Search

Accurate segmentation of infected regions in lung computed tomography (CT) images is essential for the detection and diagnosis of coronavirus disease 2019 (COVID-19). However, lung lesion segmentation has some challenges, such as obscure boundaries, low contrast and scattered infection areas. In this paper, the dilated multiresidual boundary guidance network (Dmbg-Net) is proposed for COVID-19 infection segmentation in CT images of the lungs. This method focuses on semantic relationship modelling and boundary detail guidance. First, to effectively minimize the loss of significant features, a dilated residual block is substituted for a convolutional operation, and dilated convolutions are employed to expand the receptive field of the convolution kernel. Second, an edge-attention guidance preservation block is designed to incorporate boundary guidance of low-level features into feature integration, which is conducive to extracting the boundaries of the region of interest. Third, the various depths of features are used to generate the final prediction, and the utilization of a progressive multi-scale supervision strategy facilitates enhanced representations and highly accurate saliency maps. The proposed method is used to analyze COVID-19 datasets, and the experimental results reveal that the proposed method has a Dice similarity coefficient of 85.6% and a sensitivity of 84.2%. Extensive experimental results and ablation studies have shown the effectiveness of Dmbg-Net. Therefore, the proposed method has a potential application in the detection, labeling and segmentation of other lesion areas. [ABSTRACT FROM AUTHOR]

Over the last several decades, the mammalian visual system has been arguably the most widely studied subject in systems neuroscience. Two additional papers published in this special issue involve novel data acquisition and analysis methods, which will likely push forward future understanding of structure-function relationships in the visual system. This method enables diffeomorphic registration of multiple visual areas, and improves the accuracy of the group analysis of fMRI datasets on the human visual system. Research on the mammalian visual system has provided important insights for our understanding of the brain in general, artificial neural networks (Yamins et al. [31]), and intervention strategies for visual disorders (Beauchamp et al. [4]). [Extracted from the article]

Carriers of functionally deficient mutations in the CYP39A1 gene have been recently reported to have a 2-fold increased risk of exfoliation syndrome (XFS). The aim of this study was to evaluate the risk of blindness and related clinical phenotypes of XFS patients carrying the loss-of-function CYP39A1 G204E mutation in comparison with XFS patients without any CYP39A1 mutation. Retrospective case study. A total of 35 patients diagnosed with XFS carrying the CYP39A1 G204E mutation and 150 XFS patients without any CYP39A1 mutation who were randomly selected from the Japanese XFS cohort. Two-sided Fisher exact test with an alpha level < 0.05 was used to estimate the significance of the calculated odds ratio (OR) for all categorical measures. Comparisons between groups of subjects were performed using linear mixed effect models with group as random effect and taking possible dependence between eyes within a subject into account. Primary analysis compared the incidence of blindness (defined as visual acuity [VA] < 0.05 decimal), prevalence of exfoliation glaucoma (XFG), history of glaucoma surgery, and indices of glaucoma severity such as visual field (VF) mean deviation (MD), intraocular pressure (IOP), and vertical cup-disc ratio (CDR) between CYP39A1 G204E carriers and those without any CYP39A1 mutation. The overall risk for blindness was significantly higher in XFS patients carrying the CYP39A1 G204E variant (10/35 [28.6%]) compared with XFS patients without any CYP39A1 mutations (8/150 [5.4%]; odds ratio [OR], 7.1; 95% confidence interval [CI], 2.7–20.2]; P < 0.001). A higher proportion of XFS patients with the CYP39A1 G204E mutation (23/35 [65.7%]) had evidence of XFG in at least 1 eye compared with the comparison group (41/150 [27.3%]; OR, 5.1; 95% CI, 2.4–11.4]; P < 0.0001). Significantly higher peak IOP, larger vertical CDR, and worse VF MD were also found in CYP39A1 G204E variant carriers (P < 0.001). Additionally, patients with the CYP39A1 G204E mutation (18/35 [51.4%]) required more laser or glaucoma surgical interventions compared with those without any CYP39A1 mutation (32/150 [21.3%], P < 0.001). Patients with XFS carrying the CYP39A1 G204E mutation had significantly increased risk of blindness, higher occurrence of XFG, and more severe glaucoma compared with patients with XFS without any CYP39A1 mutation. [ABSTRACT FROM AUTHOR]

