[Verse 1]
In twenty-sixteen a paper dropped knowledge so profound
Lillicrap showed us feedback weights don't need to be crowned
Random connections flowing backward through the neural maze
Still teach the network proper moves in computational ways
The brain don't need symmetric paths like backprop demands
Nature builds with messy wires but learning still expands
[Chorus]
Random feedback, random feedback, breaks the symmetry chains
Direct alignment, direct alignment, rewires learning's reins
No perfect mirrors in biology, yet gradients still flow
These cortical columns dance and grow, that's how the patterns go
[Verse 2]
Nøkland stepped up that same year with alignment so direct
Skip the backward pass entirely, connect output to reflect
Each hidden layer gets its signal straight from error's call
No need to propagate precisely, feedback weights enthrall
The mathematics still converge though pathways seem bizarre
Biological plausibility brings learning from afar
[Chorus]
Random feedback, random feedback, breaks the symmetry chains
Direct alignment, direct alignment, rewires learning's reins
No perfect mirrors in biology, yet gradients still flow
These cortical columns dance and grow, that's how the patterns go
[Bridge]
Bartunov came in twenty-eighteen to scale these ideas wide
Testing algorithms against the tide of deeper architectures
Can random weights survive the test when networks multiply
The scalability assessment shows where bio-learning flies
[Verse 3]
Three papers paint a picture of how cortex might compute
Without the weight transport problem that makes backprop mute
Synaptic feedback randomly wired but functionally sound
Local learning rules distributed across neural ground
The columns process information in parallel streams
While feedback shapes the learning through biological schemes
[Verse 4]
Evolution carved these circuits through millions of years past
No central planner needed when learning rules amass
Hebbian updates mixing with the random weight cascade
Neurons wire together when their patterns masquerade
The beauty of these systems lies in their robust design
Approximating gradients through feedback so divine
[Chorus]
Random feedback, random feedback, breaks the symmetry chains
Direct alignment, direct alignment, rewires learning's reins
No perfect mirrors in biology, yet gradients still flow
These cortical columns dance and grow, that's how the patterns go
[Outro]
From Lillicrap to Nøkland then Bartunov's testing ground
The mathematics of the cortex in these papers can be found
Random weights supporting learning, alignment showing way
Biological computation's here to stay