Summary: A new theoretical framework argues that the long-standing split between computational functionalism and biological naturalism misses how real brains actually compute.<\/p>\n
The authors propose \u201cbiological computationalism,\u201d the idea that neural computation is inseparable from the brain\u2019s physical, hybrid, and energy-constrained dynamics rather than an abstract algorithm running on hardware. In this view, discrete neural events and continuous physical processes form a tightly coupled system that cannot be reduced to symbolic information processing.<\/p>\n
The theory suggests that digital AI, despite its capabilities, may not recreate the essential computational style that gives rise to conscious experience. Instead, truly mind-like cognition may require building systems whose computation emerges from physical dynamics similar to those found in biological brains.<\/p>\n
Key Facts:<\/p>\n
Hybrid Dynamics: Brain computation arises from discrete spikes embedded within continuous chemical and electrical fields.Multi-Scale Coupling: Neural processes remain deeply intertwined across levels, meaning algorithms cannot be separated from physical implementation.Energetic Constraints: Metabolic limits shape neural computation, influencing learning, stability, and information flow.<\/p>\n
Source: Estonian Research Council<\/p>\n
Right now, the debate about consciousness often feels frozen between two entrenched positions. <\/p>\n
On one side sits\u00a0computational 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.<\/p>\n
On the other side is\u00a0biological naturalism, which insists that consciousness is inseparable from the distinctive properties of living brains and bodies: biology isn\u2019t just a vehicle for cognition, it is part of what cognition\u00a0is.<\/p>\n
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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. Credit: Neuroscience News<\/p>\n
Each camp captures something important, but the stalemate suggests that something is missing from the picture.<\/p>\n
In our new paper, we argue for a third path:\u00a0biological computationalism. The idea is deliberately provocative but, we think, clarifying. Our core claim is that the\u00a0traditional computational paradigm is broken\u00a0or at least badly mismatched to how real brains operate.<\/p>\n
For decades, it has been tempting to assume that brains \u201ccompute\u201d 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.<\/p>\n
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 \u201ccomputation\u201d in the first place.<\/p>\n
Biological computation, as we describe it, has three defining properties.<\/p>\n
First, it is\u00a0hybrid: it combines\u00a0discrete events\u00a0with\u00a0continuous 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.<\/p>\n
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.<\/p>\n
Second, it is\u00a0scale-inseparable. In conventional computing, we can draw a clean line between software and hardware, or between a \u201cfunctional level\u201d and an \u201cimplementation level.\u201d In brains, that separation is not clean at all.<\/p>\n
There is no tidy boundary where we can say:\u00a0here is the algorithm, and\u00a0over 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.<\/p>\n
Changing the \u201cimplementation\u201d changes the \u201ccomputation,\u201d because in biological systems, those are deeply entangled.<\/p>\n
Third, biological computation is\u00a0metabolically 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.<\/p>\n
In this view, tight coupling across levels is not accidental complexity. It is an\u00a0energy optimization strategy: a way to produce robust, adaptive intelligence under severe metabolic limits.<\/p>\n
These three properties lead to a conclusion that can feel uncomfortable if we are used to thinking in classical computational terms:\u00a0computation 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 \u201cmere implementation.\u201d<\/p>\n
Instead, in biological computation, the\u00a0algorithm is the substrate. The physical organization does not just\u00a0support\u00a0the computation; it\u00a0constitutes\u00a0it. Brains don\u2019t merely run a program. They\u00a0are\u00a0a particular kind of physical process that performs computation by unfolding in time.<\/p>\n
This also highlights a key limitation in how we often talk about contemporary AI. Current systems, for all their power, largely\u00a0simulate 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.<\/p>\n
Brains, by contrast,\u00a0instantiate computation in physical time. Continuous fields, ion flows, dendritic integration, local oscillatory coupling, and emergent electromagnetic interactions are not just biological \u201cdetails\u201d we might safely ignore while extracting an abstract algorithm.<\/p>\n
In our view, these are\u00a0the computational primitives\u00a0of the system. They are the mechanism by which the brain achieves real-time integration, resilience, and adaptive control.<\/p>\n
This does\u00a0not\u00a0mean we think consciousness is magically exclusive to carbon-based life. We are not making a \u201cbiology or nothing\u201d argument.<\/p>\n
What we are claiming is more specific: if consciousness (or mind-like cognition) depends on this kind of computation, then it may require\u00a0biological-style computational organization, even if it is implemented in\u00a0new substrates.<\/p>\n
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.<\/p>\n
That shift changes the target for anyone interested in synthetic minds. If the brain\u2019s computation is inseparable from the way it is physically realized, then\u00a0scaling digital AI alone may not be sufficient.\u00a0Not because digital systems can\u2019t become more capable, but because capability is only part of the story.<\/p>\n
The deeper challenge is that we might be optimizing the wrong thing: improving algorithms while leaving the underlying computational\u00a0ontology\u00a0untouched.<\/p>\n
Biological computationalism suggests that to engineer genuinely mind-like systems, we may need to build\u00a0new 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.<\/p>\n
So, if we want something like\u00a0synthetic consciousness, the problem may not be, \u201cWhat algorithm should we run?\u201d The problem may be, \u201cWhat kind of physical system must exist for that algorithm to be inseparable from its own dynamics?\u201d<\/p>\n
What are the necessary features\u2014hybrid event\u2013field interactions, multi-scale coupling without clean interfaces, energetic constraints that shape inference and learning\u2014such that computation is not an abstract description laid on top, but an intrinsic property of the system itself?<\/p>\n
That is the shift biological computationalism demands: moving from a search for the right\u00a0program\u00a0to a search for the right\u00a0kind of computing matter.<\/p>\n
Key Questions Answered:Q: What problem does the new framework aim to solve?<\/p>\n
A: It addresses the stalemate between theories that view consciousness as pure information processing and those that ground it exclusively in biology, proposing a model that integrates computation with physical dynamics.<\/p>\n
Q: Why can\u2019t brain computation be treated like conventional digital computation?<\/p>\n
A: Biological computation depends on continuous physical processes, energy constraints, and multi-scale interactions that fundamentally change how information is represented and transformed.<\/p>\n
Q: What does this imply for creating synthetic consciousness?<\/p>\n
A: If consciousness depends on biological-style computation, then future artificial systems may need new physical architectures\u2014not just scaled-up digital algorithms\u2014to replicate mind-like properties.<\/p>\n
Editorial Notes:This article was edited by a Neuroscience News editor.Journal paper reviewed in full.Additional context added by our staff.About this consciousness and AI research news<\/p>\n
Author: Merilin Reede<\/a>
Source: Estonian Research Council<\/a>
Contact: Merilin Reede \u2013 Estonian Research Council
Image: The image is credited to Neuroscience News<\/p>\n