Erik-JanAnother friday, another 3-hour scheduled "focused research work" time block passed doing more urgent emails and teaching stuff and budget stuff. Any tips? #AcademicChatter
Dan Levenstein
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Neuroscientist, in theory.
Studying sleep in 🧠s and 💻s with Blake Richards and Adrien Peyrache at Mila/McGill.
An emergent property of a few billion neurons, their interactions with each other and the world over ~1 century.
Counting chickens 🥚🐣🐥.
Realized today there was a time (circa 2016-2020?) when #ScienceTwitter was an amazing resource for career advice. Long threads re: “how to apply for grad school”, “what to expect in a chalk talk”, and other highlights from the hidden curriculum.
A practice to revive as we settle in new places?
Reflecting on the role of the academy in the mid-21st century.
@adredish have you heard of any others?
Curious to know if anyone has used our "Role of Theory and Modeling in Neuroscience" paper in a course? Have heard of it being used in a few grad courses, and all of a sudden that seems quite relevant as I write up job applications... 😬
On the Role of Theory and Modeling in Neuroscience
In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative models and theories each play a distinct role in defining and bridging levels of abstraction, will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment.
Looking forward to visiting the SWC, and presenting our work on sequential predictive learning at the Emerging Neuroscientists Seminar Series! 🙏
From: @SWC_Neuro
https://neuromatch.social/@SWC_Neuro/113197768422647079
Sainsbury Wellcome Centre (@[email protected])
Attached: 1 image Congratulations to the SWC Emerging Neuroscientists Seminar Series 2024/25 winners!🎉 Laura Grima - HHMIJanelia Daniel Levenstein @dlevenstein - McGill University Chris Zimmerman - Princeton Neuroscience Institute Noel Federman - Institute of Biomedical Research Buenos Aires Join us for their talks, starting Jan 2025 📅 https://www.sainsburywellcome.org/web/seminar/swc-emerging-neuroscientists-seminar-series-202425-winners-announced
Sainsbury Wellcome CentreCongratulations to the SWC Emerging Neuroscientists Seminar Series 2024/25 winners!🎉
Laura Grima - HHMIJanelia
Daniel Levenstein @dlevenstein - McGill University
Chris Zimmerman - Princeton Neuroscience Institute
Noel Federman - Institute of Biomedical Research Buenos Aires
Join us for their talks, starting Jan 2025 📅
Another note - we’re still putting the last touches on everything. This truly is a “preprint” in the sense that it’s pre-submission. If you have questions, concerns, or suggestions, we’d love to discuss and try to address them in the submitted version!
Super thanks to
@tyrell_turing / @adrien and my PhD student collaborators
@alxecome and Roy Eyono. They both have great projects building on this work which I’m really excited about, so keep your eyes out 👀.
🧵/🧵
In sum, sequential predictive learning can account for hippocampal representation and replay, and is a candidate theory to unify three views of the hippocampus:
1) the hippocampus is a predictive map
2) the hippocampus is a CANN
3) the hippocampus is a sequence generator
1) the hippocampus is a predictive map
2) the hippocampus is a CANN
3) the hippocampus is a sequence generator