Alex Clonan

Grad Student @UConnEngineer 🐾 - Computational Auditory Neuro - How do we 🧠 (and machines🤖) encode and perceive natural sounds? 🔊👂- he/him

[Alex Clonan @AlexCarmenClo]

Check out our new preprint! We investigate how spectrum & modulation in natural sounds can help/hinder human speech recognition👂💬

Further- we explore the bottom-up acoustic invariances driving this effect with a midbrain inspired, interpretable model🧠

https://t.co/SyWrKenCg0

[Nature Neuroscience @NatureNeuro]

Temporal integration throughout the human auditory cortex is predominantly locked to absolute time and does not vary with the duration of speech structures such as phonemes or words

@SamNormanH, @URNeuroscience, @UofR

https://t.co/jqneGu5bJS

[Dr. Nick Delaney @DelaneyNicholas]

From @UConn Today - @UConnEngineer Sophomores are doing amazing work with our community members. So proud of this special group!

https://t.co/rYnuHTJNJg

[TIME @TIME]

TIME's new cover: The dire wolf is back after over 10,000 years. Here's what that means for other extinct species https://t.co/LQtosdfiEf

[UConn Huskies @UConnHuskies]

NUMBER 12! 🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆

[Aran Nayebi @aran_nayebi]

1/ 🧵👇
What should count as a good model of intelligence?

AI is advancing rapidly, but how do we know if it captures intelligence in a scientifically meaningful way?

We propose the *NeuroAI Turing Test*—a benchmark that evaluates models based on both behavior and internal representations.

👉The key principle: given a metric, models should be *at least as good as brains are to each other*:

[Edmund Lalor @edmundlalor]

Are you Interested in:
- the role of oscillations in visual and auditory processing?
- blending computational and experimental neuroscience?
- research with clinical implications?

If yes, then maybe you'd like a postdoc with us.
DM/email me for more details.

RTs appreciated

[Josh McDermott @JoshHMcDermott]

New paper from our lab, by Mark Saddler, using machine learning to test the role of temporal coding in hearing. Here is a quick summary. (1/n)
https://t.co/QFXTdDXd7J
@mitbcs @mcgovernmit @MIT_CBMM @ScienceMIT

[CLaE @leafs_s]

Bayesian Models of Cognition

https://t.co/ZqG8seAk2v

[Josh McDermott @JoshHMcDermott]

New paper from our lab, by Jarrod Hicks. Shows that people use the statistical properties of background noise to hear concurrent “foreground” sounds. Read on for highlights (1).
https://t.co/W3e1nH3SX9
@ScienceMIT @mitbrainandcog @mcgovernmit @MIT_CBMM

[Liberty Hamilton @LibertySays]

Our paper on suppression of onset responses during speech production in auditory cortex (and lack thereof in the insula!) is now out in @SfNJournals ! Onset responses may provide an important cue for externally generated sounds! Thread below with info. https://t.co/PxbUgiExjQ

[NIH NIDCD @NIH_NIDCD]

When you can’t avoid loud sounds, wear hearing protectors, such as earplugs or protective earmuffs. Learn about the importance of protecting your hearing and the types of available hearing protectors: https://t.co/LCN9WYqTxZ #NPYHM #NIDCDinfo

[Josh McDermott @JoshHMcDermott]

We just wrote a primer on how the physics of sound constrains auditory perception:

https://t.co/NLgb4Q1ixj

Covers sound propagation and object interactions, and touches on their relevance to music and film.

I enjoyed working on this with @vin_agarwal and James Traer.

[Alex Clonan @AlexCarmenClo]

@MatthewBJane Thank you! Means a lot! I love the data visualization from your blog posts!

[Meenakshi Khosla @meenakshik93]

While I don’t agree with everything, the paper raises an important issue about linear predictivity's limitations. Another paper highlights an empirical flaw: its inability to distinguish trained from untrained models, despite clear functional differences: https://t.co/b4Id1wJyzR

[Jenelle Feather @jenellefeather]

This nicely summarizes some misconceptions in the recent position paper about measures of brain-model comparison.

One additional point: While I agree with the idea in the original paper that we shouldn't *just* focus on regression, many other measures have been proposed! 1/4

[Alexander Huth @alex_ander]

Nice quick read with an important point: even if a model predicts brain data well it doesn't mean the model uses the same mechanism the brain does. More expressive models generally do better than less expressive models regardless of mechanism.

[Edmund Lalor @edmundlalor]

A few days left to apply for a postdoc position(s) in our group.

If you are interested in brains, speech, language, oscillations, computational modeling, multisensory integration, predictive perception, please see the ad below and apply soon.

RTs appreciated.

[The Nobel Prize @NobelPrize]

BREAKING NEWS
The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”

[The Nobel Prize @NobelPrize]

2024 physics laureate Geoffrey Hinton used a network developed by his co-laureate John Hopfield as the foundation for a new network: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data.

The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning.

#NobelPrize