2(asp)_4]$ and derivatives, scan rate was 25 mV/s</figcaption></figure>
while the lowest conductivity is observed for the pure
[Cu₂(asp)₄] sample. All hysteresis loops are asymmetric. The
current that flowed through the [Cu₂(asp)₄] sample differs
approximately two orders of magnitude from that of the sam-
ples made from [Cu₂(asp)₄(bimi)₂] and [Cu₂(asp)₄(py)₂]
(Fig. 8). This means that adding axial ligands to copper aspiri-
nate increases the conductivity of the layer by two orders of
magnitude. However, at the same time, the relative span of
the hysteresis loop decreases.

Current-voltage characteristics are shown in Figs. 9 for different scan rates. Devices made from [Cu₂(asp)₄] and [Cu₂(asp)₄(bimi)₂] (Figs. 9a and b) are characterized by anticlockwise pinched hysteresis loops, whereas the sample made from [Cu₂(asp)₄(py)₂] (Fig. 9c) is characterized by a clockwise pinched hysteresis loop. This behavior indicates that the layer made of [Cu₂(asp)₄(py)₂] is a semiconductor of type *p*, while the layers made of [Cu₂(asp)₄] and [Cu₂(asp)₄(bimi)₂] are semiconductors of type *n*. The loops of the [Cu₂(asp)₄] and [Cu₂(asp)₄(bimi)₂] device remain quite similar with different scan rates (Figs. 9a and b), suggesting that the device operates based on the Schottky barrier modification mechanism. The device made of [Cu₂(asp)₄(py)₂], like the samples from [Cu₂(asp)₄(bimi)₂], does not show any significant changes in its hysteresis loops (Fig. 9c), but the loops remain tight at all scan speeds.

Two chronoamperometry experiments were performed on
devices [Cu₂(asp)₄], [Cu₂(asp)₄(bimi)₂], and [Cu₂(asp)₄
(py)₂]. In the first experiment, the devices were switched
between the High Resistive State (HRS) and the Low Resis-
tive State (LRS) by applying potentials of 2 V and –2 V,
respectively. After each switching event, the states were read
applying a potential of 0.3 V.

The second experiment was conducted to evaluate the
retention of HRS and LRS. The devices were first switched
to HRS and LRS using 2 V and –2 V potentials, and then
the states were read every 10 min using a 0.3 V potential.
All devices were stable when switching between HRS and

LRS (Fig. 10a, c, e). The retention experiment showed that the LRS in the [Cu₂(asp)₄] sample is not stable over time (Fig. 10b); after approximately 50 min, the state switches to HRS. Both states in the [Cu₂(asp)₄(bimi)₂] sample (Fig. 10d) remained unchanged even after 6 h of testing and were very stable, with no noticeable noise in the current signal. The retention experiment for the [Cu₂(asp)₄(py)₂] sample (Fig. 10f) showed that at the beginning of the measurement, the signal slightly decreased, but the states remained distinguishable and noise-free after 6 h.

## 3.2 Antisolvent Effect on Crystallization and Modulating Resistive Switching

Research plan towards investigation of the impact of mor-
phology and grain sizes on resistive switching (RS) and
memory properties of exemplary system — methylammo-
nium lead iodide perovskite — was conducted. So far, grain
growth has been shown to be controlled with changes in the
crystallization temperature and the mixed solvent ratio [36].
The surface morphology and grain sizes were controlled by
application of antisolvents, i.e., solvents that are orthogonal
to the substrates of perovskite (MAI, PbI₂). Several known
examples are for typical lead hybrid perovskites [37], but
in solar cell research topics — where it influences charge
carrier density [38]. However, the same principles should
be applicable to all neuromorphic devices. Here, we show
that the resistive switching properties can be modulated sim-
ply by changing the antisolvent during the synthesis of a
functional material layer. In order to assess changes in crys-
tallinity, we imaged each surface using SEM. Figure 11a
illustrates clear morphological differences depending on the
type of antisolvent applied. The smallest grain sizes were
observed with chlorobenzene (CB) as antisolvent, whereas
diethyl ether (DE) generated a more ruptured surface, marked
by many visible shallow holes and discontinuities on the
surface. Without antisolvent crystallization, the surface is

