In last week’s computational & theoretical neuroscience journal club I presented the following paper from Nicolas Brunel’s group:
Inferring learning rules from distributions of firing rates in cortical neurons.
Lim, McKee, Woloszyn, Amit, Freedman, Sheinberg, & Brunel.
Nature Neuroscience (2015).
The paper seeks to explain experience-dependent changes in IT cortical responses in terms of an underlying synaptic plasticity rule. Continue reading
In a recent lab meeting, I presented the following paper from Larry Abbott’s group:
From fixed points to chaos: Three models of delayed discrimination.
Barak, Sussillo, Romo, Tsodyks, & Abbott,
Progress. in Neurobiology 103:214-22 2013.
The paper seeks to connect the statistical properties of neurons in pre-frontal cortex (PFC) during short-term memory with those exhibited by several dynamical models for neural population responses. In a sense, it can be considered a follow-up to Christian Machens’ beautiful 2005 Science paper , which showed how a simple attractor model could support behavior in a two-interval discrimination task. The problem with the Machens/Brody/Romo account (which relied on mutual inhibition between two competing populations) is that it predicts extremely stereotyped response profiles, with all neurons in each population exhibiting the same profile. Continue reading
At long last, I’ve finished cleaning, commenting, and packaging up code for binary pursuit spike sorting, introduced in our 2013 paper in PLoS ONE. You can download the Matlab code here (or on github), and there’s a simple test script to illustrate how to use it on a simulated dataset.
The method relies on a generative model (of the raw electrode data) that explicitly accounts for the superposition of spike waveforms. This allows it to detect synchronous and overlapping spikes in multi-electrode recordings, which clustering-based methods (by design) fail to do.
If you’d like to know more (but don’t feel like reading the paper), I wrote a blog post describing the basic intuition (and the cross-correlation artifacts that inspired us to develop it in the first place) back when the paper came out (link).
On July 28th, I presented the following paper in lab meeting:
This paper proposes a new method for characterizing the multi-dimensional stimulus selectivity of sensory neurons. The main idea is that, instead of thinking of neurons as projecting high-dimensional stimuli into an arbitrary low-dimensional feature space (the view underlying characterization methods like STA, STC, iSTAC, GQM, MID, and all their rosy-cheeked cousins), it might be more useful / parsimonious to think of neurons as performing a projection onto convolutional subunits. That is, rather than characterizing stimulus selectivity in terms of a bank of arbitrary linear filters, it might be better to consider a subspace defined by translated copies of a single linear filter.
Yesterday marked the start of the 2014 summer course in COMPUTATIONAL NEUROSCIENCE: VISION at Cold Spring Harbor. The course was founded in 1985 by Tony Movshon and Ellen Hildreth, with the goal of inspiring new generations of students to address problems at the intersection of vision, computation, and the brain. The list of past attendees is impressive.
I’m proud to announce the publication of our “zombie” spike sorting paper (Pillow, Shlens, Chichilnisky & Simoncelli 2013), which addresses the problem of detecting overlapped spikes in multi-electrode recordings.
The basic problem we tried to address is that standard “clustering” based spike-sorting methods often miss near-synchronous spikes. As a result, you get cross-correlograms that look like this:
When I first saw these correlograms (back in 2005 or so), I thought: “Wow, amazing —retinal ganglion cells inhibit each other with 1-millisecond precision! Should we send this to Nature or Science?” My more sober experimental colleagues pointed out that that this was likely only a (lowly) spike sorting artifact. So we set out to address the problem (leading to the publication of this paper a mere 8 years later!)
This week, Memming and I are in Columbus, Ohio for a workshop on “Sensory and Coding”, organized by Brent Doiron, Adrienne Fairhall, David Kleinfeld, and John Rinzel.
Monday was “Big Picture Day”, and I gave a talk about Bayesian Efficient Coding, which represents our attempt to put Barlow’s Efficient Coding Hypothesis in a Bayesian framework, with an explicit loss function to specify what kinds of posteriors are “good”. One of my take-home bullet points was that “you can’t get around the problem of specifying a loss function”, and entropy is no less arbitrary than other choice. This has led to some stimulating lunchtime discussions with Elad Schneidman, Surya Ganguli, Stephanie Palmer, David Schwab, and Memming over whether entropy really is special (or not!).
It’s been a great workshop so far, with exciting talks from a panoply of heavy hitters, including Garrett Stanley, Steve Baccus, Fabrizio Gabbiani, Tanya Sharpee, Nathan Kutz, Adam Kohn, and Anitha Pasupathy. You can see the full lineup here: