Expressions 1983

The 1983 edition of Expressions magazine is the result of the efforts of students from several DMACC programs. Entrants in both the annual Creative Writing Contest and the Campus Chronicle Photography Contest as well as student in the commercial art program contributed material to the magazine. Layout, design and typesetting was done by the summer Publications Production class.https://openspace.dmacc.edu/expressions/1005/thumbnail.jp

Adaptive Filtering Enhances Information Transmission in Visual Cortex

Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain's coding strategy depend on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we compare responses to natural inputs with those to noise inputs matched for luminance and contrast. We find that neural filters adaptively change with the input ensemble so as to increase the information carried by the neural response about the filtered stimulus. Adaptation affects the spatial frequency composition of the filter, enhancing sensitivity to under-represented frequencies in agreement with optimal encoding arguments. Adaptation occurs over 40 s to many minutes, longer than most previously reported forms of adaptation.Comment: 20 pages, 11 figures, includes supplementary informatio

What a Plant Sounds Like: The Statistics of Vegetation Echoes as Received by Echolocating Bats

A critical step on the way to understanding a sensory system is the analysis of the input it receives. In this work we examine the statistics of natural complex echoes, focusing on vegetation echoes. Vegetation echoes constitute a major part of the sensory world of more than 800 species of echolocating bats and play an important role in several of their daily tasks. Our statistical analysis is based on a large collection of plant echoes acquired by a biomimetic sonar system. We explore the relation between the physical world (the structure of the plant) and the characteristics of its echo. Finally, we complete the story by analyzing the effect of the sensory processing of both the echolocation and the auditory systems on the echoes and interpret them in the light of information maximization. The echoes of all different plant species we examined share a surprisingly robust pattern that was also reproduced by a simple Poisson model of the spatial reflector arrangement. The fine differences observed between the echoes of different plant species can be explained by the spatial characteristics of the plants. The bat's emitted signal enhances the most informative spatial frequency range where the species-specific information is large. The auditory system filtering affects the echoes in a similar way, thus enhancing the most informative spatial frequency range even more. These findings suggest how the bat's sensory system could have evolved to deal with complex natural echoes

Estimating Receptive Fields from Responses to Natural Stimuli with Asymmetric Intensity Distributions

The reasons for using natural stimuli to study sensory function are quickly mounting, as recent studies have revealed important differences in neural responses to natural and artificial stimuli. However, natural stimuli typically contain strong correlations and are spherically asymmetric (i.e. stimulus intensities are not symmetrically distributed around the mean), and these statistical complexities can bias receptive field (RF) estimates when standard techniques such as spike-triggered averaging or reverse correlation are used. While a number of approaches have been developed to explicitly correct the bias due to stimulus correlations, there is no complementary technique to correct the bias due to stimulus asymmetries. Here, we develop a method for RF estimation that corrects reverse correlation RF estimates for the spherical asymmetries present in natural stimuli. Using simulated neural responses, we demonstrate how stimulus asymmetries can bias reverse-correlation RF estimates (even for uncorrelated stimuli) and illustrate how this bias can be removed by explicit correction. We demonstrate the utility of the asymmetry correction method under experimental conditions by estimating RFs from the responses of retinal ganglion cells to natural stimuli and using these RFs to predict responses to novel stimuli

Spatial Structure of Complex Cell Receptive Fields Measured with Natural Images

SummaryNeuronal receptive fields (RFs) play crucial roles in visual processing. While the linear RFs of early neurons have been well studied, RFs of cortical complex cells are nonlinear and therefore difficult to characterize, especially in the context of natural stimuli. In this study, we used a nonlinear technique to compute the RFs of complex cells from their responses to natural images. We found that each RF is well described by a small number of subunits, which are oriented, localized, and bandpass. These subunits contribute to neuronal responses in a contrast-dependent, polarity-invariant manner, and they can largely predict the orientation and spatial frequency tuning of the cell. Although the RF structures measured with natural images were similar to those measured with random stimuli, natural images were more effective for driving complex cells, thus facilitating rapid identification of the subunits. The subunit RF model provides a useful basis for understanding cortical processing of natural stimuli

Measurement of Preferred Features and Feature Sensitivity for V1 Complex Cells

<div><p>(A) Upper panel: example natural images. White boxes (12 × 12 pixels) indicate area presented in experiments. Lower panel: schematic spike train, binned at stimulus frame rate (24 Hz, dotted lines). Arrow indicates temporal delay (1 frame) at which preferred features were estimated, which was determined in preliminary studies to be the optimal temporal delay (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#sg002" target="_blank">Figure S2</a>).</p> <p>(B) Estimation of preferred features (significant eigenvectors) using STC analysis (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#s4" target="_blank">Materials and Methods</a>). Left panel: preferred features of a neuron, with light and dark regions represented by red and blue; dashed ovals delineate the first feature to facilitate comparison with the images. Right panel: 30 largest eigenvalues of STC matrix. Dashed lines: control confidence intervals (mean ± 12 standard deviation of control eigenvalues). Filled circles: significant eigenvalues corresponding to eigenvectors shown on left.</p> <p>(C) Upper panel: natural images. Dashed ovals correspond to those in (B). Middle panel: contrast of the first preferred feature (F.C. denotes feature contrast; see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#s4" target="_blank">Materials and Methods</a>). Lower panel: responses of the neuron (in spikes/s) to natural images. Black dots: feature contrasts (middle) and neuronal responses (lower) for the example images.</p> <p>(D) Contrast-response function. Error bar: ± standard error of the mean.</p></div

