Dimos Karamanlis @dimokaramanlis@qoto.org

I'm trying to make it a habit to post some thoughts about neuroscience papers that I like on my blog.

Here 3 papers from computational neuroscience:

1. The Neuron as a Direct Data-Driven Controller from Dmitri Chklovskii's Lab

2. A Learning Algorithm beyond Backpropagation from Rafal Bogacz' Lab

3. Continuous vs. Discrete Representations in a Recurrent Network from Rainer Friedrich's Lab

https://gcamp6f.com/2024/03/26/three-recent-interesting-papers-on-computational-neuroscience/

In a new paper, published today in Current Biology, we analyse the genome of renowned composer Ludwig van Beethoven using a polygenic index related to musicality, as a way to illustrate the limits of genetic predictions at the individual level. Beethoven, one of the most celebrated musicians in history, scored unremarkably, ranking between the 9th & 11th percentile based on modern samples. We explain why this is no surprise & how it can provide a valuable teaching moment on the complex relationships between DNA & behaviour.
An interdisciplinary collaboration across two Max Planck Institutes (Psycholinguistics in Nijmegen & Empirical Aesthetics in Frankfurt), University of Amsterdam, Karolinska Institute, Vanderbilt University and others.
#MastodonScience #science #music #genetics #genomics
@mpi_nl @maxplanckgesellschaft

https://authors.elsevier.com/sd/article/S0960982224000253

Wondering about the current state of affairs of data science and data management in neuroscience collaborations? Wondering where your tax dollars go? Well, wonder no more! Edgar Walker, Guoqiang Yu and myself collected some data! And opinions :-) https://www.biorxiv.org/content/10.1101/2024.03.20.585936v1

Representing data through music makes it possible to spot patterns that link behavior, neural activity and hemodynamic activity in an awake mouse. Neural activity is represented as piano notes, whereas hemodynamic activity is encoded as violin chords.

By Calli McMurray

https://www.thetransmitter.org/methods/getting-musical-to-spot-patterns-in-whole-brain-imaging-data-qa-with-elizabeth-hillman/?utm_source=mastodon&utm_medium=org-social&utm_campaign=2024221-getting-musical

Suppose an agent (such as an individual, organization, or an AI) needs to choose between two options A and B. One can try to influence this choice by offering incentives ("carrots") or disincentives ("sticks") to try to nudge that agent towards one's preferred choice. For instance, if one prefers that the agent choose A over B, one can offer praise if A is chosen, and criticism if B is chosen. Assuming the agent does not possess contrarian motivations and acts rationally, the probability of the agent selecting your preferred outcome is then monotone in the incentives in the natural fashion: the more one praises the selection of A, or the more one condemns the selection of B, the more likely the agent would select A over B.

However, this monotonicity can break down if there are three or more options: trying to influence an agent to select a desirable (to you) option A by criticizing option B may end up causing the person to select an even less desirable option C instead. For instance, suppose one wants to improve safety conditions for workers in some industry. One natural approach is to criticise any company in the industry that makes the news due to an workplace accident. This is disincentivizing option B ("Allow workplace accidents to make the news") in the hope of encouraging option A ("Implement policies to make the workplace safer"). However, if this criticism is driven solely by media coverage, it can perversely incentivize companies to pursue option C ("Conceal workplace accidents from making the news"), which many would consider an even worse outcome than option B. (1/2)

#Cellpose 3! Not all images are perfect. Restore your images with Cellpose3 to get better segmentations. @marius10p https://www.biorxiv.org/content/10.1101/2024.02.10.579780v1

(click to play gif)

@nsmarkov for example at the annual workshop I run, we the organisers don't choose which abstracts get to be talks and which posters, instead all the participants say which anonymous abstracts they'd like to hear as talks and we pick those that maximise the potential audience. Works well. Small step, but in the right direction.

How did birds evolve wings?

