Publication
] Differences in dynamic and static coding within different subdivision of the prefrontal cortex. Society for Neuroscience's Annual Meeting - SfN 2017 (2017). at <http://www.abstractsonline.com/pp8/#!/4376/presentation/4782>
NeuroDecodeR: A package for neural decoding analyses in R. bioRxiv (2022). at <https://www.biorxiv.org/content/10.1101/2022.12.17.520811v1>
Differential Processing of Isolated Object and Multi-item Pop-Out Displays in LIP and PFC. Cerebral Cortex (2017). doi:10.1093/cercor/bhx243
NeuroDecodeR: a package for neural decoding in RData_Sheet_1.docx. Frontiers in Neuroinformatics 17, (2024).
The Neural Decoding Toolbox. (2013). at <http://www.readout.info/>
A Data Science approach to analyzing neural data. Joint Statistical Meetings (2017).
How PFC and LIP process single and multiple-object ‘pop-out’ displays. Society for Neuroscience (2015). at <https://www.sfn.org/~/media/SfN/Documents/Annual%20Meeting/FinalProgram/NS2015/Full%20Abstract%20PDFs%202015/SfN15_Abstracts_PDF_Nanos.ashx>
New Data Science tools for analyzing neural data and computational models. Society for Neuroscience (2016).
Dynamic population coding and its relationship to working memory. Journal of Neurophysiology 120, 2260 - 2268 (2018).
Intelligent Information Loss: The Coding of Facial Identity, Head Pose, and Non-Face Information in the Macaque Face Patch System. The Journal of Neuroscience 35, (2015).
Review of the CBMM workshop on the Turing++ Question: 'who is there?'. (2016).
Review of the CBMM workshop on the Turing++ Question- 'who is there?' .pdf (555.71 KB)
Review of the CBMM workshop on the Turing++ Question- 'who is there?' .pdf (555.71 KB) Function approximation by deep networks. Communications on Pure & Applied Analysis 19, 4085 - 4095 (2020). 1534-0392_2020_8_4085.pdf (514.57 KB)
1534-0392_2020_8_4085.pdf (514.57 KB) When and Why Are Deep Networks Better Than Shallow Ones?. AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence (2017).
Deep vs. shallow networks : An approximation theory perspective. (2016). Original submission, visit the link above for the updated version (960.27 KB)
Original submission, visit the link above for the updated version (960.27 KB) Learning Functions: When Is Deep Better Than Shallow. (2016). at <https://arxiv.org/pdf/1603.00988v4.pdf>
An analysis of training and generalization errors in shallow and deep networks. Neural Networks 121, 229 - 241 (2020).
An analysis of training and generalization errors in shallow and deep networks. (2018). CBMM-Memo-076.pdf (772.61 KB) CBMM-Memo-076v2.pdf (2.67 MB)
CBMM-Memo-076.pdf (772.61 KB) CBMM-Memo-076v2.pdf (2.67 MB) An analysis of training and generalization errors in shallow and deep networks. (2019). CBMM-Memo-098.pdf (687.36 KB) CBMM Memo 098 v4 (08/2019) (2.63 MB)
CBMM-Memo-098.pdf (687.36 KB) CBMM Memo 098 v4 (08/2019) (2.63 MB) Deep vs. shallow networks: An approximation theory perspective. Analysis and Applications 14, 829 - 848 (2016).
There's Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task. Cerebral Cortex 26(7), 26:3064-3082 (2016).