The Brain’s Learning Algorithm Isn’t Backpropagation

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.

“However, there is a fundamental problem. The back propagation algorithm contradicts essential biological principles of brain function, making its exact implementation in neural tissue virtually impossible.”

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.

“For this process to work, neurons must essentially freeze their feed forward activity values like taking snapshots of activity and holding on to them while error signals flow backward.”

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.

“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.

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

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.

“This framework originated from midentth century research, proposing that the brain's fundamental objective is to predict incoming sensory information. From an evolutionary perspective, prediction enhances survival by allowing organisms to anticipate threats and interpret noisy observations.”

The credit assignment problem is the fundamental challenge that computational systems must solve: determining which parameters to adjust and by how much to achieve a desired output

“The fundamental challenge that computational systems must solve is called credit assignment. When you have a system with numerous parameters like connection weights between neurons that can be adjusted to achieve a desired output such as recognizing objects in an image or executing appropriate actions. How do you determine which parameters to adjust and by how much?”

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).

“Information flows birectionally through this hierarchy. Top-down connections carry predictions from higher levels to lower levels, while bottom up connections carry prediction errors, differences between predictions and the actual activity.”

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.

“The local autonomy makes the algorithm extremely parallelizable and in certain settings more efficient than back propagation. Theoretical considerations suggest that resulting updates may actually lead to better solutions than back propagation.”

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.

“We'll approach our network as a so-called energy- based model. Essentially, this means associating each possible network state with a single number representing some form of abstract energy. We can then derive rules for how the system should evolve to reduce this energy. This framework parallels physical systems that naturally progress towards minimum energy states like a ball rolling downhill to minimize gravitational potential energy or proteins folding to minimize atomic interaction energy.”

While some coordinating mechanisms like theta and gamma rhythms, attentional systems and neuromodulators such as dopamine influence broad neural populations, they operate at much coarser temporal and spatial scales than would be required for backpropagation which relies on cell-by-cell precision

“While there are some coordinating mechanisms, oscillations like theta and gamma rhythms, attentional systems and neurom modulators like dopamine that influence broad populations. These mechanisms operate at much coarser temporal and spatial scales than would be required for back propagation which relies on cellby cell precision.”

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.

“With this structure in mind, we can directly read off the required neural connectivity from our update rule. A representational neuron X subi must be inhibited by its corresponding error neuron and excited by error neurons sending feedback signals from the layer below. This elegantly maps our mathematical formulation onto biological circuitry.”