The rise of autonomous driving and mobile robots has drawn attention to real-time road scene segmentation. However, category confusion, incomplete segmentation and inaccurate detail contours are common issues encountered in road traffic scene segmentation tasks. To tackle these road segmentation challenges, we proposes a bilateral real-time semantic segmentation network based on multi-scale receptive fields(BSNet). To enhance the segmentation accuracy of detail contours, an auxiliary detail detection module is introduced in the spatial branch. In the semantic branch, strip pooling and channel attention are added to the short-term dense concatenate module to enrich multi-scale receptive fields and strengthen channel features, addressing issues such as incomplete segmentation and category confusion. The final design of the complementary dual-guided fusion module improves feature fusion effectiveness. Experimental results on two benchmark datasets demonstrate that BSNet significantly improves these road segmentation difficulties and achieves competitive results in both speed and accuracy compared to the current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Object detection is one of the main tasks of computer vision. Object detection algorithms usually rely on deep convolutional neural networks, which require the host device to have high computing capabilities, greatly limiting the application of object detection methods for mobile devices with limited computing capabilities, such as embedded devices. Among the current object detection algorithms, the you only look once (YOLO) series takes both speed and accuracy into consideration and is one of the most commonly used methods for object detection. In this article, TRC‐YOLO is proposed, which improves the mean average precision (mAP) and real‐time detection speed of the model while reducing the size of the model. In TRC‐YOLO, the convolution kernel of YOLO v4‐tiny is pruned and an expansive convolution layer is introduced into the residual module of the network to produce an hourglass Cross Stage Partial ResNet (CSPResNet) structure. A receptive field block (RFB) that simulates human vision is also added, increasing the receptive field of the model and strengthening the feature extraction ability of the network. In addition, the convolutional block attention module is applied, which combines spatial attention and channel attention, to enhance the effective features of the model and reduce the negative impact of noise on the model. The size of the TRC‐YOLO model is 17.8 MB, which is 5.9 MB smaller than YOLO v4‐tiny, and the model parameter is 2.983 billion floating point operations per second (BFLOP/s) (3.834 BFLOP/s less than YOLO v4‐tiny). In addition, TRC‐YOLO achieves a real‐time performance of 36.9 frames per second on a Jetson Xavier NX, and its mAP on the PASCAL VOC dataset is 66.4% (3.83% higher than YOLO v4‐tiny). In addition, the mAP of TRC‐YOLO on the MS COCO dataset is 37.7%, which is 1.9% higher than that of the baseline model. [ABSTRACT FROM AUTHOR]

The intelligentisation of coal mines is the only approach to the high‐quality development of the coal industry. Detection, identification and sorting of coal gangue is an important part of the intelligentisation of coal mines. Focusing on various problems in coal gangue detecting and recognising algorithms, such as limited receptive field, slow convergence rate and low accuracy of small particle recognition, this paper proposes a coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 (DCN‐YOLOv3). To improve the accuracy of anchor frame positioning and enhance the diversity of the dataset, the deformed convolution YOLOv3 network model is established based on the detection algorithm YOLOv3, using deformable convolution, multiple k‐means clustering results average method and data enhancement technology as means. The model was trained through the self‐designed dataset, and the algorithm's correctness and accuracy for coal gangue recognition under different size and illumination conditions are verified. The test results showed that the algorithm effectively detects and recognises coal gangue, improves the accuracy and efficiency of detecting and recognising small‐size coal and gangue and improves environmental robustness. Furthermore, compared with the traditional recognition algorithm, the network convergence speed of this algorithm is significantly improved, the mAP is increased to 99.45%, and the maximum FLOPs value is reduced by 61.4%. Accordingly, this research is considered to be of certain theoretical value and technical reference for identifying coal gangue. [ABSTRACT FROM AUTHOR]

Gene expression gradients radiating from regions of primary sensory cortices have recently been described and are thought to underlie the large-scale organization of the human cerebral cortex. However, the role of transcription in the functional layout of a single region within the adult brain has yet to be clarified, likely owing to the difficulty of identifying a brain region anatomically consistent enough across individuals with dense enough tissue sampling. Overcoming these hurdles in human primary visual cortex (V1), we show a relationship between differential gene expression and the cortical layout of eccentricity in human V1. Interestingly, these genes are unique from those previously identified that contribute to the positioning of cortical areas in the visual processing hierarchy. Enrichment analyses show that a subset of the identified genes encode for structures related to inhibitory interneurons, ion channels, as well as cellular projections, and are expressed more in foveal compared to peripheral portions of human V1. These findings predict that tissue density should be higher in foveal compared to peripheral V1. Using a histological pipeline, we validate this prediction using Nissl-stained sections of postmortem occipital cortex. We discuss these findings relative to previous studies in non-human primates, as well as in the context of an organizational pattern in which the adult human brain employs transcription gradients at multiple spatial scales: across the cerebral cortex, across areas within processing hierarchies, and within single cortical areas. [ABSTRACT FROM AUTHOR]

Non-invasive neuromodulation technology is important for the treatment of brain diseases. The effects of focused ultrasound on neuronal activity have been investigated since the 1920s. Low intensity transcranial focused ultrasound (tFUS) can exert non-destructive mechanical pressure effects on cellular membranes and ion channels and has been shown to modulate the activity of peripheral nerves, spinal reflexes, the cortex, and even deep brain nuclei, such as the thalamus. It has obvious advantages in terms of security and spatial selectivity. This technology is considered to have broad application prospects in the treatment of neurodegenerative disorders and neuropsychiatric disorders. This review synthesizes animal and human research outcomes and offers an integrated description of the excitatory and inhibitory effects of tFUS in varying experimental and disease conditions. [ABSTRACT FROM AUTHOR]

