Publications

CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

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Mlynarski, W. & McDermott, J. H. Learning mid-level codes for natural sounds. Computational and Systems Neuroscience (Cosyne) 2016 (2016). at <http://www.cosyne.org/c/index.php?title=Cosyne2016_posters_2>

Wiktor_COSYNE_2015_hierarchy_final.pdf (2.52 MB)

Mlynarski, W. & McDermott, J. H. Learning Mid-Level Codes for Natural Sounds. Advances and Perspectives in Auditory Neuroscience (2016).

APAN_large_JHM kopia.pdf (19.74 MB)

Mlynarski, W. & McDermott, J. H. Adaptive Compression of Statistically Homogenous Sensory Signals. Computational and Systems Neuroscience (COSYNE) (2017).

Mlynarski, W. & McDermott, J. H. Co-occurrence statistics of natural sound features predict perceptual grouping. Computational and Systems Neuroscience (Cosyne) 2018 (2018).

Mlynarski, W. & McDermott, J. H. Learning Mid-Level Auditory Codes from Natural Sound Statistics. (2017).

MlynarskiMcDermott_Memo060.pdf (7.11 MB)

Mlynarski, W. & McDermott, J. H. Co-occurrence statistics of natural sound features predict perceptual grouping. Computational and Systems Neuroscience (COSYNE) (2018). at <http://www.cosyne.org/c/index.php?title=Cosyne_18>

Mlynarski, W. & McDermott, J. H. Lossy Compression of Uninformative Stimuli in the Auditory System. Association for Otolaryngology Mid-Winter Meeting (2017).

Mlynarski, W. & Hermundstad, A. M. Adaptive Coding for Dynamic Sensory Inference. eLife (2018).

Mlynarski, W. & McDermott, J. H. Learning Mid-Level Codes for Natural Sounds. Association for Otolaryngology Mid-Winter Meeting (2017).

Misra, P., Marconi, A., Peterson, M. F. & Kreiman, G. Minimal memory for details in real life events. Scientific Reports 8, (2018).

Miconi, T., Groomes, L. & Kreiman, G. A normalization model of visual search predicts single trial human fixations in an object search task. (2014).

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 [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. There’s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task [dataset]. (2016).

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. Neural Networks 121, 229 - 241 (2020).

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. 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)

Mhaskar, H. & Poggio, T. Function approximation by deep networks. Communications on Pure & Applied Analysis 19, 4085 - 4095 (2020).

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).

Mhaskar, H. & Poggio, T. 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)

Mhaskar, H. & Poggio, T. Deep vs. shallow networks : An approximation theory perspective. (2016).

Original submission, visit the link above for the updated version (960.27 KB)

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., 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

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