Vision: stimulating your attention.
Attentional selection biases the processing of higher visual areas to particular parts of a scene. Recent experiments show how stimulation of neurons in the frontal eye fields can mimic this process.
Animals↗
Biomedical subjects
Christoph Kayser
Publications and source records attributed to Christoph Kayser.
Feature selectivity in area 21a of the cat.
We compared the feature tuning of neuronal activity in area 21a with the tuning in areas 17/18. Local field potentials and multi-unit activity recorded in alert animals showed similar selectivity to orientation in both areas, higher selectivity to spatial frequencies in areas 17/18 and higher selectivity and tuning significance to temporal frequencies in areas 17/18. In addition, only at sites in areas 17/18 did the local field potential exhibit locking to a horizontal motion pattern extracted from a natural movie. These results suggest that area 21a is concerned with the analysis of spatial features but lacks a faithful representation of temporal features. Hence, they foster the hypothesis that cortical area 21a is part of a ventral form pathway.
Action Potentials↗
Fixations in natural scenes: interaction of image structure and image content.
Explorative eye movements specifically target some parts of a scene while ignoring others. Here, we investigate how local image structure--defined by spatial frequency contrast--and informative image content--defined by higher order image statistics-are weighted for the selection of fixation points. We measured eye movements of macaque monkeys freely viewing a set of natural and manipulated images outside a particular task. To probe the effect of scene content, we locally introduced patches of pink noise into natural images, and to probe the interaction with image structure, we altered the contrast of the noise. We found that fixations specifically targeted the natural image parts and spared the uninformative noise patches. However, both increasing and decreasing the contrast of the noise attracted more fixations, and, in the extreme cases, compensated the effect of missing content. Introducing patches from another natural image led to similar results. In all paradigms tested, the interaction between scene structure and informative scene content was the same in any of the first six fixations on an image, demonstrating that the weighting of these factors is constant during viewing of an image. These results question theories, which suggest that initial fixations are driven by stimulus structure whereas later fixations are determined by informative scene content.
Animals↗
Functional imaging reveals numerous fields in the monkey auditory cortex.
Anatomical studies propose that the primate auditory cortex contains more fields than have actually been functionally confirmed or described. Spatially resolved functional magnetic resonance imaging (fMRI) with carefully designed acoustical stimulation could be ideally suited to extend our understanding of the processing within these fields. However, after numerous experiments in humans, many auditory fields remain poorly characterized. Imaging the macaque monkey is of particular interest as these species have a richer set of anatomical and neurophysiological data to clarify the source of the imaged activity. We functionally mapped the auditory cortex of behaving and of anesthetized macaque monkeys with high resolution fMRI. By optimizing our imaging and stimulation procedures, we obtained robust activity throughout auditory cortex using tonal and band-passed noise sounds. Then, by varying the frequency content of the sounds, spatially specific activity patterns were observed over this region. As a result, the activity patterns could be assigned to many auditory cortical fields, including those whose functional properties were previously undescribed. The results provide an extensive functional tessellation of the macaque auditory cortex and suggest that 11 fields contain neurons tuned for the frequency of sounds. This study provides functional support for a model where three fields in primary auditory cortex are surrounded by eight neighboring "belt" fields in non-primary auditory cortex. The findings can now guide neurophysiological recordings in the monkey to expand our understanding of the processing within these fields. Additionally, this work will improve fMRI investigations of the human auditory cortex.
Animals↗
Texture signals in whisker vibrations.
Rodents excel in making texture judgments by sweeping their whiskers across a surface. Here we aimed to identify the signals present in whisker vibrations that give rise to such fine sensory discriminations. First, we used sensors to capture vibration signals in metal whiskers during active whisking of an artificial system and in natural whiskers during whisking of rats in vivo. Then we developed a classification algorithm that successfully matched the vibration frequency spectra of single trials to the texture that induced it. For artificial whiskers, the algorithm correctly identified one texture of eight alternatives on 40% of trials; for in vivo natural whiskers, the algorithm correctly identified one texture of five alternatives on 80% of trials. Finally, we asked which were the key discriminative features of the vibration spectra. Under both artificial and natural conditions, the combination of two features accounted for most of the information: The modulation power-the power of the part of the whisker movement representing the modulation due to the texture surface-increased with the coarseness of the texture; the modulation centroid-a measure related to the center of gravity within the power spectrum-decreased with the coarseness of the texture. Indeed, restricting the signal to these two parameters led to performance three-fourths as high as the full spectra. Because earlier work showed that modulation power and centroid are directly related to neuronal responses in the whisker pathway, we conclude that the biological system optimally extracts vibration features to permit texture classification.