Rubin's face-vase illusion demonstrates how one can switch back and forth between two different interpretations depending on how the figure outlines are assigned. In the primate visual system, assigning ownership along figure borders is encoded by neurons called the border ownership (BO) cells. Studies show that the responses of these neurons not only depend on the local features within their receptive fields, but also on contextual information. Despite two decades of studies on BO neurons, the ownership assignment mechanism in the brain is still unknown. Here, we propose a hierarchical recurrent model grounded on the hypothesis that neurons in the dorsal stream provide the context required for ownership assignment. Our proposed model incorporates early recurrence from the dorsal pathway as well as lateral modulations within the ventral stream. While dorsal modulations initiate the response difference to figure on either side of the border, lateral modulations enhance the difference. We found responses of our dorsally-modulated BO cells, similar to their biological counterparts, are invariant to size, position and solid/outlined figures. Moreover, our model BO cells exhibit comparable levels of reliability in the ownership signal to biological BO neurons. We found dorsal modulations result in high levels of accuracy and robustness for BO assignments in complex scenes compared to previous models based on ventral feedback. Finally, our experiments with illusory contours suggest that BO encoding could explain the perception of such contours in higher processing stages in the brain. [ABSTRACT FROM AUTHOR]

The human hand can detect both form and texture information of a contact surface. The detection of skin displacement (sustained stimulus) and changes in skin displacement (transient stimulus) are thought to be mediated in different tactile channels; however, tactile form perception may use both types of information. Here, we studied whether both the temporal frequency and the temporal coherency information of tactile stimuli encoded in sensory neurons could be used to recognize the form of contact surfaces. We used the fishbone tactile illusion (FTI), a known tactile phenomenon, as a probe for tactile form perception in humans. This illusion typically occurs with a surface geometry that has a smooth bar and coarse textures in its adjacent areas. When stroking the central bar back and forth with a fingertip, a human observer perceives a hollow surface geometry even though the bar is physically flat. We used a passive high-density pin matrix to extract only the vertical information of the contact surface, suppressing tangential displacement from surface rubbing. Participants in the psychological experiment reported indented surface geometry by tracing over the FTI textures with pin matrices of the different spatial densities (1.0 and 2.0 mm pin intervals). Human participants reported that the relative magnitude of perceived surface indentation steeply decreased when pins in the adjacent areas vibrated in synchrony. To address possible mechanisms for tactile form perception in the FTI, we developed a computational model of sensory neurons to estimate temporal patterns of action potentials from tactile receptive fields. Our computational data suggest that (1) the temporal asynchrony of sensory neuron responses is correlated with the relative magnitude of perceived surface indentation and (2) the spatiotemporal change of displacements in tactile stimuli are correlated with the asynchrony of simulated sensory neuron responses for the fishbone surface patterns. Based on these results, we propose that both the frequency and the asynchrony of temporal activity in sensory neurons could produce tactile form perception. [ABSTRACT FROM AUTHOR]

Persuasion is a crucial component of the courtship ritual needed to overcome contact aversion. In fruit flies, it is well established that the male courtship song prompts receptivity in female flies, in part by causing sexually mature females to slow down and pause, allowing copulation. Whether the above receptivity behaviours require the suppression of contact avoidance or escape remains unknown. Here we show, through genetic manipulation of neurons we identified as required for female receptivity, that male song induces avoidance/escape responses that are suppressed in wild type flies. First, we show that silencing 70A09 neurons leads to an increase in escape, as females increase their walking speed during courtship together with an increase in jumping and a reduction in pausing. The increase in escape response is specific to courtship, as escape to a looming threat is not intensified. Activation of 70A09 neurons leads to pausing, confirming the role of these neurons in escape modulation. Finally, we show that the escape displays by the female result from the presence of a courting male and more specifically from the song produced by a courting male. Our results suggest that courtship song has a dual role, promoting both escape and pause in females and that escape is suppressed by the activity of 70A09 neurons, allowing mating to occur. [ABSTRACT FROM AUTHOR]

Luminance contrast is one of the key factors in the visibility of objects in the world around us. Previous work has shown that the perceived depth from binocular disparity depends profoundly on the luminance contrast of the image. This dependence cannot be explained by existing disparity models, such as the well-established disparity energy model, because they predict no effect of luminance contrast on depth perception. Here, we develop a model for disparity processing that incorporates contrast normalization of the neural response into the disparity energy model to account for the contrast dependence of perceived depth from disparity. Our model contains an array of disparity channels, each with a different disparity selectivity. The binocular images are first processed by the left- and right-eye receptive fields of each channel. The outputs of the two receptive fields are combined linearly as the excitatory disparity sensitivity and then fed into a nonlinear contrast gain control mechanism. The perceived depth is determined by the weighted average of all the disparity channels that respond to the binocular images. This model provides the first analytic account of how luminance contrast affects perceived depth from disparity. [ABSTRACT FROM AUTHOR]

