Brain Criticality - Optimizing Neural Computations

Physical systems undergo phase transitions when moving from one well-defined organizational state to another—water molecules transitioning between liquid and gaseous phases represent paradigmatic example with profound implications for understanding brain dynamics.

Ising model developed explaining magnet properties consists of lattice where each site holds spin (+1 or -1) representing particles generating magnetic fields—when spins align, individual fields sum producing macroscopic magnetization; when random, fields cancel eliminating macro-scale magnetism.

Ising lattice exhibits crosstalk similar to neural communication across brain—flipping one spin affects neighbors, propagating information potentially across entire lattice despite only nearest-neighbor interactions explicitly defined, demonstrating how long-range communication emerges from purely local interactions.

Critical brain hypothesis receiving significant attention and experimental evidence recent years states neural networks operate near phase transition point—critical state where neurons exist in intermediate condition enabling optimal information processing through largest number of synapses.

Neuronal avalanches represent cascading bursts of neural activity where initial neuron firing triggers neighbors activating their neighbors and so forth—propagating activity through network in chain-reaction resembling physical avalanches but involving neurons rather than particles.

John Beggs and Dietmar Plenz published seminal 2003 paper “Neuronal Avalanches in Neocortical Circuits” providing first experimental observations suggesting brain operates near critical point—grew neurons from rat somatosensory cortex on 8×8 electrode grid recording spontaneous activity patterns.

Power-law distributions P(s) ∝ s^(-α) characterize critical systems where probability of event with size s decreases as power of s—avalanche sizes, durations, and amplitudes exhibiting power-law relationships provide definitive statistical signature distinguishing criticality from subcritical or supercritical regimes.

Critical point maximizes brain information processing and computational power—understanding through information transmission guessing game where observer attempts inferring input layer activity from output layer responses across branching neural network model.

Brain hovering near critical point optimizes information processing through fine balance between excitation and inhibition—evolutionary pressure selecting neural architectures maintaining critical dynamics because computational advantages outweigh metabolic costs.