# Unconventional Computing How can physical, chemical and biological dynamics implement specified computations? Covers computing with living, chemical and bodily dynamics, including how inputs are encoded and results read out. ## What constitutes a computation - **Computation and dynamical regime** - Langton studies information storage, transmission and modification near transitions in cellular-automaton behavior. The proposed link between computation and criticality is a hypothesis drawn from the studied rule families. [Langton 1990]( "Computation at the edge of chaos: Phase transitions and emergent computation; Physica D"). - **Physical computation criteria** - Relates a physical system to an abstract computation through encoding, evolution and decoding. The proposed framework distinguishes performing a computation from observing dynamics that can be described mathematically. [Horsman et al. 2014]( "When does a physical system compute?; Proc. R. Soc. A"). ## Molecular computation - **DNA computation** - DNA strands encode candidate paths through a seven-node graph. Amplification and successive laboratory filters isolate a Hamiltonian path, visiting each node once. [Adleman 1994]( "Molecular Computation of Solutions to Combinatorial Problems; Science"). - **DNA associative memory** - DNA strand displacement implements a four-neuron Hopfield network that recalls stored patterns from incomplete inputs. Weights are trained in software, then encoded as molecular concentrations. [Qian et al. 2011]( "Neural network computation with DNA strand displacement cascades; Nature"). ## Physical and biological computations - **Pask's electrochemical ear** - Cariani documents electrochemical devices whose growing conductive structures acquired sensitivity to sound or magnetic fields. A historical account of changing the sensor itself, not only adjusting a controller. [Cariani 1993]( "To evolve an ear. Epistemological implications of Gordon Pask's electrochemical devices; Systems Research"). - **Slime-mould maze solving** - Physarum initially fills a small maze, then retracts into a short connection between two food sources. The assay demonstrates distributed path selection through growth and retraction. [Nakagaki et al. 2000]( "Maze-solving by an amoeboid organism; Nature"); [video](). - **Physarum transport networks** - Slime mould connects food sites representing Tokyo-area cities. Its networks balance cost, transport efficiency and fault tolerance comparably to the railway; a flow-dependent tube model captures adaptation. [Tero et al. 2010]( "Rules for Biologically Inspired Adaptive Network Design; Science"). - **Soft-body computation** - A driven silicone arm provides nonlinear dynamics and memory for trained readouts. It emulates temporal systems better than the tested direct linear readout, but worse than an optimized software reservoir. [Nakajima et al. 2015]( "Information processing via physical soft body; Sci. Rep."). - **Iontronic memory** - Salt redistribution gives aqueous channels fading conductance memory. Tests at three channel lengths support retention time proportional to length squared. Software classifiers use measured pulse responses to recognize digits. [Kamsma et al. 2024]( "Brain-inspired computing with fluidic iontronic nanochannels; PNAS"). ## Trainable physical systems - **Physics-aware training** - Physical forward passes and gradients from a differentiable digital model train optical, electronic and mechanical systems for classification. Measuring hardware during training reduces errors from simulation mismatch. [Wright et al. 2022]( "Deep physical neural networks trained with backpropagation; Nature"). - **Contrastive local learning** - Twin transistor networks learn XOR and nonlinear regression by locally comparing free and target-clamped voltages. Circuit dynamics update parameters without a digital model; training inputs and targets are externally supplied. [Dillavou et al. 2024]( "Machine learning without a processor: Emergent learning in a nonlinear analog network; PNAS"). - **Physical-network training** - Training can optimize a digital surrogate, combine measured hardware outputs with digital gradients, or update parameters through physical dynamics. Model accuracy, measurement access and drift constrain the approaches a substrate supports. [Momeni et al. 2025]( "Training of physical neural networks; Nature"). ## Open questions - How much of a demonstrated computation is performed by the substrate versus preprocessing, readout or external feedback? - Does performance persist after accounting for preparation time, calibration, energy, error and repeated use? ## Related topics - [Hyperorganism](https://hyperorganism.github.io/) - [Basal cognition](https://hyperorganism.github.io/basal-cognition/) - A computational use of a substrate does not settle its own behavioral capacities. - [Embodiment](https://hyperorganism.github.io/embodiment/) - Body dynamics can contribute to computation and control. - [Digital life](https://hyperorganism.github.io/digital-life/) - Reproducing computational systems pose a different question from using a substrate to calculate. - [Biohybrid Systems](https://hyperorganism.github.io/biohybrid-systems/)