Visual stimuli presented around the time of a saccade have been shown to be perceived differently by the visual system, including a reduction in the harmful impact of flankers (crowding). However, whether the effects observed are due strictly to crowding remains controversial, and the effects have only been measured with large saccades in peripheral vision. Here we investigate how crowded stimuli placed 20 arc minutes from the center of gaze are affected by an upcoming microsaccade. The stimulus consisted of a rotated T of size 6.25 arcminutes that was either unflanked, surrounded by four flankers (Experiment 1), or surrounded by two flankers that were positioned either radially or tangentially (Experiments 2 and 3). In 80 % of trials, subjects made voluntary microsaccades to the target when cued, and in the remaining 20 % of the trials subjects continued to maintain fixation. In Experiments 1 and 2, subjects were required to saccade to the same location as the target, while in Experiment 3 subjects saccaded to a different location ∼ 20 arc min to the upper left of the target. Thus, we provide evidence for two separable pre-saccadic benefits for crowded parafoveal targets: one isotropizes the crowding zone for stimuli presented 200 to 125 ms before microsaccadic onset, and another provides a benefit exclusively for microsaccade targets surrounded by tangential flankers in the presence of imminent microsaccades. Two possible mechanisms are attentional enhancement and predictive remapping of receptive fields, respectively. [ABSTRACT FROM AUTHOR]

Delivering appropriate stimuli remains a challenge in vision research, particularly for aquatic animals such as zebrafish. Due to the shape of the water tank and the associated optical paths of light rays, the stimulus can be subject to unwanted refraction or reflection artifacts, which may spoil the experiment and result in wrong conclusions. Here, we employ computer graphics simulations and calcium imaging in the zebrafish optic tectum to show, how a spherical glass container optically outperforms many previously used water containers, including Petri dish lids. We demonstrate that aquatic vision experiments suffering from total internal reflection artifacts at the water surface or at the flat container bottom may result in the erroneous detection of visual neurons with bipartite receptive fields and in the apparent absence of neurons selective for vertical motion. Our results and demonstrations will help aquatic vision neuroscientists on optimizing their stimulation setups. [ABSTRACT FROM AUTHOR]

To obtain deeper insights into the tactile processing pathway from a population-level point of view, we have modeled three stages of the tactile pathway from the periphery to the cortex in response to indentation and scanned edge stimuli at different orientations. Three stages in the tactile pathway are, (1) the first-order neurons which innervate the cutaneous mechanoreceptors, (2) the cuneate nucleus in the midbrain and (3) the cortical neurons of the somatosensory area. In the proposed network, the first layer mimics the spiking patterns generated by the primary afferents. These afferents have complex skin receptive fields. In the second layer, the role of lateral inhibition on projection neurons in the cuneate nucleus is investigated. The third layer acts as a biomimetic decoder consisting of pyramidal and cortical interneurons that correspond to heterogeneous receptive fields with excitatory and inhibitory sub-regions on the skin. In this way, the activity of pyramidal neurons is tuned to the specific edge orientations. By modifying afferent receptive field size, it is observed that the larger receptive fields convey more information about edge orientation in the first spikes of cortical neurons when edge orientation stimuli move across the patch of skin. In addition, the proposed spiking neural model can detect edge orientation at any location on the simulated mechanoreceptor grid with high accuracy. The results of this research advance our knowledge about tactile information processing and can be employed in prosthetic and bio-robotic applications. [ABSTRACT FROM AUTHOR]

Objectives: Diabetic encephalopathy (DE) is a serious complication of diabetes mainly occurring in the elderly patients. Berberine (BBR) is an isoquinoline alkaloids extracted from Coptis chinensis that is applied in the treatment of diabetes clinically. This study explored the possible mechanism of BBR in relieving DE. Methods: Wistar rats were injected with streptozotocin and fed a high fat diet to establish the model of DE. The model rats were treated with BBR. The body weight, blood glucose and insulin of rats were measured, and Morris water maze test was conducted to evaluate the learning and memory abilities. The pathological conditions of cortical tissues were detected. The cortical mitochondria membrane potential (MMP) and reactive oxygen species (ROS) were monitored. The expressions of Rho/ROCK pathway-related genes of rat cortex were detected. The changes of MMP and ROS were detected after the treatment of Rho/ROCK pathway activator. Results: The body weight of model rats changed little, and levels of blood glucose and insulin were increased. The spatial learning and memory abilities were impaired, with disordered cortical neurons, and obvious neurons apoptosis and glia proliferation. BBR alleviated cognitive dysfunction and pathological damage in rats with DE. BBR enhanced cortical MMP and suppressed ROS. BBR treatment inhibited the Rho/ROCK pathway. Activation of the Rho/ROCK pathway reversed the effects of BBR on MMP and ROS. Conclusion: BBR elevated MMP and reduced ROS in rats with DE by inhibiting the Rho/ROCK pathway. This study may offer novel insights for the management of DE. [ABSTRACT FROM AUTHOR]

