Publication

Found 906 results
[ Author(Desc)] Title Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
M
Meyers, E. M. NeuroDecodeR: A package for neural decoding analyses in R. bioRxiv (2022). at <https://www.biorxiv.org/content/10.1101/2022.12.17.520811v1>
Meyers, E., Liang, A., Katsuki, F. & Constantinidis, C. Differential Processing of Isolated Object and Multi-item Pop-Out Displays in LIP and PFC. Cerebral Cortex (2017). doi:10.1093/cercor/bhx243
Meyers, E. M. NeuroDecodeR: a package for neural decoding in RData_Sheet_1.docx. Frontiers in Neuroinformatics 17, (2024).
Meyers, E. A Data Science approach to analyzing neural data. Joint Statistical Meetings (2017).
Meyers, E. 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>
Meyers, E., Dean, M. & Hale, G. J. New Data Science tools for analyzing neural data and computational models. Society for Neuroscience (2016).
Meyers, E. The Neural Decoding Toolbox. (2013). at <http://www.readout.info/>
Meyers, E. Dynamic population coding and its relationship to working memory. Journal of Neurophysiology 120, 2260 - 2268 (2018).
Meyers, E. Review of the CBMM workshop on the Turing++ Question: 'who is there?'. (2016).PDF icon Review of the CBMM workshop on the Turing++ Question- 'who is there?' .pdf (555.71 KB)
Mhaskar, H., Liao, Q. & Poggio, T. When and Why Are Deep Networks Better Than Shallow Ones?. AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence (2017).
Mhaskar, H. & Poggio, T. Deep vs. shallow networks : An approximation theory perspective. (2016).PDF icon Original submission, visit the link above for the updated version (960.27 KB)
Mhaskar, H. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. Neural Networks 121, 229 - 241 (2020).
Mhaskar, H., Liao, Q. & Poggio, T. Learning Functions: When Is Deep Better Than Shallow. (2016). at <https://arxiv.org/pdf/1603.00988v4.pdf>
Mhaskar, H. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. (2018).PDF icon CBMM-Memo-076.pdf (772.61 KB)PDF icon CBMM-Memo-076v2.pdf (2.67 MB)
Mhaskar, H. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. (2019).PDF icon CBMM-Memo-098.pdf (687.36 KB)PDF icon CBMM Memo 098 v4 (08/2019) (2.63 MB)
Mhaskar, H. & Poggio, T. Function approximation by deep networks. Communications on Pure & Applied Analysis 19, 4085 - 4095 (2020).PDF icon 1534-0392_2020_8_4085.pdf (514.57 KB)
Mhaskar, H. & Poggio, T. Deep vs. shallow networks: An approximation theory perspective. Analysis and Applications 14, 829 - 848 (2016).
Miconi, T., Groomes, L. & Kreiman, G. There’s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task [code]. (2016).
Miconi, T., Groomes, L. & Kreiman, G. 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).
Miconi, T., Groomes, L. & Kreiman, G. A normalization model of visual search predicts single trial human fixations in an object search task. (2014).PDF icon CBMM-Memo-008.pdf (854.51 KB)
Miconi, T., Groomes, L. & Kreiman, G. There’s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task [dataset]. (2016).
Misra, P., Marconi, A., Peterson, M. F. & Kreiman, G. Minimal memory for details in real life events. Scientific Reports 8, (2018).
Mlynarski, W. & Hermundstad, A. M. Adaptive Coding for Dynamic Sensory Inference. eLife (2018).
Mlynarski, W. & McDermott, J. H. Learning Mid-Level Auditory Codes from Natural Sound Statistics. (2017).PDF icon MlynarskiMcDermott_Memo060.pdf (7.11 MB)
Mlynarski, W. & McDermott, J. H. Learning Mid-Level Codes for Natural Sounds. Advances and Perspectives in Auditory Neuroscience (2016).PDF icon APAN_large_JHM kopia.pdf (19.74 MB)

Pages