In our new paper, we propose a different approach: biological computationalism. The label is meant to be provocative, but also to sharpen the conversation. Our main argument is that the standard computational framework is broken, or at least poorly suited to how brains actually work. For a long time, it has been tempting to picture the mind as software running on neural hardware, with the brain “computing” in roughly the way a conventional computer does. But real brains are not von Neumann machines, and forcing that comparison leads to shaky metaphors and fragile explanations. If we want a serious account of how brains compute, and what it would take to build minds in other substrates, we first need a broader definition of what “computation” can be.

Biological computation, as we describe it, has three core features.

Hybrid Brain Computation in Real Time

First, biological computation is hybrid. It mixes discrete events with continuous dynamics. Neurons fire spikes, synapses release neurotransmitters, and networks shift through event-like states. At the same time, these events unfold within constantly changing physical conditions such as voltage fields, chemical gradients, ionic diffusion, and time-varying conductances. The brain is not purely digital, and it is not simply an analog machine either. Instead, it works as a multi-layered system where continuous processes influence discrete events, and discrete events reshape the continuous background, over and over, in an ongoing feedback loop.

Why Brain Computation Cannot Be Separated by Scale

Second, biological computation is scale-inseparable. In conventional computing, it is often possible to cleanly separate software from hardware, or a “functional level” from an “implementation level.” In the brain, that kind of separation breaks down. There is no neat dividing line where you can point to the algorithm on one side and the physical mechanism on the other. Cause and effect run across many scales at once, from ion channels to dendrites to circuits to whole-brain dynamics, and these levels do not behave like independent modules stacked in layers. In biological systems, changing the “implementation” changes the “computation,” because the two are tightly intertwined.

Metabolism and Energy Constraints Shape Intelligence

Third, biological computation is metabolically grounded. The brain operates under strict energy limits, and those limits shape its structure and function everywhere. This is not just an engineering detail. Energy constraints influence what the brain can represent, how it learns, which patterns remain stable, and how information is coordinated and routed. From this perspective, the tight coupling across levels is not accidental complexity. It is an energy optimization strategy that supports robust, flexible intelligence under severe metabolic limits.

The Algorithm Is the Substrate

Taken together, these three features point to a conclusion that can feel strange if you are used to classical computing ideas. Computation in the brain is not abstract symbol manipulation. It is not simply about moving representations around according to formal rules while the physical medium is treated as “mere implementation.” In biological computation, the algorithm is the substrate. The physical organization does not just enable the computation, it is what the computation consists of. Brains do not merely run a program. They are a specific kind of physical process that computes by unfolding through time.

What This Means for AI and Synthetic Minds

This view also exposes a limitation in how people often describe modern AI. Even powerful systems mostly simulate functions. They learn mappings from inputs to outputs, sometimes with impressive generalization, but the computation is still a digital procedure running on hardware built for a very different style of computing. Brains, by contrast, carry out computation in physical time. Continuous fields, ion flows, dendritic integration, local oscillatory coupling, and emergent electromagnetic interactions are not just biological “details” that can be ignored while extracting an abstract algorithm. In our view, these are the computational primitives of the system. They are the mechanisms that enable real-time integration, resilience, and adaptive control.

Not Biology Only, But Biology Like Computation

This does not mean we think consciousness is somehow restricted to carbon-based life. We are not arguing “biology or nothing.” Our claim is narrower and more practical. If consciousness (or mind-like cognition) depends on this kind of computation, then it may require biological-style computational organization, even if it is built in new substrates. The key issue is not whether the substrate is literally biological, but whether the system instantiates the right kind of hybrid, scale-inseparable, metabolically (or more generally energetically) grounded computation.

A Different Target for Building Conscious Machines

That reframes the goal for anyone trying to build synthetic minds. If brain computation cannot be separated from how it is physically realized, then scaling digital AI alone may not be enough. This is not because digital systems cannot become more capable, but because capability is only part of the puzzle. The deeper risk is that we may be optimizing the wrong thing by improving algorithms while leaving the underlying computational ontology unchanged. Biological computationalism suggests that building truly mind-like systems may require new kinds of physical machines whose computation is not organized as software on hardware, but spread across levels, dynamically linked, and shaped by the constraints of real-time physics and energy.

So if we want something like synthetic consciousness, the central question may not be, “What algorithm should we run?” It may be, “What kind of physical system must exist for that algorithm to be inseparable from its own dynamics?” What features are required, including hybrid event-field interactions, multi-scale coupling without clean interfaces, and energetic constraints that shape inference and learning, so that computation is not an abstract description layered on top but an intrinsic property of the system itself?

