Why your brain can remember everything even if half is gone

Why your brain can remember everything even if half is gone
Image: Intellect (generated)

In short: Traditional neuroscience views memory as a series of specific locations, like files in a cabinet. But what if your mind works more like a hologram? In a hologram, every tiny piece contains the blueprint for the entire image, allowing for a type of non-local storage that defies standard biological logic.

Developed by neuroscientist Karl Pribram and influenced by physicist David Bohm, Holonomic Brain Theory suggests that human cognition is modeled as a holographic storage network. Unlike traditional models that focus on neurons firing in specific clusters, this theory proposes that memories are encoded as interference patterns of electric oscillations within the brain's fine-fibered dendritic webs. These waves are distinct from the common action potentials of axons and synapses, functioning more like the wave interference patterns used to create laser-produced holograms.

'Non-locality' defines the primary process. Within a hologram, every segment of adequate dimensions holds the complete data set. Pribram argues that a piece of long-term memory is distributed over an entire dendritic arbor so that each part of the network contains the full information. This explains why certain children retain normal intelligence even after losing large portions of their cortex—the memory isn't in one spot; it is spread across the remaining network. It also accounts for 'fast associative memory,' where the brain connects disparate pieces of information instantly.

Pribram distinguishes this from a single brain-wide hologram, proposing 'patch holography' or holonomy. He suggests that smaller neural networks create localized holograms within the larger brain. Furthermore, he posits a dual-layer structure: a 'surface structure' of localized circuits acts as the retrieval mechanism, while a 'deep structure' of dendritic arborization binds everything together. This model offers a much larger theoretical storage capacity than traditional models like the Hopfield memory model, which suffers from early memory saturation.

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