• PL neurons only respond well to acoustic stimuli in the awake state. • PL neurons responded better to broadband noise than pure tone in awake state. • No obvious receptive fields found to pure tone on all the recorded neurons. • Responses of PL neurons to the CS increased after auditory fear conditioning. • Lower threshold and higher SNR relative to increased responses. Although many studies have shown that the prelimbic (PL) cortex of the mPFC is involved in the formation of conditioned freezing behavior, few have considered the acoustic response characteristics of PL cortex. Importantly, the change in auditory response characteristics of the PL cortex after conditional fear learning is largely unknown. Here we used in vivo cell-attached recordings targeting the mPFC during the waking state. We confirmed that the mPFC of adult C57 mice have neurons that respond to noise and tone in the waking state, especially in the PL cortex. Interestingly, the data also confirmed that these neurons responded well to the intensity of sound but did not have frequency topological distribution characteristics. Furthermore, we found that the number of c-fos positive neurons in the PL cortex increased significantly after auditory fear conditioning. The auditory-induced local field potential recordings and in vivo cell-attached recordings demonstrated that the PL cortex was more sensitive to the auditory conditioned stimulus after the acquisition of conditioned fear. The proportion of neurons responding to noise was significantly increased, and the signal to noise ratio of the spikes were also increased. These data reveal that PL neurons themselves responded to the main information (sound intensity), while the secondary information (frequency) response was almost negligible after auditory fear conditioning. This phenomenon may be the functional basis for handling this type of emotional memory, and this response characteristic is thought to be emotional sensitization but does not change the nature of this response. [ABSTRACT FROM AUTHOR]

An impossible structure gives us the impression of looking at a three-dimensional object, even though this object cannot exist, since it possesses parts that are spatially non-connectable, and are characterized by misleading geometrical properties not instantly evident. Therefore, impossible artworks appeal to our intellect and challenge our perceptive capacities. We analyzed lithographs containing impossible structures (e.g., the Necker cube), created by the famous Dutch painter Maurits Cornelis Escher (1898-1972), and used one of them (The Belvedere, 1958) to unveil the artist's hidden secrets by means of a discrete model of the human retina based on a non-uniform distribution of receptive fields. We demonstrated that the ability of Escher in composing his lithographs by connecting spatial coherent details into an impossible whole lies in drawing these incoherent fragments just outside the zone in which 3D coherence can be perceived during a single fixation pause. The main aspects of our paper from the point of view of image processing and image understanding are the following: (1) the peculiar and original digital filter to process the image, which simulates the human vision process, by producing a space-variant sampling of the image; (2) the software for the filter, which is homemade and created for our purposes. The filtered images resulting from the processing are used to understand impossible figures. As an example, we demonstrate how the impossible figures hidden in Escher's paintings can be understood. [ABSTRACT FROM AUTHOR]

A receptive field code is a recently proposed deterministic model of neural activity patterns in response to stimuli. The main question is to characterize the set of realizable codes, and their minimal embedding dimensions with respect to a given family of receptive fields. Here we answer both of these questions when the receptive fields are connected. In particular, we show that all connected codes are realizable in dimension at most three. To our knowledge, this is the first family of receptive field codes for which both the exact characterization and minimal embedding dimension are known. [ABSTRACT FROM AUTHOR]

Lamina I spino-parabrachial neurons (SPNs) receive peripheral nociceptive input, process it and transmit to the supraspinal centres. Although responses of SPNs to cutaneous receptive field stimulations have been intensively studied, the mechanisms of signal processing in these neurons are poorly understood. Therefore, we used an ex-vivo spinal cord preparation to examine synaptic and cellular mechanisms determining specific input-output characteristics of the neurons. The vast majority of the SPNs received a few direct nociceptive C-fiber inputs and generated one spike in response to saturating afferent stimulation, thus functioning as simple transducers of painful stimulus. However, 69% of afferent stimulation-induced action potentials in the entire SPN population originated from a small fraction (19%) of high-output neurons. These neurons received a larger number of direct Aδ- and C-fiber inputs, generated intrinsic bursts and efficiently integrated a local network activity via NMDA-receptor-dependent mechanisms. The high-output SPNs amplified and integrated the nociceptive input gradually encoding its intensity into the number of generated spikes. Thus, different mechanisms of signal processing allow lamina I SPNs to play distinct roles in nociception. [ABSTRACT FROM AUTHOR]

