Jeremy Freeman: May 24th

Jeremy Freeman talked about his recent work on subunit identification.

Eizaburo Doi : March 15

I will discuss the details of the following paper:

B. G. Borghuis, C. P. Ratliff, R. G. Smith, P. Sterling, and V. Balasubramanian. Design of a neuronal array. Journal of Neuroscience, 28:3178–3189, 2008.

I'd also mention a couple of related papers, including those cited in:

T. E. Holy. ”Yes! We’re all individuals!”: redundancy in neuronal circuits. Nature Neuroscience, 13:1306–1307, 2010.

Basically I plan to lead a discussion of efficient coding, population coding, redundancies in neural populations, and retinal coding.  This is partly because we're finishing a journal draft on this topic.  It would be great if you could bring any other papers that you'd like to discuss.

Alex Ramirez : Feb. 8

Alex will be talking about: "Spike patterns in retinal ganglion cells required for decoding - a progress report."

Eizaburo Doi : Sept. 22

Title: Testing efficient coding for a complete and inhomogeneous neural population

The theory of efficient coding under the linear Gaussian model, originally formulated by Linsker (1989), Atick & Redlich (1990), and van Hateren (1992), is quite well-known.  However, its direct test with physiological data (a complete population of receptive fields) has been hampered in the past twenty years for two reasons:  a) There is no physiological data available.  b) The earlier models are too simplistic to compare with physiological data.

We resolve these two issues, and furthermore, we develop two novel methods to assess how the structures of the retinal transform match those of the theoretically derived, optimal transform.  The main conclusion of this study is that the retinal transform is at least 80% optimal, when evaluated with the linear-Gaussian model.

We also clarify the characteristics of the retinal transform that are and are not explained by the proposed model, and discuss the future directions and preliminary results along these lines.

This is a joint work with Jeff Gauthier, Greg Field, Alexander Sher, John Shlens, Martin Greschner, Tim Machado, Keith Mathieson, Deborah Gunning, Alan Litke, Liam Paninski, EJ Chichilnisky, and Eero Simoncelli.

Kolia Sadeghi : July 28

I will present work done with Liam, Jeff Gauthier and others in EJ Chichilnisky's lab on locating retinal cones from multiple ganglion cell recordings.  We write down a single hierarchical model where ganglion cell responses are modeled as independent GLMs with space-time-color separable filters and no spike history.  Assuming the stimulus was gaussian ensures that the ganglion cell Spike Triggered Averages are sufficient statistics.  The spatial component is then assumed to be a weighted sum of non-overlapping and appropriately placed archetypical cone receptive fields.  With a benign approximation, we can integrate out the weights and focus on doing MCMC in the space of cone locations and colors only.  As it turns out, this likelihood landscape has many nasty local maxima; we use parallel tempering and a few techniques specific to this problem to ensure ergodicity of the markov chain.

Doing a google scholar search on parallel tempering, also known as replica exchange, or just exchange Monte Carlo, will bring up many papers on this simple technique. Here is a review:
Parallel tempering: Theory, applications, and new perspectives