One basic aim of cognitive neuroscience is to answer questions like 1) what does a neuron or a group of neurons represent, and 2) how is cognitive computation implemented in neuronal hardware? A common critique is that the field has simply failed to shed light on either of these questions. Our experimental techniques are perhaps too crude: fMRI’s temporal resolution is way too slow, EEG and MEG’s spatial resolution is far too coarse, electrode recordings miss the forest for the trees. But underlying these criticisms is the assumption that there is some implementation-level description of neural activity that is interpretable at the level of cognition: if only we recorded from a sufficient number of neurons and actually knew what the … Read More
Some of the common complaints I hear about (learning) theoretical work run along the lines of “those bounds are meaningless in practice,” “that result doesn’t apply to any algorithm someone would actually use,” and “you lost me as soon as martingales/Banach spaces/measure-theoretic niceties/… got involved.” I don’t have a good answer for the latter concern, but a very nice paper by Sasha Rakhlin, Ohad Shamir, and Karthik Sridharan at NIPS this year goes some ways toward address the first two criticisms. Their paper, “Relax and Randomize: From Value to Algorithms,” (extended version here) is concerned with transforming non-constructive online regret bounds into useful algorithms.
My aim in this introductory post is to provide context for future contributions by sharing my thoughts on the role of computer scientists in the study of brain, particularly in the field of computational neuroscience. For a field with “computation” in the name, it seems that computer scientists are underrepresented. I believe there are significant open questions in neuroscience which are best addressed by those who study the theory of computation, learning, and algorithms, and the systems upon which they are premised. In my opinion “computational neuroscience” has two definitions: the first, from Marr, is the study of the computational capabilities of the brain, their algorithmic details, and their implementation in neural circuits; the second, stemming from machine learning, is … Read More
Much of what we do when we analyze data and invent algorithms is think about estimators for unknown quantities, even when we don’t directly phrase things this way. One type of estimator that we commonly encounter is the Monte Carlo estimator, which approximates expectations via the sample mean. That is, many problems in which we are interested involve a distribution on a space , where we wish to calculate the expectation of a function : This is very nice because it gives you an unbiased estimator of . That is, the expectation of this estimator is the desired quantity. However, one issue that comes up very often is that we want to find an unbiased estimator of a … Read More
While at NIPS, I came across the paper Deep Learning of Invariant Features via Simulated Fixations in Video by Will Zou, Shenghuo Zhu, Andrew Ng, and Kai Yu. It proposes a particularly appealing unsupervised method for using videos to learn image features. Their method appears to be somewhat inspired by the human visual system. For instance, people have access to video data, not static images. They also attempt to mimic the human tendency to fixate on particular objects. They track objects through successive frames in order to provide more coherent data to the learning algorithm. The authors use a stacked architecture, where each layer is trained by optimizing an embedding into a feature space. As usual, the optimization problem involves a reconstruction … Read More
Last week I attended the NIPS 2012 workshop on Connectomics: Opportunities and Challenges for Machine Learning, organized by Viren Jain and Moritz Helmstaedter. Connectomics is an emerging field that aims to map the neural wiring diagram of the brain. The current bottleneck to progress is analyzing the incredibly large (terabyte-petabyte range) data sets of 3d images obtained via electron microscopy. The analysis of the images entails tracing the neurons across images and eventually inferring synaptic connections based on physical proximity and other visual cues. One approach is manual tracing: at the workshop I learned that well over one million dollars has already been spent hiring manual tracers, resulting in data that is useful but many orders of magnitude short of … Read More
I’m excited to announce a new collaborative blog, written by members of the Harvard Intelligent Probabilistic Systems group. Broadly, our group studies machine learning, statistics, and computational neuroscience, but we’re interested in lots of things outside these areas as well. The idea is to use this as a venue to discuss interesting ideas and results — new and old — about probabilistic modeling, inference, artificial intelligence, theoretical neuroscience, or anything else research-related that strikes our fancy. There will be posts from folks at both Harvard and MIT, in computer science, mathematics, biophysics, and BCS departments, so expect a wide variety of interests. — Ryan Adams