"More Is Different'' in Neural Circuits: Algebraic Emergence of Effective Theories in Canonical Recurrent Motifs of Biological Neuronal Networks
2026-08-31 • Formal Languages and Automata Theory
Formal Languages and Automata TheoryNeural and Evolutionary Computing
AI summaryⓘ
The authors explain common neural circuit patterns, like divisive normalization and winner-take-all competition, using algebra to understand how they behave individually and when combined. They show that while each part’s updates might seem simple, combining them can create more complex behaviors that only appear when circuits work together. Specifically, they find that mixing these motifs produces cycles and group structures in the system's behavior that don't exist in the single parts alone. This work suggests that neural circuits can be thought of as programmable systems where the way pieces are combined affects what computations they can perform.
divisive normalizationwinner-take-all (WTA)neural circuitstransition monoidtransformation systemsaperiodic updatesKrohn-Rhodes cascadeholonomy analysisrecurrent excitationshared inhibition
Authors
Nima Dehghani
Abstract
Canonical neural circuit motifs are usually described functionally: divisive normalization rescales population activity by a pooled signal, and winner-take-all competition selects one pattern through recurrent excitation and shared inhibition. We represent them, and their compositions, algebraically as finite transformation systems and analyze the transition monoids generated by their input-conditioned updates, distinguishing structure already present in a generator from structure that appears only through composition, and, on a joint state space, structure inherited from one factor from structure that lives on a joint configuration. Individually aperiodic updates can generate non-aperiodic monoids. In the WTA, every frozen-drive generator collapses to fixed points, yet short input sequences create local cycles of winner-dependent inhibitory gating: globally dissipative dynamics with a reversible action. The strongest result arises in WTA-to-DN composition. The composed monoid then contains a genuinely composite local cycle in which normalization state and the winner's gating state change together, although every primitive generator is aperiodic. Holonomy analysis certifies this as a group component of the Krohn-Rhodes cascade rather than an incidental cycle, and finds most group-carrying image sets on joint configurations, whereas the uncoupled product has none. An exhaustive interface sweep shows that the composite cycle is a property of the coupling rather than of a chosen map. If motifs are building blocks of neural computation, composing them is a form of programming: one chooses primitives and interfaces so that the generated algebra has the intended repertoire. The transition monoid is that repertoire - what a primitive presents to any later construction. Recurrent circuits are compositional transformation systems; their algebra constrains what they can be programmed to compute.