Retinal metric: a stimulus distance measure derived from population neural responses

The ability of the organism to distinguish between various stimuli is limited by the structure and noise in the population code of its sensory neurons. Here we infer a distance measure on the stimulus space directly from the recorded activity of 100 neurons in the salamander retina. In contrast to previously used measures of stimulus similarity, this "neural metric" tells us how distinguishable a pair of stimulus clips is to the retina, given the noise in the neural population response. We show that the retinal distance strongly deviates from Euclidean, or any static metric, yet has a simple structure: we identify the stimulus features that the neural population is jointly sensitive to, and show the SVM-like kernel function relating the stimulus and neural response spaces. We show that the non-Euclidean nature of the retinal distance has important consequences for neural decoding.Comment: 5 pages, 4 figures, to appear in Phys Rev Let

Stimulus-dependent maximum entropy models of neural population codes

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. To be able to infer a model for this distribution from large-scale neural recordings, we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. The model is able to capture the single-cell response properties as well as the correlations in neural spiking due to shared stimulus and due to effective neuron-to-neuron connections. Here we show that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. As a result, the SDME model gives a more accurate account of single cell responses and in particular outperforms uncoupled models in reproducing the distributions of codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like surprise and information transmission in a neural population.Comment: 11 pages, 7 figure

Multicellular Features of Phytoplankton

Microscopic marine phytoplankton drift freely in the ocean, harvesting sunlight through photosynthesis. These unicellular microorganisms account for half of the primary productivity on Earth and play pivotal roles in the biogeochemistry of our planet (Field et al., 1998). The major groups of microalgae that comprise the phytoplankton community are coccolithophores, diatoms and dinoflagellates. In present oceans, phytoplankton individuals and populations are forced to rapidly adjust, as key chemical and physical parameters defining marine habitats are changing globally. Here we propose that microalgal populations often display the characteristics of a multicellular-like community rather than a random collection of individuals. Evolution of multicellularity entails a continuum of events starting from single cells that go through aggregation or clonal divisions (Brunet and King, 2017). Phytoplankton may be an intermediate state between single cells and aggregates of physically attached cells that communicate and co-operate; perhaps an evolutionary snapshot toward multicellularity. In this opinion article, we journey through several studies conducted in two key phytoplankton groups, coccolithophores and diatoms, to demonstrate how observations in these studies could be interpreted in a multicellular context

Data_Sheet_1_Multicellular Features of Phytoplankton.docx

<p>Microscopic marine phytoplankton drift freely in the ocean, harvesting sunlight through photosynthesis. These unicellular microorganisms account for half of the primary productivity on Earth and play pivotal roles in the biogeochemistry of our planet (Field et al., 1998). The major groups of microalgae that comprise the phytoplankton community are coccolithophores, diatoms and dinoflagellates. In present oceans, phytoplankton individuals and populations are forced to rapidly adjust, as key chemical and physical parameters defining marine habitats are changing globally. Here we propose that microalgal populations often display the characteristics of a multicellular-like community rather than a random collection of individuals. Evolution of multicellularity entails a continuum of events starting from single cells that go through aggregation or clonal divisions (Brunet and King, 2017). Phytoplankton may be an intermediate state between single cells and aggregates of physically attached cells that communicate and co-operate; perhaps an evolutionary snapshot toward multicellularity. In this opinion article, we journey through several studies conducted in two key phytoplankton groups, coccolithophores and diatoms, to demonstrate how observations in these studies could be interpreted in a multicellular context.</p

Supplemental data for the publication: "The Role of Vitamin D in Emiliania huxleyi: A Microalgal Perspective on UV-B Exposure"