Action Potentials↗
Mechanisms for allocating auditory attention: an auditory saliency map.
Our nervous system is confronted with a barrage of sensory stimuli, but neural resources are limited and not all stimuli can be processed to the same extent. Mechanisms exist to bias attention toward the particularly salient events, thereby providing a weighted representation of our environment. Our understanding of these mechanisms is still limited, but theoretical models can replicate such a weighting of sensory inputs and provide a basis for understanding the underlying principles. Here, we describe such a model for the auditory system-an auditory saliency map. We experimentally validate the model on natural acoustical scenarios, demonstrating that it reproduces human judgments of auditory saliency and predicts the detectability of salient sounds embedded in noisy backgrounds. In addition, it also predicts the natural orienting behavior of naive macaque monkeys to the same salient stimuli. The structure of the suggested model is identical to that of successfully used visual saliency maps. Hence, we conclude that saliency is determined either by implementing similar mechanisms in different unisensory pathways or by the same mechanism in multisensory areas. In any case, our results demonstrate that different primate sensory systems rely on common principles for extracting relevant sensory events.
Acoustic Stimulation↗
Integration of touch and sound in auditory cortex.
To form a coherent percept of the environment, our brain combines information from different senses. Such multisensory integration occurs in higher association cortices; but supposedly, it also occurs in early sensory areas. Confirming the latter hypothesis, we unequivocally demonstrate supra-additive integration of touch and sound stimulation at the second stage of the auditory cortex. Using high-resolution fMRI of the macaque monkey, we quantified the integration of auditory broad-band noise and tactile stimulation of hand and foot in anaesthetized animals. Integration was found posterior to and along the lateral side of the primary auditory cortex in the caudal auditory belt. Integration was stronger for temporally coincident stimuli and obeyed the principle of inverse effectiveness: greater enhancement for less effective stimuli. These findings demonstrates that multisensory integration occurs early and close to primary sensory areas and--because it occurs in anaesthetized animals--suggests that this integration is mediated by preattentive bottom-up mechanisms.
Animals↗
Population coding of orientation in the visual cortex of alert cats--an information theoretic analysis.
We studied the encoding of stimulus orientation in the visual cortex of alert animals using information theory methods. Based on a labeled-line code, the encoding of orientation was mostly synergistic and only few pairs coded redundant. The synergy contributed about 20% of the information and was strongest for sites with distinct tuning curves. A recently proposed decomposition of synergy revealed that redundancy introduced by common tuning preferences is more than just compensated by noise correlations which mostly contributed synergistically. Based on a pooled response code, the contribution of noise correlations diminished resulting in a severe information loss. Thus, to operate economically, cortical neurons should either employ a labeled-line code or, if using pooled responses, be highly selective in choosing afferents.
Animals↗
A comparison of hemodynamic and neural responses in cat visual cortex using complex stimuli.
We compare fMRI-BOLD responses in anesthetized cats with local field potentials (LFPs), aggregate high-frequency responses (analog-Mua) and spiking activity recorded in primary and higher visual cortex of alert animals. The similarity of the activations in these electrophysiological signals to those in the BOLD is quantified by counting recording sites where different stimuli elicit the same relative activation as in the imaging experiments. Using artificial stimuli, a comparison of BOLD and LFP strongly depends on the frequency range used. Stimulating with complex or natural stimuli reduces this frequency dependence and yields a good match of LFP and BOLD. In general, this match is best between 20 and 50 Hz. The measures of high-frequency activity behave qualitatively different: the responses of the analog-Mua match those of the LFP; the spiking activity shows a low concordance with the BOLD signal. This dissociation of BOLD and spiking activity is most prominent upon stimulation with natural stimuli.
Animals↗
Interactions between eye movement systems in cats and humans.
