When random variation results in functional significance
Barfield, J. H.; Kells, P. A.; Gautam, S. H.; Li, J.; Shew, W.
Show abstract
Many functional properties vary dramatically across neurons in cerebral cortex. Two fundamental goals of systems neuroscience are to determine which neurons execute which functions and how the different functional properties of a neuron are related. Often, it is assumed that if two properties are uncorrelated, then there is no important relationship to report. Here we show that this assumption can lead to wrong conclusions; functional segregation can emerge, by chance, due to random variation when that variation is distributed according to skewed, heavy-tailed distributions. We reinterpret the results we previously reported in Kells et al 2019 [1], showing that they are a prime example of functional segregation due to random variation.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Comparing surrogates to evaluate precisely timed higher-order spike correlations 95%
- Synchrony in Networks of Type 2 Interneurons is More Robust to Noise with Hyperpolarizing Inhibition Compared to Shunting Inhibition in Both the Stochastic Population Oscillator and the Coupled Oscillator Regimes 94%
- A Stochastic Dynamic Operator framework that improves the precision of analysis and prediction relative to the classical spike-triggered average method, extending the toolkit. 94%
Similar papers in this journal
- The unbiased estimation of the fraction of variance explained by a model 94%
- Emergence of Sparse Coding, Balance and Decorrelation from a Biologically-Grounded Spiking Neural Network Model of Learning in the Primary Visual Cortex 94%
- Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson's disease 94%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.