20110131

Collins Assisi & al. Using the Structure of Inhibitory Networks to Unravel Mechanisms of Spatiotemporal Patterning

Collins Assisi, Mark Stopfer, Maxim Bazhenov (2011) Using the Structure of Inhibitory Networks to Unravel Mechanisms of Spatiotemporal Patterning, DOI 10.1016/j.neuron.2010.12.019 [via]

Initially I was quite excited by this paper. I was a little disappointed that only a qualitative description of the results was provided. The figures were remarkably clear.

The way they constructed graphs with a given k-coloring was just to use bipartite, tripartite ... k-partite graphs to explore 1,2,...,k colorings. This didn't seem all that biologically plausible to me, but they do some controls that suggests that the results will generalize.

Real neural networks probably have connectivity such that the actual number of colors required to color the graph is huge, and that in practice there aren't really as many oscillatory subpopulations as there are colors. They address this with their "multiple coloring" explanation.

They note that as the number of colors increased the orderly activity decreased. It sounds like the timescale of adaptation determines this : you need the k colorings to be able to form a cycle that fits within the natural wavelength of slow inhibitory processes.

I wonder if the sets of neurons with identical color in this paper are related to the potential wells seen in Tkačik & al. In Tkačik & al the wells are stable minima for spontaneous activity. In Assisi & al., if you turn of adaptation single-color clusters are also the stable minima.

I would be interested in seeing a more theoretical analysis using LIF, theta, or rate-based neurons.

Breakout Elements


Hello. Now that I've got my MakerBot in practically working order, I'm thinking of posting one thing every week. Maybe even more frequently than that. Since I just started, I have a backlog of things I've designed.

Today's thing is "Breakout Elements". I designed this script to make snap-together brackets for Sparkfun breakout-boards. This thing also sets a new standard in terms of snap dimensions and distances.



making 3D printing at home more sustainable

20110114

Fake Dynamic DNS, SSH, BASH, and Dropbox

I recently wanted to set up remote access to my home computer, which doesn't have a stable IP address. Rather than do the sane thing and set up dynamic DNS, I decided to experiment with an ad-hoc solution involving drop box.

In brief : the home computer writes its current IP address to a drop-box folder, and scripts on remote computers reference this information to log in remotely.

Hatsopoulos &al — Encoding of Movement Fragments in the Motor Cortex (2007)

Nicholas G. Hatsopoulos, Qingqing Xu, and Yali Amit, Encoding of Movement Fragments in the Motor Cortex, The Journal of Neuroscience, May 9, 2007 - 27(19)

This paper explores the spatio-temporal response fields of M1 neurons. The authors were able to reconstruct spiking activity based on movement trajectory information within -100 to +300 ms. They use the average area under the receiver operating characteristic (ROC) curve for predicted spikes to quantify the predictive power of their model.

20110113

Vangeneugden & al. — Distinct Mechanisms for Coding of Visual Actions in Macaque Temporal Cortex (2011)

Joris Vangeneugden, Patrick A. De Maziere, Marc M. Van Hulle, Tobias Jaeggli, Luc Van Gool, and Rufin Vogels — Distinct Mechanisms for Coding of Visual Actions in Macaque Temporal Cortex, The Journal of Neuroscience, January 12, 2011 - 31(2)

The authors explore the coding of action perception, specifically walking, in extrastriate cortex. They used stick-silhouette figures, and used measured neural responses as inputs to a linear classifier in attempt to see how well the neurons encoded the stimuli.

Nelissen & al. — Charting the Lower Superior Temporal Region, a New Motion-Sensitive Region in Monkey Superior Temporal Sulcus (2006)

Koen Nelissen, Wim Vanduffel, and Guy A. Orban — Charting the Lower Superior Temporal Region, a New Motion-Sensitive Region in Monkey Superior Temporal Sulcus, The Journal of Neuroscience 26(22)

This paper explores motion sensitive areas in the superior temporal sulcus (STS) using fMRI. Their approach is to present a diversity of stimuli with different properties, then look at the pairwise differences in fMRI activation between each type of stimulus.

20110112

Learning Transformational Invariants from Natural Movies

Cadieu & Olshausen, 2009, Learning Transformational Invariants from Natural Movies

My favorite thing about this paper is its cool spatiotemporal eigenfunction video. Although their mathematical methods don't relate directly to biology, their model provides a new way of thinking about separation of form and motion information in early visual processing.

Their model has two layers, the first of which resembles basis-function learning explored in previous papers, and the second of which learns a sparse representation of motion based on modulations of spatial basis functions. They aim to extract transformation invariants : the features common to moving objects that depend entirely on the movement, and very little on the form of the object.

20110111

Pietro Berkes &al. — Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment (2011)

Pietro Berkes, Gergő Orbán, Máté Lengyel, József Fiser (2011) Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment, Science Vol. 331 no. 6013 pp. 83-87, DOI: 10.1126/science.119587

One theory for early sensory cortex is that the brain learns the statistics of the environment, and predicts incomplete information about the environment in a Bayesian optimal manner.

20110110

M. Szabo, R. Almeida, G. Deco, M. Stetter — A neuronal model for the shaping of feature selectivity in IT by visual categorization (2005)

M. Szabo, R. Almeida, G. Deco, M. Stetter (2005) A neuronal model for the shaping of feature selectivity in IT by visual categorization, Neurocomputing 65–66 (2005) 195–201

The part of the brain that handles complex forms ( inferior temporal cortex : IT ) will change how it responds to stimuli as you learn to categorize objects based on their features. IT learns to ignore features useless for categorization while becoming more sensitive ( selective ) to features that are useful.

The authors propose that this effect might be explained by interaction between IT and another part of the brain, speculatively pref-frontal cortex (PFC), a region implicated in attention and judgement. A competing hypothesis would be that area IT changes how it interprets visual input, rather than relying on such top-down judgements. It is possible that both mechanisms are at play, but on different timescales. However, this paper explains experimental data using only the attention model.
I think I will start cross-posting to here my contributions to http://pqdb.org/posts/, at least those that relate to my honest research efforts ( as opposed to baseless speculation ).

20110104

Subject 3, Trial 4, "Walk on uneven terrain"

CMU motion capture dataset
Subject 3, Trial 4, "Walk on uneven terrain"
100 overlaid walkers
scale : Uniform(0.4,1.0), offset : Gaussian(μ=0,σ = [1 2 3 4 6 8])