Surya Ganguli

@sganguli
1.3K Followers
152 Following
17 Posts
Associate Professor of Applied Physics at Stanford, and Departments of Computer Science, Electrical Engineering and Neurobiology. Research Scientist at Meta AI.
Okay. After a “rigorous totally unbiased survey” across multiple sites I have come to the conclusion that Bluesky is currently best for academic social media with highest SNR for technical topics. I am gonna primarily post and read there. Join us!
Hey all, so what in your experience is best for academic social media these days? Bluesky, Threads, or Mastodon?
Was curious - what topics do people think should be taught now in a modern intro to theoretical neuroscience course? Also have your opinions changed in the last 5 years? Or even 1 year?
Harvard’s Admissions Is Challenged for Favoring Children of Alumni

After the Supreme Court banned race-conscious affirmative action, activists filed a complaint, saying legacy admissions helped students who are overwhelmingly rich and white.

The New York Times

I love these soft profile pieces on theocratic fascists who blatantly violate the separation of church and state like they are just sincere actors following their principles, instead of forcing their narrow religious viewpoints on people’s lives.

Something something something “liberal media” something something.

https://www.washingtonpost.com/politics/2023/02/25/texas-judge-abortion-pill-decision/

The Texas judge who could take down the abortion pill

A devout Christian, Matthew Kacsmaryk has been shaped by his deep antiabortion beliefs

The Washington Post
@gershbrain There's an inspiring paper by @Katejjeffery, Pollack and Rovelli 2019 "On the Statistical Mechanics of Life: Schrödinger Revisited" https://www.mdpi.com/1099-4300/21/12/1211 that argues how life, and specifically complex structures, opens up the space of possible variations in the organization of components and therefore more entropy. And that statistically this implies life is entropically favoured.
#entropy #science
On the Statistical Mechanics of Life: Schrödinger Revisited

We study the statistical underpinnings of life, in particular its increase in order and complexity over evolutionary time. We question some common assumptions about the thermodynamics of life. We recall that contrary to widespread belief, even in a closed system entropy growth can accompany an increase in macroscopic order. We view metabolism in living things as microscopic variables directly driven by the second law of thermodynamics, while viewing the macroscopic variables of structure, complexity and homeostasis as mechanisms that are entropically favored because they open channels for entropy to grow via metabolism. This perspective reverses the conventional relation between structure and metabolism, by emphasizing the role of structure for metabolism rather than the converse. Structure extends in time, preserving information along generations, particularly in the genetic code, but also in human culture. We argue that increasing complexity is an inevitable tendency for systems with these dynamics and explain this with the notion of metastable states, which are enclosed regions of the phase-space that we call “bubbles,” and channels between these, which are discovered by random motion of the system. We consider that more complex systems inhabit larger bubbles (have more available states), and also that larger bubbles are more easily entered and less easily exited than small bubbles. The result is that the system entropically wanders into ever-larger bubbles in the foamy phase space, becoming more complex over time. This formulation makes intuitive why the increase in order/complexity over time is often stepwise and sometimes collapses catastrophically, as in biological extinction.

MDPI

@CoriBargmann
So glad you’re here, Cori!

Everyone: yes, this is ‘that Cori’. Wiser than wise, convincer of at least one US president to invest in brain research, overseer of ambitious goals to eliminate disease, worm whisperer and all around wonderful person. Follow Cori!

https://en.m.wikipedia.org/wiki/Cornelia_Bargmann

Cornelia Bargmann - Wikipedia

The A^* heuristic path planing algorithm reduces the search space of Dijkstra using a heuristic. A valid heuristic should not overestimate the distance to the target. https://en.wikipedia.org/wiki/A*_search_algorithm
A* search algorithm - Wikipedia

Our "Beyond Neural Scaling laws" paper got a #NeurIPS22 outstanding paper award! Congrats to awesome collaborators Ben Sorscher, Robert Geirhos, Shashank Sekhar & Ari Morcos!
awards: https://blog.neurips.cc/2022/11/21/announcing-the-neurips-2022-awards/
paper: https://arxiv.org/abs/2206.14486
🧵
https://twitter.com/SuryaGanguli/status/1542599453659451392?s=20&t=rIijKXtOKyHpGmtTh50_XA
Announcing the NeurIPS 2022 Awards – NeurIPS Blog

Our new paper in Neuron: "A unified theory for the computational and mechanistic origins of grid cells" lead by Ben Sorscher & Gabriel Mel w/ Sam Ocko & Lisa Giocomo explains when & why grid cells appear (or don't) in trained neural path-integrators
https://authors.elsevier.com/c/1f~Ze3BtfH1ZNm