A team of researchers have a blog post about using neural networks to decode hippocampal activity to accurately predict a mouse’s location using.

https://www.twosixlabs.com/translating-between-brain-and-world-decoding-biological-neural-nets-with-artificial-neural-nets/

Lurker, posting:

So I’ve written this medium article explaining the fundamentals of bias and variance of ML models. https://medium.com/@kocherlakota/bias-variance-e4502eb4ad5?sk=6f9ed14ed41ead4ef8e3cb4b2ec1e204

​

Looking for any feedback or discussion you guys might have on the contents.

​

​

PS. Not sure if this belongs on /r/learnmachinelearning, I guess there’s some non-trivial statistics that warrants a discussion on this sub?

Hi all,

I’m about to start a PhD in cognitive/computational neuroscience and I was having trouble finding some good background on this but I was wondering if anyone here has some good suggestions for reviews or landmark pieces of literature on the study of RNN’s for modeling neural dynamics especially in prefrontal cortex?

I’m mostly thinking along the lines of Earl Miller’s recent work in applying models using reservoir computing or the Shenoy labs use of a sequential variational autoencoder (LFADS) for modeling neural state space trajectories and the associated background. I have a BS and MS in Applied Math so technical reviews that unify and lend generality are preferred such as, my all-time favorite, A Unifying Review of Gaussian Linear Models by Roweis and Ghahramani (but for RNN’s). I don’t suppose there are any books about these yet.

Also, theoretical perspectives regarding the training and topology of RNN’s are also of much interest!

Thanks in advance!

Interested in dimensionality reduction? TSNE is so last century, these days it’s all about UMAP! Join Mihaela Curmei as she delivers a sublime presentation on UMAP!

https://www.youtube.com/watch?v=G9s3cE8TNZo

UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.

Not sure if anyone is actively following the political situation in Hong Kong, but my understanding is that flights have been cancelled at the airport. Many parts of the city has also been subject to tear gas and there have been reports of police attacks on bystanders on the street near the political protests.

Is the situation affecting the people currently at IJCAI? Many people are flying to Macau via HK.

Also EMNLP will take place in HK in November, not far away. With the tensions between mainland China and Hong Kong escalating, I wonder if organizers have plans to move the conference to another location.

https://www.emnlp-ijcnlp2019.org

I haven’t seen any discussion in this subreddit yet, though it was published only a few days ago.

Paper: https://www.nature.com/articles/s41598-019-48016-4

Abstract:

Recently, deep learning algorithms have outperformed human experts in various tasks across several domains; however, their characteristics are distant from current knowledge of neuroscience. The simulation results of biological learning algorithms presented herein outperform state-of-the-art optimal learning curves in supervised learning of feedforward networks. The biological learning algorithms comprise asynchronous input signals with decaying input summation, weights adaptation, and multiple outputs for an input signal. In particular, the generalization error for such biological perceptrons decreases rapidly with increasing number of examples, and it is independent of the size of the input. This is achieved using either synaptic learning, or solely through dendritic adaptation with a mechanism of swinging between reflecting boundaries, without learning steps. The proposed biological learning algorithms outperform the optimal scaling of the learning curve in a traditional perceptron. It also results in a considerable robustness to disparity between weights of two networks with very similar outputs in biological supervised learning scenarios. The simulation results indicate the potency of neurobiological mechanisms and open opportunities for developing a superior class of deep learning algorithms.