Detectability of Features from Neuronal Responses to Natural Images and Random Stimuli

<div><p>(A) Probability distribution of feature contrast in a natural ensemble (or, equivalently, its matched random ensemble). For simplicity, only the positive side (feature contrast >0) is shown. Gray shading: feature contrasts near zero (<<i>T</i>₀, here <i>T</i>₀<i>=</i> 0.007, “feature absent”); black shading: high feature contrasts (><i>T</i>₁, here <i>T</i>₁ = 0.04, “feature present”).</p> <p>(B) Conditional probability distributions of responses evoked by natural images (upper) and random stimuli (lower). Solid lines: response distributions when the feature was present in stimulus (black shading in [A]); dashed lines: distributions when the feature was absent (gray shading in [A]).</p> <p>(C) Feature detectability in natural images versus that in matched random stimuli, for the same population of cells shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#pbio-0030342-g003" target="_blank">Figure 3</a>B. Detectability was measured as the percentage of trials in which stimuli were correctly classified as “feature present” or “feature absent” (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#s4" target="_blank">Materials and Methods</a>).</p></div

Feature Sensitivity of Complex Cells in Response to Natural Images and Random Stimuli

<div><p>(A) Contrast-response functions for both preferred features (insets above) of a complex cell. Curves: fits of data with quadratic functions.</p> <p>(B) Gain of contrast-response function (in spikes/s per unit feature contrast) for natural ensemble versus that for contrast-matched random ensemble. For this population of cells, the gain was significantly higher for the natural than for the random ensemble (<i>n</i> = 24, from 14 cells; <i>p</i> < 10⁻⁴, Wilcoxon signed rank test).</p></div

Effects of Power and Phase Spectra of Stimuli on Cortical Feature Sensitivity

<div><p>(A) Four classes of stimulus ensembles with distinct combinations of power (<i>P</i>) and phase (ϕ) characteristics; +: natural; −: random. Example stimuli from each class are shown. The <i>P⁻</i>/ϕ<i>⁻</i> and <i>P⁻</i>/ϕ⁺ stimuli are matched for both the global contrast and the feature contrasts for a particular complex cell.</p> <p>(B) Summary of cortical feature sensitivity (contrast-response gain; see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#pbio-0030342-g003" target="_blank">Figure 3</a>B) for the stimulus classes in (A). In each experiment, a random (<i>P⁻</i>/ϕ<i>⁻</i>) stimulus ensemble was generated to match <i>P</i>⁺/ϕ⁺, <i>P</i>⁺/ϕ<i>⁻</i>, or <i>P⁻</i>/ϕ⁺ in global and feature contrasts (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#pbio-0030342-g002" target="_blank">Figure 2</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#s4" target="_blank">Materials and Methods</a>), and the measured contrast-response gain was plotted against the gain for <i>P⁻</i>/ϕ<i>⁻</i> (as in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#pbio-0030342-g003" target="_blank">Figure 3</a>B). Bar represents slope of linear regression (through origin); >1 indicates higher contrast-response gain relative to <i>P⁻</i>/ϕ<i>⁻</i>. Error bar: ± standard deviation. <i>P</i>⁺/ϕ⁺ bars for simple (S) and complex (C) cells were computed from data in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#pbio-0030342-g003" target="_blank">Figures 3</a>B and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030342#pbio-0030342-g004" target="_blank">4</a>B, respectively, and <i>P</i>⁺/ϕ<i>⁻</i> (<i>n</i> = 10, from six cells) and <i>P⁻</i>/ϕ⁺ (<i>n</i> = 11, from six cells) were from largely nonoverlapping populations of complex cells (one cell was used in two separate experiments).</p></div

Matching of Feature Contrasts in Natural and Random Ensembles

<div><p>(A) Example images in the natural (upper row) and the random (lower row) ensembles, which were matched frame by frame for both global and feature contrasts.</p> <p>(B) Contrasts of a preferred feature of a complex cell (inset at center) in each frame of the natural (squares) and random (circles) ensembles in (A). F.C. denotes feature contrast.</p> <p>(C) Distributions of feature contrasts in the natural (left) and random (middle) ensembles, and the distribution of the difference in feature contrast between the two ensembles (right).</p></div