In functional imaging, large numbers of neurons are measured during sensory stimulation or behavior. This data can be used to map receptive fields that describe neural associations with stimuli or with behavior. The temporal resolution of these receptive fields has traditionally been limited by image acquisition rates. However, even when acquisitions scan slowly across a population of neurons, individual neurons may be measured at precisely known times. Here, we apply a method that leverages the timing of neural measurements to find receptive fields with temporal resolutions higher than the image acquisition rate. We use this temporal super-resolution method to resolve fast voltage and glutamate responses in visual neurons in Drosophila and to extract calcium receptive fields from cortical neurons in mammals. We provide code to easily apply this method to existing datasets. This method requires no specialized hardware and can be used with any optical indicator of neural activity. The temporal resolution of optical measurements of neural activity has traditionally been limited by the image or volume acquisition rate. Here, the authors describe an analysis that exploits the short duration of neural measurements within each image to extract neural responses at higher temporal resolution than the acquisition rate. [ABSTRACT FROM AUTHOR]

Designer receptors exclusively activated by designer drugs (DREADDs) are extensively used to modulate neuronal activity in rodents, but their use in primates remains limited. An essential need that remains is the demonstration that DREADDs are efficiently expressed on the plasma membrane of primate neurons. To address this issue, electron microscopy immunogold was used to determine the subcellular localization of the AAV vector‐induced DREADDs hM4Di and hM3Dq fused to different tags in various brain areas of rhesus monkeys and mice. When hM4Di was fused to mCherry, the immunogold labelling was mostly confined to the intracellular space, and poorly expressed at the plasma membrane in monkey dendrites. In contrast, the hM4Di‐mCherry labelling was mostly localized to the dendritic plasma membrane in mouse neurons, suggesting species differences in the plasma membrane expression of these exogenous proteins. The lack of hM4Di plasma membrane expression may limit the functional effects of systemic administration of DREADD‐actuators in monkey neurons. Removing the mCherry and fusing of hM4Di with the haemagglutinin (HA) tag resulted in strong neuronal plasma membrane immunogold labelling in both monkeys and mice neurons. Finally, hM3Dq‐mCherry was expressed mostly at the plasma membrane in monkey neurons, indicating that the fusion of mCherry with hM3Dq does not hamper membrane incorporation of this specific DREADD. Our results suggest that the pattern of ultrastructural expression of DREADDs in monkey neurons depends on the DREADD/tag combination. Therefore, a preliminary characterization of plasma membrane expression of specific DREADD/tag combinations is recommended when using chemogenetic approaches in primates. [ABSTRACT FROM AUTHOR]

Previous studies have shown that the auditory cortex can enhance the perception of behaviorally important sounds in the presence of background noise, but the mechanisms by which it does this are not yet elucidated. Rapid plasticity of spectrotemporal receptive fields (STRFs) in the primary (A1) cortical neurons is observed during behavioral tasks that require discrimination of particular sounds. This rapid task-related change is believed to be one of the processing strategies utilized by the auditory cortex to selectively attend to one stream of sound in the presence of mixed sounds. However, the mechanism by which the brain evokes this rapid plasticity in the auditory cortex remains unclear. This paper uses a neural network model to investigate how synaptic transmission within the cortical neuron network can change the receptive fields of individual neurons. A sound signal was used as input to a model of the cochlea and auditory periphery, which activated or inhibited integrate-and-fire neuron models to represent networks in the primary auditory cortex. Each neuron in the network was tuned to a different frequency. All neurons were interconnected with excitatory or inhibitory synapses of varying strengths. Action potentials in one of the model neurons were used to calculate the receptive field using reverse correlation. The results were directly compared to previously recorded electrophysiological data from ferrets performing behavioral tasks that require discrimination of particular sounds. The neural network model could reproduce complex STRFs observed experimentally through optimizing the synaptic weights in the model. The model predicts that altering synaptic drive between cortical neurons and/or bottom-up synaptic drive from the cochlear model to the cortical neurons can account for rapid task-related changes observed experimentally in A1 neurons. By identifying changes in the synaptic drive during behavioral tasks, the model provides insights into the neural mechanisms utilized by the auditory cortex to enhance the perception of behaviorally salient sounds. [ABSTRACT FROM AUTHOR]

Highlights • The hippocampus generates ordered spike sequences to support memory functions. • However, not all synchronized spike sequences contain similar ensemble pattern. • Some synchronized events include non-spatial pyramidal cells. • Non-ordered spike sequences may play functional roles in enriching brain functions. Abstract The hippocampal network generates synchronized spikes of a large population of pyramidal neurons associated with sharp-wave ripples in local field potential signals. Ample evidence demonstrates that the synchronized spikes are created by sequential activation of hippocampal place cells that correspond to the animal's past or future trajectories and are hypothesized to play instrumental roles in mnemonic functions. However, not all place-cell spike sequences are precisely organized, and some sequences are composed of spikes from non-spatial cells, implying that not all hippocampal synchronized events directly replicate learned behavioral episodes. While less attention has been given to such non-ordered spike sequences, variable and dynamic selection of active neuronal assemblies may be optimal mechanisms for rapidly reorganizing functional circuits and self-developing novel representations to enable flexible decision-making processes. We recently showed that specific neurons, including both spatial and non-spatial cells, are preferentially recruited in synchronous events for particular time periods, suggesting that there are temporally fluctuating background states of the hippocampal network that determine active neuronal ensembles. Based on recent reports, this review discusses potential roles of the low-fidelity, heterogeneous repertoire of synchronized spike sequences of hippocampal neurons. [ABSTRACT FROM AUTHOR]

