Recurrent attractor networks have long been the leading theoretical explanation for working memory. In these circuits, neurons excite one another in loops, so that a pattern of activity, once ignited, sustains itself long after the triggering stimulus has vanished. Neurons in the prefrontal cortex of primates show exactly this kind of persistent firing during memory tasks, outlasting the intrinsic time constants of their membranes by orders of magnitude, which argues that the persistence comes from the network rather than from any single cell. But attractor networks carry an inherent trade-off. A circuit tuned to hold activity stably is notoriously hard to switch off, and models have typically solved this by adding abstract gates that control when the attractor is allowed to run. What those gates look like in real biology, synapse by synapse, has remained unclear.

The fan-shaped body of the fruit fly, part of a navigation centre called the central complex, offered the research team led by Aaron Lanz and Katherine Nagel at NYU School of Medicine an unusually tractable place to look. Connectomics has mapped this region with synaptic precision, and genetic tools allow researchers to record from identified cell types in walking flies. Within the fan-shaped body, two local neuron types, called hΔK and PFG, are reciprocally connected in a ring, forming the most numerous recurrent connections of any columnar pair in the central complex. PFG neurons also receive direct inputs from the fly’s internal compass in the ellipsoid body, while both populations get broad input from tangential neurons. The architecture is strikingly reminiscent of classic working memory models: local recurrent excitation, structured input, and global inhibition.

Using two-photon calcium imaging in flies walking on an air-supported ball, the researchers confirmed that both hΔK and PFG neurons show a localized bump of activity that switches on with odour and persists while the fly maintains its heading. When the bump turned off, the fly’s trajectory deviated from its goal, linking the neural persistence directly to navigational behaviour. Whole-cell patch clamp recordings then probed where this persistence comes from. Injecting current into hΔK neurons produced rapid responses with no lingering excitation, ruling out intrinsic membrane properties. Instead, when the team optogenetically activated upstream tangential neurons, hΔK cells responded with slow, persistent excitation that vanished when recurrent signalling from hΔK itself was blocked with tetanus toxin. Persistence, in other words, is a property of the loop, not the neuron.

The synaptic details proved to be the study’s most surprising twist. Excitation arriving at hΔK is slow, likely carried by neuropeptides and modulators, since PFG neurons express no classical fast transmitter at all, only tyramine, myoinhibitory peptide and diuretic hormone 31. Inhibition, by contrast, is fast: two tangential neuron types, FB5V and FB6M, deliver rapid GABAergic inhibition that depresses over time and can be converted to excitation by the blocker picrotoxin. When the researchers built a computational model of the circuit based on real connectome weights, with 30 hΔK neurons, 18 PFG neurons and a global inhibitory unit, this speed asymmetry turned out to be crucial. With fast recurrent excitation, only a narrow band of excitation and inhibition strengths produced stable persistence; small parameter changes flipped the network between transient and non-decaying regimes. With slow excitation, a broad regime of tuneable persistence emerged, allowing the duration of the memory to be graded smoothly by modest changes in synaptic strength, matching the variability seen in the flies.

But a stable memory still needs a switch. Close inspection of the imaging data revealed that hΔK and PFG, despite their tight anatomical coupling, behave alike only during straight goal-directed runs. During turns and rest, the two populations decouple: PFG neurons maintain a low-amplitude bump that slides across the fan-shaped body in register with the fly’s heading, exactly like the compass it receives, while hΔK activity largely disappears and reappears only when the fly settles on a new direction. When the experimenters rotated the wind by 90 degrees mid-trial, low-amplitude PFG bumps followed the heading change faithfully, whereas high-amplitude bumps stayed fixed in place. A single population could thus flip between tracking the compass and holding a goal, depending on its own activity level.

The mechanism the team proposed is disarmingly elegant: disinhibition. In the connectome, only PFG neurons receive direct compass input, and that feedforward input is several-fold weaker than the recurrent connections between hΔK and PFG. In the model, when hΔK is strongly inhibited, the recurrent loop is functionally broken and PFG simply follows its compass input. The moment inhibition is lifted, recurrence locks the current compass value into place, and both populations snap into a high-amplitude, positionally stable bump, like a latch in digital logic capturing whatever value was on the line. Simulations showed that disinhibition could write a heading into memory at any chosen moment, and that sequential disinhibition bouts could update the stored goal to new compass positions.

Imaging the two inhibitory tangential types in behaving flies supported the model’s predictions with remarkable fidelity. FB5V neurons, which inhibit only hΔK, are tonically active but strongly suppressed by odour, and the suppression continues through the goal-directed run after the odour ends. Moment to moment, FB5V activity is negatively correlated with upwind velocity and positively correlated with angular speed, and it surges during large turns and whenever the fly deviates from its goal. FB6M, which inhibits both populations, shows a related pattern, suppressing during wind and rising during turns, though the model suggests it permissively regulates both populations together rather than decoupling them. The dendrites of these neurons sit in brain regions encoding odour and pre-motor signals, providing a plausible route by which sensory events and the fly’s own behaviour gate the memory circuit.

