I'm currently developing a new course "Neuroscience for machine learners" that I hope to be able to make publicly available, and I'd love to hear what you think should be in it.

It's aimed at people with a machine learning background to learn a bit about neuroscience. My thinking is that neuroscience and ML have had fruitful links in the past, and may again in the future (although right now they're drifting apart). This course is designed to give students the background they'd need to be able to discover, understand and make use of new opportunities arising from neuroscience (if they do). I'm not trying to tell them only about the bits of neuroscience that we already think are applicable to ML, but to give them enough background to read and understand enough neuroscience to allow them to make new discoveries about what might be applicable to ML. The constraint is that it can't just be an intro to neuro course I think, because I'm not sure how compelling that would be to students with an ML focus. The course is 10 weeks and will have quite a practical focus, with most of the attention on weekly coding based exploratory group work rather than lectures. (Similar to @neuromatch Academy.)

I have thoughts about what should be on this course, but I'd love to know what you all think would be most relevant.

#neuroscience #compneuro #machinelearning #ai

Now out in @CurrentBiology -- our paper on cortical pattern amplification with learning, w/ @KanakaRajanPhD 🧠📄

Below in thread: Huge perceptual improvement. No vis resp changes. Simple recurrent model. Relationship to recent S. Peron lab paper. +more.

What does it mean? Biological recurrent networks in vivo can change to accommodate totally new patterns of input. We provide those artificially w/ optogenetics.

1/ #neuroscience #ai #postprint

Nature Human Behaviour is strengthening its reporting requirements, banning results showing only p-values. Great in general, but I have a few quibbles...

https://www.nature.com/articles/s41562-023-01586-w

They want to see:
1) statistic (degrees of freedom) = value;
2) P = value;
3) effect size statistic = value; and
4) per cent confidence intervals = values.
Sure, all good but:

A) No mention of the importants of units; "effect size" suggest units don't matter and r or Cohen's d will suffice. I want to see an 'effect magnitude' with meaningful units.

B) Why %CI's? If the effect units are seconds, mm, etc, I'd want CIs in those units, right?

Join our team at a top-ranked academic center and make a real difference, translating #MachineLearning and #Neuroscience to patients.

We're seeking a highly motivated faculty member to apply #LLM/#GPT3 to #BCI.

For more info: https://bit.ly/3GCtGyS
#hiring

I’m very proud to share: A smartphone intervention that enhances real-world memory and promotes differentiation of hippocampal activity in older adults.

Over the last 8 years, we developed @HippoCamera as an easy-to-use smartphone app that embodies principles from memory science to record and replay brief but powerful memory cues of everyday events.

Work led by @_chris__martin_, w/ @honey @chrishoney @bryan_hong_ @rachelnewsome @melellen_m

https://www.pnas.org/doi/10.1073/pnas.2214285119

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