Edge detection is one of the most basic stages of image processing and have been used in many areas. Its purpose is to determine the pixels formed the objects. Many researchers have aimed to determine objects' edges correctly, like as they are determined by the human eye. In this study, a new edge detection technique based on spiking neural network is proposed. The proposed model has a different receptor structure than the ones found in literature and also does not use gray level values of the pixels in the receptive field directly. Instead, it takes the gray level differences between the pixel in the center of the receptive field and others as input. The model is tested by using BSDS train dataset. Besides, the obtained results are compared with the results calculated by Canny edge detection method. [ABSTRACT FROM AUTHOR]

Dilated convolutions support expanding receptive field without parameter exploration or resolution loss, which turn out to be suitable for pixel-level prediction problems. In this paper, we propose multiscale single image super-resolution (SR) based on dilated convolutions. We adopt dilated convolutions to expand the receptive field size without incurring additional computational complexity. We mix standard convolutions and dilated convolutions in each layer, called mixed convolutions, i.e., in the mixed convolutional layer, and the feature extracted by dilated convolutions and standard convolutions are concatenated. We theoretically analyze the receptive field and intensity of mixed convolutions to discover their role in SR. Mixed convolutions remove blind spots and capture the correlation between low-resolution (LR) and high-resolution (HR) image pairs successfully, thus achieving good generalization ability. We verify those properties of mixed convolutions by training 5-layer and 10-layer networks. We also train a 20-layer deep network to compare the performance of the proposed method with those of the state-of-the-art ones. Moreover, we jointly learn maps with different scales from a LR image to its HR one in a single network. Experimental results demonstrate that the proposed method outperforms the state-of-the-art ones in terms of PSNR and SSIM, especially for a large-scale factor. [ABSTRACT FROM AUTHOR]

The noninvasive estimation of neuronal receptive field (RF) properties in vivo allows a detailed understanding of brain organization as well as its plasticity by longitudinal following of potential changes. Visual RFs measured invasively by electrophysiology in animal models have traditionally provided a great extent of our current knowledge about the visual brain and its disorders. Voxel-based estimates of population RF (pRF) by functional magnetic resonance imaging (fMRI) in humans revolutionized the field and have been used extensively in numerous studies. However, current methods cannot estimate single-neuron RF sizes as they reflect large populations of neurons with individual RF scatter. Here, we introduce an approach to estimate RF size using spatial frequency selectivity to checkerboard patterns. This method allowed us to obtain noninvasive, average single-neuron RF estimates over a large portion of human early visual cortex. These estimates were significantly smaller compared with prior pRF methods. Furthermore, fMRI and electrophysiology experiments in nonhuman primates demonstrated an exceptionally good match, validating the approach. [ABSTRACT FROM AUTHOR]

The ability to detect environmental cold serves as an important survival tool. The sodium channels NaV1.8 and NaV1.9, as well as the TRP channel Trpm8, have been shown to contribute to cold sensation in mice. Surprisingly, transcriptional profiling shows that NaV1.8/NaV1.9 and Trpm8 are expressed in nonoverlapping neuronal populations. Here we have used in vivo GCaMP3 imaging to identify cold-sensing populations of sensory neurons in live mice. We find that ~80% of neurons responsive to cold down to 1 °C do not express NaV1.8, and that the genetic deletion of NaV1.8 does not affect the relative number, distribution, or maximal response of cold-sensitive neurons. Furthermore, the deletion of NaV1.8 had no observable effect on transient cold-induced (≥5 °C) behaviors in mice, as measured by the cold-plantar, cold-plate (5 and 10 °C), or acetone tests. In contrast, nocifensive-like behavior to extreme cold-plate stimulation (-5 °C) was completely absent in mice lacking NaV1.8. Fluorescence-activated cell sorting (FACS) and subsequent microarray analysis of sensory neurons activated at 4 °C identified an enriched repertoire of ion channels, which include the Trp channel Trpm8 and potassium channel Kcnk9, that are potentially required for cold sensing above freezing temperatures in mouse DRG neurons. These data demonstrate the complexity of cold-sensing mechanisms in mouse sensory neurons, revealing a principal role for NaV1.8-negative neurons in sensing both innocuous and acute noxious cooling down to 1 °C, while NaV1.8-positive neurons are likely responsible for the transduction of prolonged extreme cold temperatures, where tissue damage causes pan-nociceptor activation. [ABSTRACT FROM AUTHOR]