<figure><img src="image_10.png" /><figcaption>**Fig. 9** Current-voltage characteristics based on different compounds with different scan rates. **a** Device based on [Cu₂(asp)₄], **b** device based on [Cu₂(asp)₄(bimi)₂], and **c** device based on [Cu₂(asp)₄(py)₂]</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 10** Chronoamperometric experiments showing repeated switching between HRS and LRS and an experiment showing retention time of HRS and LRS states. The period of a cycle for switching experiment was 0.8 s and the read cycle for retention experiment was 600 s, a switching experiment for [Cu₂(asp)₄] sample, b retention experiment</figcaption></figure>

for [Cu₂(asp)₄] sample, c switching experiment for [Cu₂(asp)₄(bimi)₂] sample, d retention experiment for [Cu₂(asp)₄(bimi)₂] sample, e switching experiment for [Cu₂(asp)₄(py)₂] sample, and f retention experiment for [Cu₂(asp)₄(py)₂] sample

characterized by even more gemetrical features (TEMP). Regardless of the synthesis route, all samples were suitable for electrical characterization, as none of the final layers exhibited through-layer pinholes. The influence of antisolvents is evident in the slight redshift of absorption maxima for samples treated with chlorobenzene (CB) and diethyl ether (DE). In contrast, ethyl acetate (EA) showed negligi-

ble changes in UV-Vis spectra (Fig. 11b), yet provided key evidence that crystallization quality and degree are critical for information-processing applications. These parameters can be modulated through both temperature and antisolvent choice (cf. Fig. 11c–f). Representative I–V characteristics are presented for samples measured using Al as the top electrode and ITO as the bottom electrode.
<figure><img src="image_12.png" /><figcaption>**Fig. 11** **a** SEM images of the surface of CH₃NH₃PbI₃ perovskite synthesized with the application of different antisolvents. The white scalebar is equal to 10 µm, **b** UV-Vis spectra of the thin-film layer sam-</figcaption></figure>

ples (transmittance mode). I-V curves for the samples without the use of antisolvent (c), (d). I-V curves for the ethyl acetate antisolvent samples (e), (f). The numbers (70 and 100) are the final post-bake temperatures

One of the key resistive switching performance metrics, the ON/OFF ratio, is provided in the Supplementary Information for the EA, DE, and CB samples. On the basis of median values for low-resistance (LRS) and high-resistance states (HRS), the ON/OFF ratio varies with antisolvent type, confirming that crystal size and distribution significantly impact the memory and synaptic behavior of the studied systems. The lowest ratio of 1.29 was recorded for the samples treated with EA, followed by DE with a ratio of 1.63, and the samples treated with CB exhibited the highest ratio of 2.00 (details are presented in Table S1, SI).

The crystallinity engineering strategy described here can be extended beyond the antisolvent method. Incorporating various carbon-based materials not only altered the morphology of the final perovskite films but also influenced their plasticity-related responses. We demonstrate modulation of spike-timing-dependent plasticity (STDP), focusing

on the potentiation regime, where the post-spike follows the pre-spike (see the Experimental section and supporting information Figs. S6 and S7). However, the observed changes in the STDP response were modest, suggesting that in the current combination of material-modifier, the effect may be insufficient to drive further technological advancement (Fig. 12). The plasticity response is elongated in time for GO, which may suggest that a charge trapping mechanism is involved. Modifications with C₆₀(OH) and multi-walled carbon nanotubes (MWCNTs) produced samples that exhibit synaptic weight potentiation behavior comparable to that of unmodified perovskite. These plasticity outcomes are consistent with the corresponding surface morphology changes observed, suggesting a correlation between structural modifications and neuromorphic performance. To enhance plasticity performance, future work may involve redesigning the device architecture to incorporate mechanisms
<figure><img src="image_13.png" /><figcaption>**Fig. 12** SEM images (left column) with STDP potentiation quadrant (right column) — electrical characterization results for organolead trihalide perovskites(OTP) modified with graphene oxide (GO) (a, b), fullerenol C₆₀(OH) (c, d), and multi-walled carbon nanotubes (e, f). STDP potentiation response in each case is shown with respect to the pure OTP sample response (blue color plots). The white scale bar in a, c, and e is equal to 100 µm</figcaption></figure>

with stronger influence, such as enhanced charge trapping or tailored interface engineering, both of which could yield more pronounced plasticity effects.

## 4 Conclusion

Preliminary experimental studies indicate that the experimental approach towards materials mimicking the hippocampal tissue is feasible. Incorporation of axial ligands into [Cu₂(asp)₄] complex may have two-fold consequences. First of all, owing to the change in electronic coupling between metal centers, it influences the bulk conductivity of the layers, as well as changes the conductivity type by modification of electron density in these materials. It has been demonstrated that ligands with various electron-donor properties exert different influences on the conductivity type, switching performance (the span of the hysteresis loop), and switching dynamics (namely, the retention time). Furthermore, these ligands may serve as points of noncovalent interaction with other aromatic counterparts in more complex systems [10, 19–22]. Appropriate selection of conjugated electron donors and electron acceptors will lead to complex, polycrystalline systems with various semiconducting domains of different conductivity type, charge carrier mobility, and bandgap. A combination of metal-ligand interactions coupled with supramolecular self-assembly may yield materials

with very complex conductivity patterns and resistive switching dynamics. The latter effects can also be achieved by the means of crystallinity engineering for the same material (e.g., by incorporation of antisolvent) or by doping the material with moieties, which in addition can also influence overall sample conductivity phenomena. Using this approach, we hope to reproduce the following hippocampal phenomena, which play a crucial role in the implementation of memristive hippocampal structures: (1) managed stochastic organization of the axon-dendritic tree implemented via nanomaterial networks, (2) high density of memristive devices per neuron, possibly up to biological scales $10^4$ implemented via polycrystalline structures grown on the top of the nanomaterial networks, and (3) generation of neuronal spikes in response to weighted through the memristive polycrystalline junctions.

