Neuroscience
OHBM Annual Meeting 2017

OHBM Annual Meeting 2017

This year I presented a poster at the Organization for Human Brain Mapping (OHBM) annual meeting in Vancouver. I had a nice time and saw some interesting stuff, which I’d like to share with you all. To keep it brief, I’m going to focus on the highlights. Overall Themes Reproducibility. In addition to creating a...
How can we show that deep learning and the brain are related? Integration of Deep Learning and Neuroscience Final Post

How can we show that deep learning and the brain are related? Integration of Deep Learning and Neuroscience Final Post

As we’ve discussed in our previous posts, this paper on the integration of deep learning and neuroscience has been highly speculative. The authors have listed deep learning-inspired hypotheses about the brain and discussed how the brain may be consistent with those hypotheses. The concluding portion of the paper discusses potential experiments that could help prove...
Paper Review: An Integration of Deep Learning and Neuroscience, Part 3

Paper Review: An Integration of Deep Learning and Neuroscience, Part 3

In our previous post we talked about how the brain might optimize cost functions. Now we’ll explore how cost functions may be generated, represented, and change over time in the brain. Marblestone et al outline several ways that cost functions could be generated.  In particular, they talk about specialized circuitry for comparing the predicted output...
Paper Review: An Integration of Deep Learning and Neuroscience, Part 2

Paper Review: An Integration of Deep Learning and Neuroscience, Part 2

One of the key sticking points in discussions comparing machine learning and the brain is how the notion of “learning” differs between computational and biological systems. In section 2 of their paper, Marblestone et al. grapple with this issue in detail. For our introduction post on this paper, go here. Deep neural networks are trained...
Paper Review: An Integration of Deep Learning and Neuroscience, Part 1

Paper Review: An Integration of Deep Learning and Neuroscience, Part 1

Nicole: More and more I see that people are very concerned with the biological plausibility of neural networks. I think this comes from the fact that we as machine learners are finally achieving human-level performance on some tasks. It has renewed faith in the idea that the best way to “solve” intelligence is to copy...
Neurons 101

Neurons 101

Neurons are the building blocks of the brain. They take information they receive from upstream neurons and summarize it as a spike that is sent  to downstream neurons. (image courtesy of wikimedia commons) Let’s talk about the anatomy of neurons:   Nucleus: Like any cell in your body, each neuron has a nucleus, where its...
Neuroscience news: what's fake and what's real?

Neuroscience news: what’s fake and what’s real?

Reporters have gotten very, very good at writing catchy headlines.  With all of the clickbait floating around the internet, the news has become an arms race, and science news is no exception.  Since people like hearing about how their brains work, and everyone secretly (not so secretly?) likes reading about sex, drugs and rock and...
Paper review: The brain adapts to dishonesty

Paper review: The brain adapts to dishonesty

Does dishonesty escalate over time? Garrett et al seek to answer this question and substantiate something we all have observed anecdotally: that dishonesty can be a “slippery slope.” The idea is that if you are dishonest once, it is easier to be dishonest again, and this effect can accumulate over time. This paper generated some...
Paper Review: Fixing the stimulus­ as fixed ­effect fallacy in task fMRI

Paper Review: Fixing the stimulus­ as fixed ­effect fallacy in task fMRI

Fixing the stimulus­-as-­fixed-­effect fallacy in task fMRI Jacob Westfall, Thomas E. Nichols, and  Tal Yarkoni http://biorxiv.org/content/biorxiv/early/2016/09/25/077131.full.pdf In traditional fMRI experiments it’s typical to show subjects a limited set of stimuli (e.g. “tasty tomato”), but generalize the results to larger classes from which the stimuli were drawn (e.g. adjectives and nouns). According to Westfall, Nichols and...