The study’s broader significance lies in the design principle it exposes. Rather than building an attractor from a uniform pool of excitatory neurons, the fly splits the network in two: one half, PFG, carries the content of the memory by receiving the compass input, while the other half, hΔK, receives the inhibitory gate that controls the timing of memory formation. Separating content from timing allows a circuit that is anatomically fixed to change its function on the timescale of a behaviour, switching between following information and storing it. Slow excitation, whether from peptides, modulators or the NMDA receptors familiar from mammalian working memory models, stabilizes the attractor across a forgiving range of synaptic weights, while fast disinhibition provides the write command. The authors suggest this motif could be general, potentially allowing other attractor networks, including line attractors and sequential dynamics, to be flexibly engaged and disengaged by sensory input or internal state. For a brain the size of a poppy seed, it is a strikingly computer-like solution, and one that neuroscientists studying working memory in larger brains will now be eager to look for.

Subject of Research: Recurrent attractor circuit mechanisms for rapid gating of navigational working memory in the Drosophila central complex

Article Title: A split attractor design for rapidly writing a navigational goal

Article References: Lanz, A. J., Kathman, N. D., Hao, E., Ermentrout, B., & Nagel, K. I. (2026). A split attractor design for rapidly writing a navigational goal. Nature. https://doi.org/10.1038/s41586-026-11144-9

Image Credits: AI Generated

DOI: 10.1038/s41586-026-11144-9

Keywords: Drosophila, attractor networks, working memory, navigation, fan-shaped body, disinhibition, connectomics, calcium imaging, whole-cell recording, computational modelling, synaptic dynamics, central complex

The central idea is that a gel need not be a passive, homogeneous medium. In a multiphasic gel, the material is structured into distinct phases with different chemical compositions, ionic mobilities and interfacial properties. When an electrical signal is applied, ions do not simply drift through the material in a straight, Ohmic fashion. Instead, they accumulate, deplete and redistribute at the boundaries between phases, giving rise to transient behaviors that depend on the history of the applied voltage. This memory of past stimulation, embedded directly in the spatial distribution of ions, is precisely the property that neuromorphic engineers prize, because it allows a single material element to perform operations that would otherwise require many transistors.

To appreciate why this matters, it helps to consider the limitations of conventional electronics. Silicon transistors move electrons through crystalline semiconductors with extraordinary speed and reliability, but they are fundamentally decoupled from the chemistry of the environments in which they operate. Biological neural systems, by contrast, process signals in wet, soft, salt-laden surroundings, and they do so at energies orders of magnitude lower than digital hardware performing comparable tasks. The commentary by Peng and Zhang situates the new ionic gel work within a broader landscape of iontronic research that has been rapidly consolidating in recent years, including reviews of bioelectronic and iontronic technologies in Nature Reviews Bioengineering and a dedicated iontronics journal literature that has emerged to catalog the field’s accelerating progress.

The physics underlying ionic signal processing is rich and, in some respects, counterintuitive. Because ions are much heavier and slower than electrons, ionic currents respond to voltage changes on time scales ranging from microseconds to seconds, depending on the mobility of the charge carriers, the geometry of the channels and the viscosity of the medium. Far from being a defect, this slowness can be exploited. It introduces natural temporal dynamics, such as gradual charging of interfacial capacitances, diffusion-limited transport and concentration-polarization effects, that mimic the integration and decay behaviors of biological synapses. In a multiphasic gel, the interfaces between phases act as internal barriers and reservoirs, so the effective response of the device becomes a convolution of transport, partitioning and interfacial reaction processes, each with its own characteristic time constant.

Researchers in the field have assembled a substantial body of evidence that such dynamics can be harnessed for computation. A landmark demonstration published in Science in 2023 showed that nanofluidic systems, in which ions are forced through channels comparable in size to the electrical double layers that screen charged surfaces, can display memory and even long-term memory behaviors, providing a physical basis for memristive operation driven entirely by ions. The memristor, a circuit element whose resistance depends on the history of current flow through it, has become the canonical building block of neuromorphic hardware, and its ionic realization suggests that synapse-like elements could be fabricated from soft materials rather than rigid oxide films. Peng and Zhang’s commentary draws on this lineage, emphasizing that the cross-interface dynamics observed in multiphasic gels extend the same conceptual framework into a new class of soft, multiphase media.

Work published in Science in 2024 by Zhang, Tan, Toepfer, Lu and Bayley, several of whom are central figures in the bio-iontronics community, demonstrated that droplet-based ionic architectures can support sophisticated signal processing functions, pointing toward computational primitives built entirely from aqueous phases separated by lipid or polymer membranes. The multiphasic gel described in the Nature Electronics commentary represents a conceptual cousin of these droplet interfacial systems, but with the phases locked into a continuous solid-like scaffold. This distinction matters for applications. Droplet networks are exquisite for fundamental studies but fragile, whereas gels can be handled, patterned and potentially integrated with electrodes, wires and biological tissue, opening a route from laboratory curiosity toward practical soft devices.

