| Time |
Speaker |
Title |
| 7/21, 11:00am |
Daniel Wolpert
Columbia University |
Keynote: Statistical learning in sensorimotor control[Video] |
| 7/21, 9:00am |
All Participants |
Roasting the program organizers |
| 7/21 |
|
Keynote lecture |
| 7/20, 11:00am |
Rob Froemke
New York University |
Love, death, and statistical learning[Video][CC][Transcript] |
| 7/20, 9:45am |
Li Zhang
University of Southern California |
"Dormant" Cells and Sparse Coding in Awake Auditory Cortex[Video][CC][Transcript] |
| 7/20, 9:00am |
Christine Constantinople
New York University |
Neural mechanisms of inference[Embargoed] |
| 7/20 |
|
Statistical learning for interference and generalization |
| 7/19, 12:00pm |
Huizhong Tao
University of Southern California |
A bottom-up sensory pathway for reward associative learning[Video][CC][Transcript] |
| 7/19, 11:30am |
Yonatan Aljadeff
University of California San Diego |
How flies got their sensilla? Statistical learning on evolutionary timescales[Video][CC][Transcript] |
| 7/19, 11:00am |
Marta Zlatic
University of Cambridge, MRC Laboratory of Molecular Biology |
Combining brain-wide connectivity maps with brain-wide activity and behaviour maps to understand learning in Drosophila[Video][CC][Transcript] |
| 7/19, 9:00am |
Aurore Avargues-Weber
Centre de Recherche sur la Cognition Animale |
Mastering learning about relations: a honeybee perspective[Video][CC][Transcript] |
| 7/19 |
|
Studying statistical learning in animals |
| 7/18, 11:45am |
Marta Zlatic
University of Cambridge, MRC Laboratory of Molecular Biology |
How can we use the architecture of learning circuits to provide clues about learning algorithms and constrain learning models?[Video][CC][Transcript] |
| 7/18, 11:00am |
Mitya Chklovskii
Flatiron Institute |
Neurons as Direct Data-Driven Controllers (DD-DC) II[Video][CC][Transcript] |
| 7/18, 9:00am |
Peter Latham
University College London |
Mathematical framework of learning as inference and control[Video][CC][Transcript] |
| 7/18 |
|
What can we learn about the brain? |
| 7/17, 11:00am |
Israel Nelken
Hebrew University |
Prediction errors and predictions in the auditory system[Video][CC][Transcript] |
| 7/17, 10:00am |
Andrew Saxe
University College London |
Prediction as a learning objective[Video][CC][Transcript] |
| 7/17, 9:00am |
Floris de Lange
Radboud University, Donders Institute |
Prediction in statistical learning[Video][CC][Transcript] |
| 7/17 |
|
The role of prediction in statistical learning |
| 7/14, 12:00pm |
Peter Dayan
MPI-BK |
Accounting for Every Choice[Video][CC][Transcript] |
| 7/14, 11:30am |
Floris de Lange
RU Nijmegen |
Successor representation in human primary visual cortex and hippocampus[Video][CC][Transcript] |
| 7/14, 10:00am |
Jozsef Fiser
CEU |
Lamp-posts and hidden representations[Video][CC][Transcript] |
| 7/14, 9:00am |
Miguel Maravall
U. Sussex |
Parsing statistical learning[Video][CC][Transcript] |
| 7/14 |
|
The role of prediction in statistical learning |
| 7/10-13 |
Conference: |
Timescales of Plasticity and Underlying Mechanisms |
| 7/07, 11:00am |
Maneesh Sahani
(University College London)
Peter Latham
(University College London)
Mate Lengyel
(University of Cambridge, Central European University) |
Discussion: internal beliefs, uncertainty and learning[Video] |
| 7/07, 9:00am |
Maneesh Sahani
University College London |
Internal beliefs, uncertainty and learning[Video][CC][Transcript] |
| 7/07 |
|
On the ineluctable manifestation of uncertainty |
| 7/06, 11:30am |
Bernhard Englitz
Donders Inst. |
Hearing the needle in the haystack[Video][CC][Transcript] |
| 7/06, 11:00am |
Livia de Hoz
Charite |
Sensitivity of subcortical activity to statistics acquired slow and fast[Embargoed] |
| 7/06, 9:30am |
Eli Nelken
Hebrew Univ. |
Statistical learning in single neurons: data and models[Video][CC][Transcript] |
| 7/06, 9:00am |
Jennifer Linden
University College London |
Introduction to the session and Representation of Primitives for Statistical Learning in the Auditory System[Video][CC][Transcript] |
| 7/06 |
|
Statistical Learning in the Auditory System |
| 7/05, 12:00am |
All participants |
Dimensions of learning: Where does Statistical Learning map to?[Video] |
| 7/05, 11:00am |
Mitya Chklovskii
Flatiron Institute |
Volatility in neuronal circuits: bug or feature?[Video][CC][Transcript] |
| 7/05, 10:00am |
Michael Goard
UCSB |
Stability and volatility in the mouse visual system[Slides][Video][CC][Transcript] |
| 7/05, 9:00am |
Simon Rumpel
Univ. Mainz |
Experimental results on synaptic and representational drift[Slides][Video][CC][Transcript] |
| 7/05 |
|
The impact of intrinsic volatility in neuronal circuits on learning and memory |
| 7/03, 11:00am |
David Gross
KITP |
On ITP and KITP |
| 7/03, 10:00am |
All Participants |
Participant's one slide-introductions: part 6[Video][CC][Transcript] |
| 7/03, 9:00am |
Livia de Hoz
(Charite Berlin)
Jozsef Fiser
(Central European University) |
General introduction, information session, practicalities[Video] |
| 7/03 |
|
|
| 6/30, 11:00am |
Merav Ahissar
(Hebrew University of Jerusalem)
Sebastian Goldt
(Scuola Internazionale Superiore di Studi Avanzati)
Athena Akrami
(University College London) |
Discussion: making predictions concrete[Video][CC][Transcript] |
| 6/30, 9:00am |
Andrew Saxe
University College London |
Solvable models of deep learning dynamics, predictive coding and statistical learning[Video][CC][Transcript] |
--->
| 6/30 |
|
The dynamics of neural representations in predictive learning |
| 6/28 |
|
How task statistics and biology shape neural response |
| 6/26 |
|
Perception and Statistical Learning in Development |
| 6/23 |
|
From pairwise associations to higher-order correlations: what and how do neural networks learn from them? |
| 6/19 |
|
Implicit learning of Task Structure |
