Sander Keemink

Behavioral-state modulation of inhibition is context-dependent and cell type specific in mouse visual cortex

Cortical responses to sensory stimuli are modulated by behavioral state. In the primary visual cortex (V1), visual responses of pyramidal neurons increase during locomotion. This response gain was suggested to be mediated through inhibitory neurons, resulting in the disinhibition of pyramidal neurons. Using in vivo two-photon calcium imaging in layers 2/3 and 4 in mouse V1, we reveal that locomotion increases the activity of vasoactive intestinal peptide (VIP), somatostatin (SST) and parvalbumin (PV)-positive interneurons during visual stimulation, challenging the disinhibition model. In darkness, while most VIP and PV neurons remained locomotion responsive, SST and excitatory neurons were largely non-responsive. Context-dependent locomotion responses were found in each cell type, with the highest proportion among SST neurons. These findings establish that modulation of neuronal activity by locomotion is context-dependent and contest the generality of a disinhibitory circuit for gain control of sensory responses by behavioral state. DOI: http://dx.doi.org/10.7554/eLife.14985.001

FISSA: A neuropil decontamination toolbox for calcium imaging signals

In vivo calcium imaging has become a method of choice to image neuronal population activity throughout the nervous system. These experiments generate large sequences of images. Their analysis is computationally intensive and typically involves motion correction, image segmentation into regions of interest (ROIs), and extraction of fluorescence traces from each ROI. Out of focus fluorescence from surrounding neuropil and other cells can strongly contaminate the signal assigned to a given ROI. In this study, we introduce the FISSA toolbox (Fast Image Signal Separation Analysis) for neuropil decontamination. Given pre-defined ROIs, the FISSA toolbox automatically extracts the surrounding local neuropil and performs blind-source separation with non-negative matrix factorization. Using both simulated and in vivo data, we show that this toolbox performs similarly or better than existing published methods. FISSA requires only little RAM, and allows for fast processing of large datasets even on a standard laptop. The FISSA toolbox is available in Python, with an option for MATLAB format outputs, and can easily be integrated into existing workflows. It is available from Github and the standard Python repositories.

A unified account of tilt illusions, association fields, and contour detection based on elastica

As expressed in the Gestalt law of good continuation, human perception tends to associate stimuli that form smooth continuations. Contextual modulation in primary visual cortex, in the form of association fields, is believed to play an important role in this process. Yet a unified and principled account of the good continuation law on the neural level is lacking. In this study we introduce a population model of primary visual cortex. Its contextual interactions depend on the elastica curvature energy of the smoothest contour connecting oriented bars. As expected, this model leads to association fields consistent with data. However, in addition the model displays tilt-illusions for stimulus configurations with grating and single bars that closely match psychophysics. Furthermore, the model explains not only pop-out of contours amid a variety of backgrounds, but also pop-out of single targets amid a uniform background. We thus propose that elastica is a unifying principle of the visual cortical network.

Unconscious Biases in Neural Populations Coding Multiple Stimuli

Throughout the nervous system, information is commonly coded in activity distributed over populations of neurons. In idealized situations where a single, continuous stimulus is encoded in a homogeneous population code, the value of the encoded stimulus can be read out without bias. However, in many situations, multiple stimuli are simultaneously present; for example, multiple motion patterns might overlap. Here we find that when multiple stimuli that overlap in their neural representation are simultaneously encoded in the population, biases in the read-out emerge. Although the bias disappears in the absence of noise, the bias is remarkably persistent at low noise levels. The bias can be reduced by competitive encoding schemes or by employing complex decoders. To study the origin of the bias, we develop a novel general framework based on gaussian processes that allows an accurate calculation of the estimate distributions of maximum likelihood decoders, and reveals that the distribution of estimates is bimodal for overlapping stimuli. The results have implications for neural coding and behavioral experiments on, for instance, overlapping motion patterns.

