Neural correlates of sparse coding and dimensionality reduction
Fig 8
Comparison between experimental data and two computational models of rat RSC suggest a functional similarity between STDPH and NMF.
Rats used two turn sequences (inbound: LRL; outbound: RLR) to traverse a W-shaped track located at two different allocentric locations (α, β). (A) Experimental data from [109]. (B) Simulated using NMF with sparsity constraints. (C) Simulated by evolving STDPH parameters to fit experimental data [127, 128]. Left column: Functional neuron type distributions. Right column: Location prediction errors. The prediction error is based on how well the neuronal population response can predict the rat's location on the maze. For details, see [50, 109]. Prediction error when comparing even and odd trials on the same maze in the same location in the room (prefix α or β) was much smaller than when the same maze was in different locations (prefix αβ; Kruskal-Wallis and Tukey's range test, *** = p<0.001), demonstrating that the network can distinguish similar routes that occur in different allocentric positions. For details see Supporting information. LRL, left-right-left; NMF, nonnegative matrix factorization; RLR, right-left-right; RSC, retrosplenial cortex; STDPH, spike-timing–dependent plasticity and homeostatic synaptic scaling.