A partial wing is of no use for flying, but natural selection can't plan ahead and allow a species to gradually evolve toward wings that are actually useful for flying.

Evolutionary biologists suspect that wings originally evolved for some purpose other than flight, and then later were coopted for flying.

Researchers have proposed a wide range of possible original purposes for wings, ranging from thermoregulation to knocking down insects to running up hills.

I have never considered the design of #DB 's official #ICE and #IC network map convincing. This was my motivation to create my own 🇩🇪 long-distance rail map a few years ago. It focuses more on systematic connections and their frequencies for better understanding rather than just showing every line.

The 2024 map is finally online 🥳
https://larstransportmaps.com/germany-2024/

Boost appreciated.

Official Map: https://assets.static-bahn.de/dam/jcr:a2ed983a-36e1-4533-a4ee-8b85daf684c2/231127_Liniennetz%20ICE%20IC%202024.pdf

"Metastatic cancers (those that have spread beyond the organ where they originated) account for around 67–90% of cancer deaths2,3, and are almost always treated systemically, meaning with drugs that enter the bloodstream. To improve treatments for people with metastatic cancer, the community urgently needs to shift from using organ-based classifications of cancer to using molecular-based ones. This will require radical changes in how medical oncology is structured, conducted and taught."

https://www.nature.com/articles/d41586-024-00216-3?utm_medium=Social&utm_campaign=nature&utm_source=Twitter&error=cookies_not_supported&code=17a5c0c0-698b-485d-9227-07079517b9c4#Echobox=1706725921

Simultaneous, cortex-wide and cellular-resolution neuronal population dynamics reveal an *unbounded* scaling of dimensionality with neuron number
https://www.biorxiv.org/content/10.1101/2024.01.15.575721v1

#neuroscience #dimensionality

Back in the 90s, when I was in grad school, sensory decisions were analyzed with “signal detection theory”. Since then, many have found it more fruitful to use logistic classification: the observer weighs the factors, uses their sum to bias a coin, and flips the coin.

However, a student starting today would find the relevant information scattered around. To fix this, I wrote “Sensory choices as logistic classification": https://www.biorxiv.org/content/10.1101/2024.01.17.576029v1. If you have suggestions, please let me know!

Looking for paper suggestions!

I'm teaching a class in Neuroecology for first year PhD students and part of it is a journal club.

I'm looking for additional, pure behavior papers that study "natural" / ecological behaviors, if possible not in mice.
And also for rather short papers that perform any form of brain recordings during natural /ecological behaviors (plus points if not in mice).

Any suggestions?
#Neuroecology #behaviors #neuroscience #teaching

Crazy read: detecting positions of players in Counterstrike by *listening to their GPU over a microphone*

https://faculty.cc.gatech.edu/~genkin/papers/lendear.pdf

I guess we do #introduction posts over here? I work on the #neuroscience (am I doing those hashtags right!?) of learning and memory, specifically how we learn while we navigate space and context. To do this, I take in vivo recordings (currently calcium imaging but ephys has my heart) of freely moving rats! After that, I use computational and mathematical approaches to analyze their neural activity! I am currently a BRAIN Initiative K99/R00 postdoc at Northwestern working with John Disterhoft and Sara Solla. I was trained at MIT with Matt Wilson, where I got my PhD in biology, and my BS is from Carnegie Mellon. Welcome!

@manisha @alicia_izquierdo @dimokaramanlis
@jonny

Loving the discussion in this thread. For dendritic tiling, one candidate would be clustered protocadherins, a group of 60 cell-surface genes in 3 clusters, some with weird split-promoters.

Their expression is randomized, and thought to form a combinatorial code that allows self-avoidance and (potentially) other-recognition.

In Retina:
https://www.jneurosci.org/content/38/11/2713.full

Structural interaction:
https://elifesciences.org/articles/72416

Review:
https://link.springer.com/article/10.1007/s12264-020-00578-4