[Verse 1] The camera clicks and captures light, but that's where likeness ends CNN flows straight ahead while brain circuits bend Feedforward stacks march single file through layers clean and neat But biological vision loops back, makes the cycle complete [Chorus] Where silicon meets flesh, the pathways split in two Feedforward versus recurrent loops, spikes versus values smooth Backprop trains the artificial mind with gradients flowing back While Hebbian rules and local codes keep biology on track [Verse 2] In primate IT cortex, some images take their time The settling loop keeps working when first pass can't define Those tricky visual puzzles that make the monkeys pause Are exactly where the CNNs break their feedforward laws [Chorus] Where silicon meets flesh, the pathways split in two Feedforward versus recurrent loops, spikes versus values smooth Backprop trains the artificial mind with gradients flowing back While Hebbian rules and local codes keep biology on track [Verse 3] Artificial neurons whisper numbers soft and continuous But real neurons fire in bursts, timing synchronous Rate codes tell us how much, but spikes encode the when Latency and synchrony dance in the temporal den [Bridge] Add some recurrence to the net, CORnet-R shows the way Closing gaps between machine and brain processing day by day But adversarial attacks still fool the pixel-reading eye While human vision sees the shape that textures can't deny [Verse 4] The global gradient flows backward through each weighted connection But neurons can't send error signals in reverse direction STDP and BCM rules, three factors gate the learning Local plasticity solves the puzzle that researchers are discerning [Verse 5] Object recognition scales through the ventral visual stream V1 detects the edges, IT holds the semantic dream But cortical columns organize in ways we've yet to code While artificial layers stack like freight along a road [Chorus] Where silicon meets flesh, the pathways split in two Feedforward versus recurrent loops, spikes versus values smooth Backprop trains the artificial mind with gradients flowing back While Hebbian rules and local codes keep biology on track [Outro] Fragile networks stumble on perturbations microscopic While robust biological vision stays philosophic The divergence shows us clearly what the feedforward stack is missing Recurrent loops and spiking codes are what we should be wishing [Chorus] Where silicon meets flesh, the pathways split in two Feedforward versus recurrent loops, spikes versus values smooth Backprop trains the artificial mind with gradients flowing back While Hebbian rules and local codes keep biology on track
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