Right now, the debate about consciousness often feels frozen between two entrenched positions. On one side sits computational functionalism, which treats cognition as something you can fully explain in terms of abstract information processing: get the right functional organization (regardless of the material it runs on) and you get consciousness. On the other side is biological naturalism, which insists that consciousness is inseparable from the distinctive properties of living brains and bodies: biology isn't just a vehicle for cognition, it is part of what cognition is. Each camp captures something important, but the stalemate suggests that something is missing from the picture.
In our new paper, we argue for a third path: biological computationalism. The idea is deliberately provocative but, we think, clarifying. Our core claim is that the traditional computational paradigm is broken or at least badly mismatched to how real brains operate. For decades, it has been tempting to assume that brains "compute" in roughly the same way conventional computers do: as if cognition were essentially software, running atop neural hardware. But brains do not resemble von Neumann machines, and treating them as though they do forces us into awkward metaphors and brittle explanations. If we want a serious theory of how brains compute and what it would take to build minds in other substrates, we need to widen what we mean by "computation" in the first place.
Biological computation, as we describe it, has three defining properties.
First, it is hybrid: it combines discrete events with continuous dynamics. Neurons spike, synapses release neurotransmitters, and networks exhibit event-like transitions, yet all of this is embedded in evolving fields of voltage, chemical gradients, ionic diffusion, and time-varying conductances. The brain is not purely digital, and it is not merely an analog machine either. It is a layered system where continuous processes shape discrete happenings, and discrete happenings reshape continuous landscapes, in a constant feedback loop.
Second, it is scale-inseparable. In conventional computing, we can draw a clean line between software and hardware, or between a "functional level" and an "implementation level." In brains, that separation is not clean at all. There is no tidy boundary where we can say: here is the algorithm, and over there is the physical stuff that happens to realize it. The causal story runs through multiple scales at once, from ion channels to dendrites to circuits to whole-brain dynamics and the levels do not behave like modular layers in a stack. Changing the "implementation" changes the "computation," because in biological systems, those are deeply entangled.
Third, biological computation is metabolically grounded. The brain is an energy-limited organ, and its organization reflects that constraint everywhere. Importantly, this is not just an engineering footnote; it shapes what the brain can represent, how it learns, which dynamics are stable, and how information flows are orchestrated. In this view, tight coupling across levels is not accidental complexity. It is an energy optimization strategy: a way to produce robust, adaptive intelligence under severe metabolic limits.
These three properties lead to a conclusion that can feel uncomfortable if we are used to thinking in classical computational terms: computation in the brain is not abstract symbol manipulation. It is not simply a matter of shuffling representations according to formal rules, with the physical medium relegated to "mere implementation." Instead, in biological computation, the algorithm is the substrate. The physical organization does not just support the computation; it constitutes it. Brains don't merely run a program. They are a particular kind of physical process that performs computation by unfolding in time.
This also highlights a key limitation in how we often talk about contemporary AI. Current systems, for all their power, largely simulate functions. They approximate mappings from inputs to outputs, often with impressive generalization, but the computation is still fundamentally a digital procedure executed on hardware designed for a very different computational style. Brains, by contrast, instantiate computation in physical time. Continuous fields, ion flows, dendritic integration, local oscillatory coupling, and emergent electromagnetic interactions are not just biological "details" we might safely ignore while extracting an abstract algorithm. In our view, these are the computational primitives of the system. They are the mechanism by which the brain achieves real-time integration, resilience, and adaptive control.
This does not mean we think consciousness is magically exclusive to carbon-based life. We are not making a "biology or nothing" argument. What we are claiming is more specific: if consciousness (or mind-like cognition) depends on this kind of computation, then it may require biological-style computational organization, even if it is implemented in new substrates. In other words, the crucial question is not whether the substrate is literally biological, but whether the system instantiates the right class of hybrid, scale-inseparable, metabolically (or more generally energetically) grounded computation.
That shift changes the target for anyone interested in synthetic minds. If the brain's computation is inseparable from the way it is physically realized, then scaling digital AI alone may not be sufficient. Not because digital systems can't become more capable, but because capability is only part of the story. The deeper challenge is that we might be optimizing the wrong thing: improving algorithms while leaving the underlying computational ontology untouched. Biological computationalism suggests that to engineer genuinely mind-like systems, we may need to build new kinds of physical systems: machines whose computing is not layered neatly into software on hardware, but distributed across levels, dynamically coupled, and grounded in the constraints of real-time physics and energy.
So, if we want something like synthetic consciousness, the problem may not be, "What algorithm should we run?" The problem may be, "What kind of physical system must exist for that algorithm to be inseparable from its own dynamics?" What are the necessary features—hybrid event–field interactions, multi-scale coupling without clean interfaces, energetic constraints that shape inference and learning—such that computation is not an abstract description laid on top, but an intrinsic property of the system itself?
That is the shift biological computationalism demands: moving from a search for the right program to a search for the right kind of computing matter.