Eye movements can be broadly classified into target-selecting and gaze-stabilizing eye movements. How do the different systems interact under natural conditions? Here we investigate interactions between the optokinetic and the target-selecting system in cats and humans. We use combinations of natural and grating stimuli. The natural stimuli are movies and pictures taken from the cat's own point of view with a head-mounted camera while it moved about freely in an outdoor environment. We superimpose linear global motion on the stimuli and use measurements of optokinetic nystagmus as a probe to study the interaction between the different systems responsible for controlling eye movements. Cats display higher precision stabilizing eye movements in response to natural pictures as compared to drifting gratings. In contrast, humans perform similarly under these two conditions. This suggests an interaction of the optokinetic and the pursuit system. In cats, the natural movies elicit very weak optokinetic responses. In humans, by contrast, the natural movie stimuli elicit effectively stabilizing eye movements. In both species, we find a unimodal distribution of saccades for all stimulus velocities. This suggests an early interaction of target-selecting and gaze-stabilizing saccades. Thus, we argue for a more integrated view in humans of the different eye movement systems.
Animals↗
Effects of training on neuronal activity and interactions in primary and higher visual cortices in the alert cat.
The effects of behavioral training on early visual representations have been elusive when assessed with firing rates. Learning-induced changes in performance, however, suggest that representations should encompass early cortical stages. Here, we address the question of whether training-induced effects are pertinent to neuronal activity outside the task proper, which is a requirement if subsequent perceptional processes should profit from training. To search for a neuronal signature of training effects beyond firing rates, we measured local field potentials, multiunit and isolated spike activity during passive viewing of previously learned stimulus response associations (S+ and S-) in areas 17/18 and 21a of two alert cats. Evoked potential responses as well as gamma oscillations even during the first 200 msec were found to be stronger for S+ in both areas. Most importantly, the later parts of the response (>200 msec) not only exhibit a highly significant difference in coherent gamma oscillations for S+ and S- both within and across areas, but are also characterized by a pronounced preference in firing rate for S+ in area 21a, whereas primary cortex shows a nonsignificant trend for weaker spike responses. From these results, we conclude that training-induced plasticity occurs in adult visual cortex for behaviorally relevant stimuli by changing primarily the temporal structure of neuronal activity at early stages of cortical processing, whereas later stages of cortical processing express the increased coherence of their input in elevated firing rates.
Action Potentials↗
Processing of complex stimuli and natural scenes in the visual cortex.
A major part of vision research builds on the assumption that processing of visual stimuli can be understood on the basis of knowledge about the processing of simplified, artificial stimuli. Recent experimental advances, however, show that a combination of responses to simplified stimuli does not adequately describe responses to natural visual scenes. The systems performance exceeds the performance predicted from understanding its basic constituents. This highlights the fact that the visual system is specifically adapted to the properties of its everyday input and can only fully be understood when probed with naturalistic stimuli.
Animals↗
Stimulus locking and feature selectivity prevail in complementary frequency ranges of V1 local field potentials.
The local field potential (LFP) is a population measure of neuronal activity complementary to spike trains. Whereas the response properties of the spiking activity in the visual cortex have been characterized extensively, the responses of the LFP have not been well explored. No coherent picture exists about which frequency ranges exhibit feature tuning or show stimulus locked activity. Addressing this, we recorded LFP in the primary visual cortex of alert cats and calculated the tuning indices for orientation, spatial and temporal frequency. Furthermore, we quantified the locking of the power in different LFP frequency bands to the velocity profile of artificial and natural stimuli. We found that the LFP in alert animals is well tuned with similar specificity to orientation, spatial frequency and temporal frequency. Tuning to these features is most prominent in two frequency bands (8-23 Hz and 39-109 Hz). In two complementary frequency bands (23-39 Hz and above 109 Hz) the dynamics of the LFP power is locked tightly to the temporal structure of the stimulus. This locking is furthermore independent of the spatial structure of the stimulus. Together these four frequency bands cover the whole frequency range investigated. In contrast to previous studies, which often reported correlates of visual processing only in a limited frequency range of the LFP, the present results suggest that the entire frequency range of the LFP can be assigned a role in visual processing.
Action Potentials↗
Directed interactions between visual areas and their role in processing image structure and expectancy.
During sensory processing, cortical areas continuously exchange information in different directions along the hierarchy. The functional role of such interactions, however, has been the subject of various proposals. Here, we investigate the role of bottom-up and top-down interactions in processing stimulus structure and their relation to expected events. Applying multivariate autoregressive methods to local field potentials recorded in alert cats, we quantify directed interactions between primary (A17/18) and higher (A21) visual areas. A trial-by-trial analysis yields the following findings. To assess the role of interareal interactions in processing stimulus structure, we recorded in naïve animals during stimulation with natural movies and pink noise stimuli. The overall interactions decrease compared with baseline for both stimuli. To investigate whether forthcoming events modulate interactions, we recorded in trained animals viewing two stimuli, one of which had been associated with a reward. Several results support such modulations. First, the interactions increase compared with baseline and this increase is not observed in a context where food was not delivered. Second, these stimuli have a differential effect on top-down and bottom-up components. This difference is emphasized during the stimulus presentation and is maximal shortly before the possible reward. Furthermore, a spectral decomposition of the interactions shows that this asymmetry is most dominant in the gamma frequency range. Concluding, these results support the notion that interareal interactions are more related to an expectancy state rather than to processing of stimulus structure.