## 5 Methods

### 5.1 Copper (II) Aspirinate [Cu₂(asp)₄]

The synthesis was performed on the basis of the literature [39]. First, 1 g of aspirin (Sigma-Aldrich), 0.296 g of sodium bicarbonate (POCH S.A.), and 0.705 g of copper sulfate (Acros Organics) were weighed. Two flasks, each containing 10 ml of distilled water, were prepared. Aspirin was added to the first, and sodium bicarbonate was dissolved in the other.
While the aspirin mixture was stirred, the sodium bicarbonate solution was added dropwise. The mixture was stirred until the aspirin dissolved. Once the aspirin had dissolved, the solution was filtered. Copper sulfate was then dissolved in 3 ml of distilled water. While the aspirin solution was stirred slowly, the copper sulfate solution was added, resulting in the formation of a blue powder of $[Cu_2(asp)_4]$. The powder was filtered, washed with distilled water, and dried.

## Complexes of $[Cu_2(asp)_4]$ with Axial Ligands

Three different compounds were obtained by adding ligands to $[Cu_2(asp)_4]$: benzimidazole (Sigma-Aldrich), pyridine (Chempur) and 1,4-diazabicyclo[2.2.2]octane (DABCO) (Sigma-Aldrich). First, $[Cu_2(asp)_4]$ was added to dimethylformamide (DMF) (EUROCHEM BGD SP. z.o.o.) at a ratio of 300 mg per 1 ml of DMF. The mixture was then heated until $[Cu_2(asp)_4]$ dissolved. After that step, the axial ligand was added to the $[Cu_2(asp)_4]$ solution in DMF at a molar ratio of 2:1 (ligand:[$Cu_2(asp)_4$]).

## Device Preparation and Characterization

The devices were prepared on glass substrates of indium tin oxide (ITO) obtained from Osilla (100 nm thick ITO with 1.8 nm RMS and $20 \Omega \cdot 10^{-1}$ quoted sheet resistance, without pattern). Layers of the investigated compounds were prepared using spin coating. For spin coating, a solution of 300 mg $[Cu_2(asp)_4]$ in 0.5 ml of DMF and 0.5 ml of toluene (Chempur) was prepared and filtered. In addition, a solution of 300 mg $[Cu_2(asp)_4]$ with benzimidazole in DMF was prepared. For the last two compounds, solutions from the synthesis of axial ligands were used.

The layers were made by drop-casting 70 µl of solution onto the ITO glass, followed by a two-step spinning process. The spin acceleration in both steps was 1000 rpm per second. The first step involved spinning at 1000 rpm for 10 s, followed by a second step at 2000 rpm for 30 s. After that step, the devices were placed on a 95°C hot plate for 15 min to remove solvents from the layers. The final step involved sputtering copper electrodes using the thermal evaporation method by resistively heating the tungsten boat with copper under high vacuum.

All electrochemical measurements were performed with SP-300 potentiostat (BioLogic Science Instruments) with Instec manual probe station.

## XAS Measurements

For X-ray absorption spectroscopy (XAS) measurements, powder samples were ground with cellulose using a mortar and pestle and pressed into thin pellets, then mounted to a stainless steel frame with adhesive Kapton tape. The spectra were collected at Cu K-edge at the bending magnet ASTRA beamline [40] of the National Synchrotron Radiation Centre SOLARIS [41] using an incident photon beam provided by a modified Lemonnier-type double crystal monochromator

equipped with a Ge(220) crystal pair. For each sample, three consecutive scans were collected, processed, and analyzed using the ATHENA program from the DEMETER software package [42]. The measurements were performed in transmission mode. The energy calibration was carried out using a copper foil, recorded before and during sample scans in the reference chamber, with the absorption edge defined at 8979 eV (the first maximum of the derivative spectrum). The EXAFS spectra presented in R space were phase-corrected.