The relationship between ionic transport and neuromorphic function can be made concrete by considering what happens when a voltage pulse crosses one of the internal interfaces in the gel. Initially, the ionic current is dominated by the mobile carriers in the phase connected to the driving electrode. As ions pile up at the interface, the local concentration rises, the interfacial electric field redistributes, and counter-diffusion begins to flatten the gradient. If the pulse ends before the system reaches equilibrium, some of the accumulated charge relaxes back, producing a short-term memory trace; if pulses arrive repeatedly, the accumulation can persist, strengthening the effective coupling across the interface in a manner analogous to synaptic facilitation. Two-terminal elements built on this principle naturally implement paired-pulse facilitation, temporal filtering and thresholded switching, all canonical operations of biological synapses.

Chemical reviews published over the past two years have begun to systematize this design space. A comprehensive survey in Chemical Reviews in 2025 examined ionic materials and devices for neuromorphic and bioelectronic applications, while a 2024 review in Chemical Society Reviews mapped the physics of ion transport and rectification in engineered nanostructures. Together with a further Chemical Society Reviews article in 2026 surveying the state of iontronic materials, these works chart a field that is moving from proof-of-concept demonstrations toward systematic design rules, in which the choice of polymer matrix, ionic species, phase composition and interface chemistry can be tuned to produce a desired dynamic response. The multiphasic gel strategy sits squarely within this trend, treating the internal structure of the material itself as an engineering variable.

The companion research article highlighted in the commentary, published by Wu and colleagues in Nature Electronics, provides the experimental substance behind the conceptual discussion. Although the full analytical details are available to readers of the journal, the commentary frames the work as demonstrating that ions in the multiphasic gel exhibit the complex cross-interface dynamics required for neuromorphic processing, and the accompanying figure, titled ‘Ion transporters based on multiphasic gels,’ illustrates the device concept in which ionic transporters, the structural and functional units that shuttle charge between phases, underpin the observed signal processing behavior. Framing a material platform in terms of transporters rather than passive channels reflects a shift in how the field describes ionic devices, emphasizing active, directed, history-dependent charge management rather than simple conduction.

The possible applications of soft ionic processors extend well beyond replacing silicon. Because ionic gels are mechanically compliant, chemically compatible with aqueous environments and operable at low voltages, they are natural candidates for integration with living tissue. Implantable and wearable bioelectronics could, in principle, incorporate local neuromorphic elements that preprocess electrophysiological signals in situ, reducing the bandwidth and power demands on external hardware. Artificial synapses built from multiphasic gels might one day interface directly with neurons, whose native signaling currency is the same ionic flux that the devices process. Researchers have also proposed ionic systems for reservoir computing, in which the rich internal dynamics of a physical medium perform temporal computation, with the gel itself serving as the reservoir. The commentary’s authors, based at a laboratory explicitly dedicated to bio-iontronics, underscore that the convergence of soft materials science, electrochemistry and neural engineering is what gives this direction its momentum.

Significant challenges remain before ionic gels can rival established technologies. Ionic devices are inherently slow compared with electronic ones, which limits their usefulness for high-frequency computation even as it suits them for biological time-scale tasks. Scaling from single junctions to large integrated networks requires reproducible fabrication of multiphase architectures, stable electrode contacts and materials that resist dehydration, fatigue and fouling over long lifetimes. The theoretical toolkit for describing coupled ionic transport across heterogeneous interfaces is still maturing, and researchers acknowledge that the field benefits from continued cross-disciplinary synthesis, as reflected in the breadth of the literature the commentary engages, spanning nanofluidics, memristive physics, droplet interfacial science and bioengineering. Peng and Zhang declare no competing interests, and their perspective, published in September 2026, arrives at a moment when iontronics is acquiring the journals, citations and design principles of a mature discipline.

What the multiphasic gel work ultimately illustrates is that computation need not be confined to crystalline silicon or even to electrons. Matter structured at the mesoscale, with phases that sort, store and release ions across their interfaces, can perform operations that look strikingly neural, and it can do so in a material that is soft, wet and biologically congenial. As the commentary concludes in spirit if not in a single sentence, the complex cross-interface dynamics of ions in such gels are not a nuisance to be engineered away but the very resource from which neuromorphic function is built. If the past decade belonged to memristive oxides and spintronic devices, the next may belong to materials that compute the way life does, one ion at a time, and the ionic gel now stands as one of the clearest demonstrations of that possibility.

Subject of Research: Neuromorphic signal processing using ionic transport across interfaces in a multiphasic gel

Article Title: Neural signal processing in an ionic gel

Article References: Peng, P., & Zhang, Y. (2026). Neural signal processing in an ionic gel. Nature Electronics. https://doi.org/10.1038/s41928-026-01684-3

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01684-3

Keywords: iontronics, multiphasic gel, neuromorphic computing, ionic transport, memristor, soft materials, bioelectronics, nanofluidics, synaptic dynamics, Nature Electronics, Neural, signal