Effects of V1 surround modulation tuning on visual saliency and the tilt illusion

Neurons in the primary visual cortex respond to oriented stimuli placed in the center of their receptive field, yet their response is modulated by stimuli outside the receptive field (the surround). Classically, this surround modulation is assumed to be strongest if the orientation of the surround stimulus aligns with the neurons preferred orientation, irrespective of the actual center stimulus. This neuron-dependent surround modulation has been used to explain a wide range of psychophysical phenomena, such as biased tilt perception and saliency of stimuli with contrasting orientation. However, several neurophysiological studies have shown that for most neurons surround modulation is instead center dependent: it is strongest if the surround orientation aligns with the center stimulus. As the impact of such center-dependent modulation on the population level is unknown, we examine this using computational models. We find that with neuron-dependent modulation the biases in orientation coding, commonly used to explain the tilt illusion, are larger than psychophysically reported, but disappear with center-dependent modulation. Therefore we suggest that a mixture of the two modulation types is necessary to quantitatively explain the psychophysically observed biases. Next, we find that under center-dependent modulation average population responses are more sensitive to orientation differences between stimuli, which in theory could improve saliency detection. However, this effect depends on the specific saliency model. Overall, our results thus show that center-dependent modulation reduces coding bias, while possibly increasing the sensitivity to salient features. NEW & NOTEWORTHY Neural responses in the primary visual cortex are modulated by stimuli surrounding the receptive field. Most earlier studies assume this modulation depends on the neurons tuning properties, but experiments have shown that instead it depends mostly on the stimulus characteristics. We show that this simple change leads to neural coding that is less biased and under some conditions more sensitive to salient features.

Biases in multivariate neural population codes

Throughout the nervous system information is typically coded in activity distributed over large population of neurons with broad tuning curves. In idealized situations where a single, continuous stimulus is encoded in a homogeneous population code, the value of an encoded stimulus can be read out without bias. Here we find that when multiple stimuli are simultaneously coded in the population, biases in the estimates of the stimuli and strong correlations between estimates can emerge. Although bias produced via this novel mechanism can be reduced by competitive coding and disappears in the complete absence of noise, the bias diminishes only slowly as a function of neural noise level. A Gaussian Process framework allows for accurate calculation of the bias and shows that a bimodal estimate distribution underlies the bias. The results have implications for neural coding and behavioral experiments.

Impact of orientation specific surround modulation and tuning curve shape on population coding and tilt illusion in V1

The tilt illusion is a well-studied visual phenomenon, whereby the perceived angle of a center stimulus is misjudged in the presence of a differently aligned surround stimulus (e.g. [1]). The dependence of V1 neuron activity on center-surround interactions has been studied extensively (e.g. [2]). These center-surround interactions can be used to explain the tilt illusion, as they result in tuning curve modulations. When population activity is decoded using these modulated tuning curves, the tilt illusion arises [2]. In this work, we examine two factors affecting the tilt illusion: First, we examine is the effect of the tuning curve width on the tilt illusion. Tuning curves widths vary widely in vivo [3]. Although changes in tuning curve width due to center-surround interactions have been shown to potentially contribute to the tilt illusion [2,4], how the tuning curve width itself affects the illusion is less well understood. Using a firing rate model, we show here that for narrower tuning curves the tilt illusion lessens, and that it disappears almost completely for narrow, but still realistic, tuning curves. Secondly, we consider the consequences of recent experimental findings on the tuning of surround modulation. Most models assume that V1 neurons experience most suppression when the surround stimulus is aligned with the neurons preferred orientation. However, a recent study showed that for the majority of V1 neurons, the suppression effect depends much more on the relation between center and surround orientation, being strongest when they are co-aligned, regardless of the preferred orientation [5]. We use a firing rate model based on [5] to take these new finding into account, and show that, counter-intuitively, the tilt illusion is not impacted, once we control for changes in the tuning curve widths.