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Publications

New paper in eLife: “Spike-timing-dependent ensemble encoding…”

Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons

Michele N InsanallyIoana Carcea Rachel E Field Chris C Rodgers Brian DePasqualeKanaka Rajan Michael R DeWeese Badr F Albanna Robert C Froemke 

Abstract

Neurons recorded in behaving animals often do not discernibly respond to sensory input and are not overtly task-modulated. These non-classically responsive neurons are difficult to interpret and are typically neglected from analysis, confounding attempts to connect neural activity to perception and behavior. Here we describe a trial-by-trial, spike-timing-based algorithm to reveal the coding capacities of these neurons in auditory and frontal cortex of behaving rats. Classically responsive and non-classically responsive cells contained significant information about sensory stimuli and behavioral decisions. Stimulus category was more accurately represented in frontal cortex than auditory cortex, via ensembles of non-classically responsive cells coordinating the behavioral meaning of spike timings on correct but not error trials. This unbiased approach allows the contribution of all recorded neurons – particularly those without obvious task-related, trial-averaged firing rate modulation – to be assessed for behavioral relevance on single trials.

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Publications

New Paper in Entropy: “Minimum and Maximum Entropy…”

The first paper from the Albanna Lab is out in Entropy!

Link to Paper Online

Link to PDF

Minimum and Maximum Entropy Distributions for Binary Systems with Known Means and Pairwise Correlations

Badr F. Albanna, Christopher Hillar, Jascha Sohl-Dickstein and Michael R. DeWeese

Abstract

Maximum entropy models are increasingly being used to describe the collective activity of neural populations with measured mean neural activities and pairwise correlations, but the full space of probability distributions consistent with these constraints has not been explored. We provide upper and lower bounds on the entropy for the minimum entropy distribution over arbitrarily large collections of binary units with any fixed set of mean values and pairwise correlations. We also construct specific low-entropy distributions for several relevant cases. Surprisingly, the minimum entropy solution has entropy scaling logarithmically with system size for any set of first- and second-order statistics consistent with arbitrarily large systems. We further demonstrate that some sets of these low-order statistics can only be realized by small systems. Our results show how only small amounts of randomness are needed to mimic low-order statistical properties of highly entropic distributions, and we discuss some applications for engineered and biological information transmission systems.

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Members

Looking for Grad students

If you are a graduate student in neuroscience, physics, or computer science the Albanna Lab is interested in talking to you!  Contact Badr at balbanna@fordham.edu if you are interested in getting involved.

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News

Hello world!

The Albanna Lab website is live! I plan on using this site to post news, papers, and code from the Albanna Lab.

Stay tuned!