Organoid Intellect

October 10, 2026 Autonomous prediction on self-assembled nanowire reservoirs

A University of Sydney group shows simulated neuromorphic nanowire networks predicting a chaotic time series entirely on their own, no teacher signal, at delays up to tau 21. It sets the task protocol and the competitor bar that organoid reservoirs will have to clear.

October 10, 2026 Measuring how close neural tissue sits to criticality

The branching ratio is the most-used proxy for criticality in neural systems, and its raw statistics turn out not to locate criticality at all. A new analysis shows Fisher information of that same observable does, giving a continuous, model-free distance readout computable from spike counts.

October 9, 2026 Every network has a price list for computation

Kulkarni, Kim, Fotiadis, Pasqualetti, and Bassett define a computational affordance landscape from the controllability Gramian of a network, pricing every activity transition by the input energy it demands. In the fly head-direction circuit the cheapest computation is exactly its known function with anatomy-matched inputs; human sensory networks have heterogeneous landscapes and association networks homogeneous ones; and training recurrent networks progressively sculpts the landscape toward specialization.

October 9, 2026 Predictive coding beyond the Gaussian neuron

Kataoka and Doya show the correspondence between the free-energy principle and predictive coding survives when posterior and prior are exponential-family distributions rather than Gaussian, holding up to the second posterior cumulant. The resulting networks may use arbitrary nonlinear, neuron-specific activation curves and train with local plasticity rules, and the authors demonstrate stable free-energy reduction in a two-layer spiking network on sequential MNIST.

October 9, 2026 A programmable decoder that wins on scarce neural data

A single-author preprint introduces VN-SST, a Transformer variant whose feed-forward block is synthesized per token from a low-rank instruction bank read from a carried low-dimensional state. On three public motor-cortex benchmarks it beats a matched Transformer under scarce data, turns longer context into rising rather than falling accuracy, and runs its computation on roughly 3 bits of program per token.

October 8, 2026 Learning as braid words, not gradient steps

Exceptional-point braiding in a non-Hermitian Hamiltonian yields a discrete program space with quantized invariants. Gradient-free braid programming is a speculative but rigorous answer to a question wetware computing keeps asking.