The following table contains raw transcriptomic data of Emiliania huxleyi CCMP3266 grown under continuous UV radiation or control conditions for 7, 10, and 13 days, obtained using the MARS-seq protocol (Keren-Shaul H. et al. 2013) Column A: E. huxleyi CCMP3266 gene locus IDs Column B: E. huxleyi CCMP3266 representative gene transcripts Columns C: Best matching transcripts in E. huxleyi CCMP1516 Columns D - U: Raw read counts Columns V - AM: DESew2 normalized read counts Columns AN - BE: rlog transformed read counts. Columns BF - FC: Each block of 6 columns represents the results of the differential gene expression analysis between two treatments (time point or UV/nonUV). Columns FD - FJ: Functional annotation

Morphological Heterogeneity and Attachment of <i>Phaeobacter inhibens</i>

<div><p>The Roseobacter clade is a key group of bacteria in the ocean exhibiting diverse metabolic repertoires and a wide range of symbiotic life-styles. Many Roseobacters possess remarkable capabilities of attachment to both biotic and abiotic surfaces. When attached to each other, these bacteria form multi-cellular structures called rosettes. <i>Phaeobacter inhibens</i>, a well-studied Roseobacter, exhibits various cell sizes and morphologies that are either associated with rosettes or occur as single cells. Here we describe the distribution of <i>P</i>. <i>inhibens</i> morphologies and rosettes within a population. We detect an N-acetylglucosamine-containing polysaccharide on the poles of some cells and at the center of all rosettes. We demonstrate that rosettes are formed by the attachment of individual cells at the polysaccharide-containing pole rather than by cell division. Finally, we show that <i>P</i>. <i>inhibens</i> attachment to abiotic surfaces is hindered by the presence of DNA from itself, but not from other bacteria. Taken together, our findings demonstrate that cell adhesiveness is likely to play a significant role in the life cycle of <i>P</i>. <i>inhibens</i> as well as other Roseobacters.</p></div

Kinetics of rosette formation.

<p>(A) Rosettes form by cell encounters. Two independent aliquots from the same synchronized culture were stained with either Alexa 488-conjugated WGA (green) or Alexa 594-conjugated WGA (red) and were mixed for 30 minutes at room temperature in buffer prior to imaging. Rosettes with dual-labeled centers are shown. Images are overlay of phase contrast (gray) with green and red fluorescence channels. (B) Rosette complexity increases over time. The number of cells per rosette was quantified over time. Error bars indicate standard deviation of two biological replicates; n > 300 cells.</p

Polar polysaccharide at the center of rosettes.

<p>(A) Fluorescent overlay image of membrane stained cells (red) and Alexa 488-conjugated WGA lectin (green). Scale bar corresponds to 1 μm. (B) Quantification of lectin fluorescence intensity per cell in arbitrary units [AU]. Shown is a box plot generated with the R software [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141300#pone.0141300.ref028" target="_blank">28</a>]. (C) Percent of population in rosettes at early growth stages (2, 4, and 6 hours) or in a mid-exponential (ME) culture (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141300#sec004" target="_blank">Methods</a>). Cultures for this time course were obtained as described in Methods. Error bars indicate standard deviation of two biological replicates.</p

Self-DNA prevents attachment to an abiotic surface.

<p>Crystal violet absorbance of attached cells was quantified before and after addition of genomic DNA originating from either <i>P</i>. <i>inhibens</i> or <i>E</i>. <i>coli</i>. “Before treatment” measurement was conducted after 2 hours of incubation in an attachment assay (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141300#sec004" target="_blank">Methods</a>) and the different treatments were then applied. Measurements after treatments were conducted one hour following the addition of water or DNA. Error bars indicate standard deviation of at least two biological replicates. Note: crystal violet absorbance values before treatment and after treatment should be compared to time points 2 and 3 hours, respectively, in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141300#pone.0141300.g005" target="_blank">Fig 5A</a>.</p

Cell type distribution does not vary with culture age.

<p>Seven different cell types were characterized by size, labeling with Alexa 488-conjugated WGA (green) and presence of a septum using membrane stain FM4-64 (red). Each cell type was quantified at 2, 4 and 6 hours of a culture in early growth stages; n > 300 cells. Scale bar corresponds to 1 μm.</p