Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli

By Frédéric E. Theunissen, Stephen V. David, Nandini C. Singh, A. Hsu, William E. Vinje, Jack L. Gallant. 2001

236 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/fb0d617180f42bdcb0c7e2f96bceb2293fc7b5fb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Matteo Carandini
2. Eero P. Simoncelli
3. Valerio Mante
4. Matthias Bethge
5. Nicole C. Rust
6. Odelia Schwartz
7. Frédéric E. Theunissen
8. Barbara Olshausen

In Signal Processing

1. Eero P. Simoncelli
2. Matteo Carandini
3. Valerio Mante
4. Barbara Olshausen
5. Matthias Bethge
6. Fabian H Sinz
7. J. E. Elie
8. Frédéric E. Theunissen

Citations in the corpus

• Spike-triggered neural characterization. (2006, background): “More generally, it is possible to do the analysis in a space that is a nonlinear function of the input stixels (David, Vinje, & Gallant, 2004; Nishimoto, Ishida, & Ohzawa, 2006; Theunissen et al., 2001).”
• Do We Know What the Early Visual System Does? (2005, methodology): “This approach has already been used to determine precisely how well current models predict neuronal responses during natural vision (Theunissen et al., 2001; David et al., 2004; Prenger et al., 2004; David and Gallant, in press).”
• Highly Selective Receptive Fields in Mouse Visual Cortex (2008, methodology): “These include orientation and spatial frequency tuning, the presence of both simple and complex response types, and higher-order properties such as contrast-invariant tuning.”
• The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli (2016, methodology): “Regularization serves to prevent overfitting to such high-frequency, dataset-specific noise along the low-variance dimensions (Theunissen et al., 2001; Mesgarani et al., 2008).”
• Neural processing of natural sounds (2014, methodology): “ization regression techniques must be used when estimating the filter to prevent overfitting [43,44,46].”
• Statistical models for neural encoding, decoding, and optimal stimulus design. (2007, methodology): “, smoothness); Bayesian smoothing methods built in to STRFPak (Theunissen et al., 2001).”
• Rapid synaptic depression explains nonlinear modulation of spectro-temporal tuning in primary auditory cortex by natural stimuli (2009, methodology): “Measurements of prediction accuracy by correlation ignore signals at resolutions finer than the temporal window (Theunissen et al., 2001); thus, the correlation values reported in this study should not be interpreted as strict lower bounds on the portion of responses predicted by STRFs. Tuning…”
• Engineering a Less Artificial Intelligence. (2019, background): “System identification has a long tradition in neuroscience, especially for sensory areas such as audition (Knudsen and Konishi, 1978; Theunissen et al., 2001; Calabrese et al., 2011), touch (Chagas et al.”