Density functional theory (DFT) methods were employed to model X-ray absorption spectroscopy (XAS) spectra, using the FDMNES software package [43] within the local spin density approximation. The structural model for these calculations was obtained from the literature [44, 45]. For the Cu K-edge, the finite difference method was applied to simulate the fine structure of X-ray absorption (XAFS)[46], incorporating dipole ($\Delta l = \pm 1$) and quadrupole ($\Delta l = 0, \pm 2$) transitions, with a cluster radius of 8 Å. Relativistic and spin-orbit coupling effects were not included in the calculations. The resulting spectra were convoluted with a Lorentzian function for presentation.

## 5.2 Preparation and Modification Procedures of Methylammonium Lead Iodide ($CH_3NH_3PbI_3$)

The samples were synthesized following the two-step method on hot, clean transparent conductive substrates. ITO substrates were cleaned following the following procedure: sonication 3 times 10 min — first in 1 vol. % Hellmanex solution, then distilled water, and finally isopropyl alcohol, followed by 2x hot distilled water rinse. Before solution deposition, each ITO piece was treated in oxygen plasma for 15 min. ITO substrates were then dried (N2 blow) and heated to 70 °C. $PbI_2$ (0.75M in DMF, 80–100 µL) was deposited by spincoating (3000 rpm, 30s) after being heated up to 70 °C and immediately filtered by PTFE syringe filter. The substrate was then subsequently dried on the hotplate at 70 °C for 10 min. Methylammonium iodide solution (MAI, 0.25M in isopropyl alcohol, 80–100 µL) was dynamically spincoated (4000 rpm, 30 s). During the spinning of pure and dried antisolvent, volume of 200 µL, was used to wash the surface of the sample, dropwise. These antisolvents were used: diethyl ether (DE), ethyl acetate (EA), and chlorobenzene (CB). The substrates in their final states were left for perovskite crystallization in 100 °C on a hotplate for an additional 10 min. All measurements have taken place under ambient conditions with temperatures ranging from 21 to 22 °C and relative humidity not exceeding 30%. The electronic and memristive properties of the samples were registered by the Keithley 4200 SCS system, with internal procedures developed in Keithley Test Environment Interactive (KTEI) software. Carbon material (CM) additives were synthesized and purified
according to the methods described in previous publications: graphene oxide (GO) [47] and fullerenols ($C_{60}$OH) [48]. Multiwalled carbon nanotubes (MWCNT) and PEDOT:PSS solution were purchased from Sigma-Aldrich. Samples were prepared by spincoating a first PEDOT:PSS layer and then on top — mixed $PbI_2$ and CM solution. Further device preparation steps were performed as described above, leading to methylammonium lead iodide layers modified by a carbon material of choice, covered with Cu electrodes.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s12668-025-02124-w.

**Acknowledgements** The authors acknowledge the financial support from the Polish National Science Center within the OPUS programme (grant agreement No. 2022/47/B/ST4/00728). This research was partly supported by the program "Excellence initiative—research university" for the AGH University of Krakow and by the Université de la Nouvelle-Calédonie, Nouméa, New Caledonia. The authors gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2025/018527. The XANES measurements at ASTRA beamline were made under the provision of the Polish Ministry of Education and Science project: 'Support for research and development with the use of research infrastructure of the National Synchrotron Radiation Centre SOLARIS' under contract nr 1/SOL/2021/2. The further development of the ASTRA beamline for measuring at low photon energies was supported within the EU Horizon2020 programme (952148-Sylinda). This study was partially funded by the Fairground project "Artificial and Bio-Inspired Networked Intelligence for Constrained Autonomous Devices" (Fairground Bando A Cascata A Valere Sul Piano Nazionale Ripresa E Resilienza (PNRR) Missione 4, Istruzione E Ricerca-Componente 2, Dalla Ricerca All'impresa-Linea Di Investimento 1.3, Finanziato Dall'Unione Europea NextGenerationEU, Progetto Future Artificial Intelligence Fair PE0000013 CUP (Master) J53C22003010006 CUP J43C24000230007). The author(s) acknowledge the support of the APC central fund of the University of Messina.

**Author Contributions** D.C. has performed the synthesis of copper complexes as well as their full electrical characterization; A.S. has performed XAS analysis of copper complexes; T.M. and P.Z. have designed and analysed: antisolvent approach towards crystallinity control, carbon material modification towards plasticity response variations; B.B. has discussed the electrical data for aspirinate complexes; K.S. has performed DFT modelling of copper complexes and developed the concept of supramolecular composites; M.T. had developed the idea of hippocampal mimics; and G.A. has discussed synthesis, the electrical measurements, random distribution in low-dimensional nanowires and nanotubes, and coordinated the project and prepared the final version.

**Data Availability** No datasets were generated or analysed during the current study.

## Declarations

**Ethical Approval** Not applicable. This research does not involve any human or animal experiments. All funding sources has been declared in Acknowledgements section.

**Competing Interests** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

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