Podcast with Dmitri Chklovskii on predictive coding and lattice filter

Can the brain's visual wiring be explained by the same engineering principles that optimize telephone networks? Dmitri Chklovskii shows how predictive coding theory and lattice filters map onto real neural circuits, from fly photoreceptors to the mammalian LGN.

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Chklovskii bridges theoretical physics and neuroscience by applying adaptive signal processing frameworks to sensory systems. Building on Barlow's redundancy reduction principle and the predictive coding work of Srinivasan, Laughlin, and Dubs, his group derives normative predictions for neural filter shapes with no free parameters: once you specify the natural stimulus statistics and signal-to-noise ratio, the optimal filter is uniquely determined. The biphasic temporal response and center-surround spatial receptive fields of retinal and LGN neurons emerge naturally as mechanisms for subtracting predictions from incoming signals, compressing redundant information.

The key evidence supporting this framework over simple biophysical explanations like after-hyperpolarization comes from stimulus-dependent filter changes. At high contrast, neurons show sharp biphasic responses with strong negative components; at low contrast, the filter shifts toward broader low-pass characteristics with weakened negative phases. This adaptive behavior matches predictive coding predictions but would require different physiological implementations at each contrast level, suggesting the filter shape is functionally optimized rather than a fixed biophysical artifact.

Chklovskii introduces the lattice filter as a specific circuit implementation where decorrelation occurs in hierarchical stages, each operating at a different timescale. This architecture predicts that LGN temporal receptive fields should be longer than retinal ones, which matches electrophysiological observations. It also predicts two distinct LGN cell types corresponding to forward and backward prediction error pathways, identifiable with the known lagged and non-lagged cell classes. At Janelia Farm, his group has reconstructed the connectome of the fly visual system through the first two neuropils, mapping approximately 10,000 synaptic connections among 50 neurons per processing column. The L1 and L2 large monopolar cells show response properties consistent with the dual pathways of a lattice filter, and inter-column connections provide the substrate for motion detection.