The backpropagation algorithm, while remarkably successful in machine learning, contradicts essential biological principles of brain function, making its exact implementation in neural tissue virtually impossible.

As neuroscience and artificial intelligence continue to inform each other, predictive coding stands as a compelling bridge between the remarkable learning capabilities of biological brains and the next generation of neural network architectures.

Individual neurons and synapses in the brain function as autonomous agents, modifying their states based solely on information physically available at their specific locations, operating as a massively parallel locally autonomous system where computation and learning occurs simultaneously throughout the network in a distributed manner without centralized control.

The predictive coding framework can be approached as an energy-based model by associating each possible network state with a single number representing abstract energy, then deriving rules for how the system should evolve to reduce this energy, paralleling physical systems that naturally progress towards minimum energy states.

Backpropagation requires discontinuous processing where neurons must freeze their feedforward activity values while error signals flow backward, requiring the brain to pause information processing for hundreds of milliseconds before updating connections.

Information flows bidirectionally through the predictive coding hierarchy with top-down connections carrying predictions from higher levels to lower levels, while bottom-up connections carry prediction errors (differences between predictions and actual activity).

In real models with nonlinear activation functions, the update rules for opposing synapses are not mathematically identical, but research suggests perfect symmetry may not be essential; approximate symmetry emerging from independent learning is sufficient for effective network function.

Predictive coding originated from mid-twentieth century research proposing that the brain's fundamental objective is to predict incoming sensory information, which enhances survival by allowing organisms to anticipate threats and interpret noisy observations.

From the update rules, a representational neuron must be inhibited by its corresponding error neuron and excited by error neurons sending feedback signals from the layer below, elegantly mapping the mathematical formulation onto biological circuitry.

Predictive coding's local autonomy makes the algorithm extremely parallelizable and in certain settings more efficient than back propagation, and theoretical considerations suggest that resulting updates may lead to better solutions than back propagation.