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Hello world, it's time to do an ! I'm a neuroscientist (postdoc) currently based in Göttingen, Germany. I have broad interests in , including sensory coding, neural circuits, and sensorimotor transformations.

So far, I studied how the retina processes natural scenes using a combination of large-scale electrophysiology and computational modeling.

I'll be soon leaving the tidy entryway to the visual system to venture into understanding cortical processing during decision-making.

At #Fens2024 tomorrow (Wednesday 26/June) - Check out our poster comparing how representations change in #hippocampus, dorsolateral #striatum, and #prefrontal cortex in rats making decisions as environmental complexity changes.

U. Mugan et al. Poster number 211. In poster session 02: prefrontal decision-making

Some really cool results looking at interactions between decision-making systems!

How are events segmented and organized in time? And how might this impact our perception and memory of time?

Check out our work here on how neural trajectories in the lateral entorhinal cortex inherently drift over time, but abruptly shift at event boundaries to discretize a continuous experience.

biorxiv.org/content/10.1101/20

x.com/EdvardMoser/status/18029

#Events #Time #Memory #Dynamics #Experience #Circuits #EntorhinalCortex #Hippocampus #AnimalBehavior #Preprint

Meet our Neuroinformatics Unit! Driven by an open science ethos, the team of research software engineers work closely with SWC and @GatsbyUCL researchers to build tools that improve data organisation, refine analysis pipelines, and more: sainsburywellcome.org/web/blog

@BorisBarbour as I said I don't have any objection to people using hypothesis testing as one small part of the process, I have an objection to making it central to the process. As you say, it's about drawing a conclusion. My assertion would be that 99.9% of the time or more it would be inappropriate to draw a conclusion from any given experiment, but that this doesn't make those experiments bad, failed or not useful. Designing everything around an approach to analysis that is rarely the most appropriate is inefficient and distorting. And secondarily, and less contentiously I guess, applying this framework to modelling work is just flat out wrong.

How do neural circuits generate flexible, cognitive behaviours? The Duan and @jerlich labs are looking for 2️⃣ excellent postdoctoral research fellows to join the team. Check out the vacancies and apply by 25 May: sainsburywellcome.org/web/cont

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

gcamp6f.com/2024/03/26/three-r

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

authors.elsevier.com/sd/articl

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 :-) biorxiv.org/content/10.1101/20

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

thetransmitter.org/methods/get

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)

@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 🥳
larstransportmaps.com/germany-

Boost appreciated.

Official Map: assets.static-bahn.de/dam/jcr:

"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."

nature.com/articles/d41586-024

Simultaneous, cortex-wide and cellular-resolution neuronal population dynamics reveal an *unbounded* scaling of dimensionality with neuron number
biorxiv.org/content/10.1101/20

#neuroscience #dimensionality

New paper(s) alert. In two linked papers I argue that we have fundamentally misunderstood the ancestry and evolutionary history of retinal circuits for (colour) vision.
Part I @natureecoevo rdcu.be/dwCEj
Part II @plosbiology doi.org/10.1371/journal.pbio.3

@PessoaBrain
@cogneurophys
The limitation ive always seen in these systems, bafflingly, is the software: people will invest immense amounts of engineering labor in the microscope and transgenic models, and then strap on the crappiest single-purpose realtime model on the most brittle experimental tooling that can at best do a glorified if else statement on big blocks of activity, rather than some subtle manipulation eg. Coupled to a dynamical model of the circuit.

Drove me crazy writing autopilot, like yall why cant we have a modular toolkit that we can built realtime logic into rather than trying to build the whole thing from scratch every time, but I pushed that boulder up a hill for 7 years and need a break before i try again

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