That is the shift biological computationalism calls for. It moves the challenge from finding the right program to finding the right kind of computing matter.

Candida auris is especially dangerous for people who are already critically ill, leaving hospitals highly exposed to outbreaks. Although the fungus can exist on the skin without causing symptoms, patients who depend on ventilators face a much greater risk. Once infection occurs, about 45 percent of patients die, and the fungus is resistant to all major types of antifungal drugs. This resistance makes treatment extremely challenging and allows the pathogen to persist in hospital wards.

A global health threat with mysterious origins

The infection was first detected in 2008, and its origin is still unknown. Since then, outbreaks have been reported in more than 40 countries, including the UK. Candida auris, also called Candidozyma auris, is now recognized as a serious global health threat and appears on the World Health Organization’s critical priority fungal pathogens list. In the UK, reported cases have continued to rise steadily.

Studying infection in a living model

Researchers at the University of Exeter have now taken a major step forward by examining how genes are activated during Candida auris infection. This marks the first time such genetic activity has been studied in a living host using an approach based on fish larvae. The study was published in the Nature portfolio journal Communications Biology and was supported by Wellcome, the Medical Research Council (MRC), and the National Center for Replacement, Reduction and Refinement (NC3Rs).

The researchers say the results could help identify a biological target for new antifungal treatments or even allow existing drugs to be reused, if the same genetic behavior is confirmed during infection in humans.

The project was co-led by NIHR Clinical Lecturer Hugh Gifford of the University of Exeter’s MRC Center for Medical Mycology (CMM). He said: “Since it emerged, Candida auris has wreaked havoc where it takes hold in hospital intensive care units. It can be deadly for vulnerable patients, and health trusts have spent millions on the difficult job of eradication. We think our research may have revealed an Achilles heel in this lethal pathogen during active infection, and we urgently need more research to explore whether we can find drugs that target and exploit this weakness.”

Why traditional research models fell short

One of the biggest obstacles in studying Candida auris has been its ability to survive high temperatures. When combined with its unusually strong tolerance for salt, this has led some researchers to suggest it may have originated in tropical oceans or marine animals. These traits also made it difficult to study using conventional laboratory models.

To overcome this, the Exeter team developed a new infection model using Arabian killifish. The eggs of this species can survive at temperatures similar to the human body, making them suitable for observing infection in conditions that closely resemble real illness.

Genetic activity reveals possible vulnerabilities

During the experiments, researchers observed that Candida auris can change its shape by forming elongated fungal structures known as filaments. These structures may help the fungus search for nutrients while infecting a host.

The team also analyzed which genes were activated or switched off during infection to identify possible weak points. Several of the genes that became active are responsible for producing nutrient pumps that capture iron-scavenging molecules and transport iron into fungal cells. Because iron is essential for survival, this process may represent a critical vulnerability.

Co-senior author Dr. Rhys Farrer of the University of Exeter’s MRC Centre for Medical Mycology said: “Until now, we’ve had no idea what genes are active during infection of a living host. We now need to find out if this also occurs during human infection. The fact that we found genes are activated to scavenge iron gives clues to where Candida auris may originate, such as an iron-poor environment in the sea. It also gives us a potential target for new and already existing drugs.”

Hope for future treatments

Dr. Gifford, who also works as a resident physician in intensive care and respiratory medicine at the Royal Devon & Exeter Hospital, emphasized the clinical importance of the findings. He said: “While there are a number of research steps to go through yet, our finding could be an exciting prospect for future treatment. We have drugs that target iron scavenging activities. We now need to explore whether they could be repurposed to stop Candida auris from killing humans and closing down hospital intensive care units.”

The Arabian killifish larvae model was developed with support from an NC3Rs project grant as an alternative to using mouse and zebrafish models, which are commonly used to study interactions between pathogens and their hosts. Dr. Katie Bates, NC3Rs Head of Research Funding, said: “This new publication demonstrates the utility of the replacement model to study Candida auris infection and enable unprecedented insights into cellular and molecular events in live infected hosts. This is a brilliant example of how innovative alternative approaches can overcome key limitations of traditional animal studies.”

The paper is titled ‘Xenosiderophore transporter gene expression and clade-specific filamentation in Candida auris killifish (Aphanius dispar) infection’ and is published in the Nature portfolio journal Communications Biology.