Animals↗
How are complex cell properties adapted to the statistics of natural stimuli?
Sensory areas should be adapted to the properties of their natural stimuli. What are the underlying rules that match the properties of complex cells in primary visual cortex to their natural stimuli? To address this issue, we sampled movies from a camera carried by a freely moving cat, capturing the dynamics of image motion as the animal explores an outdoor environment. We use these movie sequences as input to simulated neurons. Following the intuition that many meaningful high-level variables, e.g., identities of visible objects, do not change rapidly in natural visual stimuli, we adapt the neurons to exhibit firing rates that are stable over time. We find that simulated neurons, which have optimally stable activity, display many properties that are observed for cortical complex cells. Their response is invariant with respect to stimulus translation and reversal of contrast polarity. Furthermore, spatial frequency selectivity and the aspect ratio of the receptive field quantitatively match the experimentally observed characteristics of complex cells. Hence, the population of complex cells in the primary visual cortex can be described as forming an optimally stable representation of natural stimuli.
Animals↗
Responses to natural scenes in cat V1.
Studies on processing in primary visual areas often use artificial stimuli such as bars or gratings. As a result, little is known about the properties of activity patterns for the natural stimuli processed by the visual system on a daily basis. Furthermore, in the cat, a well-studied model system for visual processing, most results are obtained from anesthetized subjects and little is known about neuronal activations in the alert animal. Addressing these issues, we measure local field potentials (lfp) and multiunit spikes in the primary visual cortex of awake cats. We compare changes in the lfp power spectra and multiunit firing rates for natural movies, movies with modified spatio-temporal correlations as well as gratings. The activity patterns elicited by drifting gratings are qualitatively and quantitatively different from those elicited by natural stimuli and this difference arises from both spatial as well as temporal properties of the stimuli. Furthermore, both local field potentials and multiunit firing rates are most sensitive to the second-order statistics of the stimuli and not to their higher-order properties. Finally, responses to natural movies show a large variability over time because of activity fluctuations induced by rapid stimulus motion. We show that these fluctuations are not dependent on the detailed spatial properties of the stimuli but depend on their temporal jitter. These fluctuations are important characteristics of visual activity under natural conditions and impose limitations on the readout of possible differences in mean activity levels.
Action Potentials↗
Learning the nonlinearity of neurons from natural visual stimuli.
Learning in neural networks is usually applied to parameters related to linear kernels and keeps the nonlinearity of the model fixed. Thus, for successful models, properties and parameters of the nonlinearity have to be specified using a priori knowledge, which often is missing. Here, we investigate adapting the nonlinearity simultaneously with the linear kernel. We use natural visual stimuli for training a simple model of the visual system. Many of the neurons converge to an energy detector matching existing models of complex cells. The overall distribution of the parameter describing the nonlinearity well matches recent physiological results. Controls with randomly shuffled natural stimuli and pink noise demonstrate that the match of simulation and experimental results depends on the higher-order statistical properties of natural stimuli.
Models, Neurological↗
Learning distinct and complementary feature selectivities from natural colour videos.
Many biological and artificial neural networks require the parallel extraction of multiple features, and meet this requirement with distinct populations of neurons that are selective to one property of the stimulus while being non-selective to another property. In this way, several populations can resolve a set of features independently of each other, and thus achieve a parallel mode of processing. This raises the question how an initially homogeneous population of neurons segregates into groups with distinct and complementary response properties. Using a colour image sequence recorded from a camera mounted on the head of a freely behaving cat, we train a network of neurons to achieve optimally stable responses, that is, responses that change minimally over time. This objective leads to the development of colour-selective neurons. Adding a second objective, decorrelating activity within the network, a subpopulation of neurons develops with achromatic response properties. Colour selective neurons tend to be non-oriented while achromatic neurons are orientation-tuned. The proposed objective thus successfully leads to the segregation of neurons into complementary populations that are either selective for colour or orientation.
Animals↗