In this work, we investigate event-based feature extraction through a rigorous framework of testing. We test a hardware efficient variant of Spike Timing Dependent Plasticity (STDP) on a range of spatio-temporal kernels with different surface decaying methods, decay functions, receptive field sizes, feature numbers, and back end classifiers. This detailed investigation can provide helpful insights and rules of thumb for performance vs. complexity trade-offs in more generalized networks, especially in the context of hardware implementation, where design choices can incur significant resource costs. The investigation is performed using a new dataset consisting of model airplanes being dropped free-hand close to the sensor. The target objects exhibit a wide range of relative orientations and velocities. This range of target velocities, analyzed in multiple configurations, allows a rigorous comparison of time-based decaying surfaces (time surfaces) vs. event index-based decaying surface (index surfaces), which are used to perform unsupervised feature extraction, followed by target detection and recognition. We examine each processing stage by comparison to the use of raw events, as well as a range of alternative layer structures, and the use of random features. By comparing results from a linear classifier and an ELM classifier, we evaluate how each element of the system affects accuracy. To generate time and index surfaces, the most commonly used kernels, namely event binning kernels, linearly, and exponentially decaying kernels, are investigated. Index surfaces were found to outperform time surfaces in recognition when invariance to target velocity was made a requirement. In the investigation of network structure, larger networks of neurons with large receptive field sizes were found to perform best. We find that a small number of event-based feature extractors can project the complex spatio-temporal event patterns of the dataset to an almost linearly separable representation in feature space, with best performing linear classifier achieving 98.75% recognition accuracy, using only 25 feature extracting neurons. [ABSTRACT FROM AUTHOR]

Orientation sensitive properties of extrastriate area 21a neurons were investigated. Special attention was paid to the spatial characteristics of the neuron receptive fields (RF) to the different orientations of visual stimulus motion across neuron RF, defined by presentation of stationary visual stimuli. The results of provided experiments have shown that the neuron RF spatial dimensions undergo essential modulations concerning different orientations of applied visual stimuli. As a result, a high degree of diversification of neuronal response patterns depending of the orientation of moving stimuli was observed. Thus, the processing of visual information in the course of visual image recognition is realized by integrated activity of surrounding cortical networks of visually sensitive neurons. [ABSTRACT FROM AUTHOR]

Abstract Cells in the peripheral retina tend to have higher contrast sensitivity and respond at higher flicker frequencies than those closer to the fovea. Although this predicts increased behavioural temporal contrast sensitivity in the peripheral visual field, this effect is rarely observed in psychophysical experiments. It is unknown how temporal contrast sensitivity is represented across eccentricity within cortical visual field maps and whether such sensitivities reflect the response properties of retinal cells or psychophysical sensitivities. Here, we used functional magnetic resonance imaging (fMRI) to measure contrast sensitivity profiles at four temporal frequencies in five retinotopically-defined visual areas. We also measured population receptive field (pRF) parameters (polar angle, eccentricity, and size) in the same areas. Overall contrast sensitivity, independent of pRF parameters, peaked at 10 Hz in all visual areas. In V1, V2, V3, and V3a, peripherally-tuned voxels had higher contrast sensitivity at a high temporal frequency (20 Hz), while hV4 more closely reflected behavioural sensitivity profiles. We conclude that our data reflect a cortical representation of the increased peripheral temporal contrast sensitivity that is already present in the retina and that this bias must be compensated later in the cortical visual pathway. Highlights • Contrast sensitivity to 20Hz flicker is increased in peripheral voxels, when compared to foveal voxels, within V1, V2, V3, and V3a. • In hV4, contrast sensitivity at 20Hz is unchanged across eccentricity, similar to psychophysical sensitivities. • Our data suggest that the temporal response properties of peripheral retinal cells are represented in the cortical periphery of early visual areas. • Temporal filtering may occur in higher-order ventral visual areas, consistent with an eccentricity-dependent invariance to lower level visual properties such as temporal contrast. [ABSTRACT FROM AUTHOR]

Visual processing is largely organized into ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways exhibit natural pairings based on morphological and physiological similarities, such as ON and OFF a-ganglion cells in the mammalian retina. Several studies have noted asymmetries in the properties of ON and OFF pathways. For example, the spatial receptive fields (RFs) of OFF a-cells are systematically smaller than ON a-cells. Analysis of natural scenes suggests that these asymmetries are optimal for visual encoding. To test the generality of ON/OFF asymmetries, we measured the spatiotemporal RF properties of multiple RGC types in rat retina. Through a quantitative and serial classification, we identified three functional pairs of ON and OFF RGCs. We analyzed the structure of their RFs and compared spatial integration, temporal integration, and gain across ON and OFF pairs. Similar to previous results from the cat and primate, RGC types with larger spatial RFs exhibited briefer temporal integration and higher gain. However, each pair of ON and OFF RGC types exhibited distinct asymmetric relationships between RF properties, some of which were opposite to the findings of previous reports. These results reveal the functional organization of six RGC types in the rodent retina and indicate that ON/OFF asymmetries are pathway specific. [ABSTRACT FROM AUTHOR]

Retinal ganglion cells (RGCs) were one of the first classes of sensory neurons to be described in terms of a receptive field (RF). Over the last six decades, our understanding of the diversity of RGC types and the nuances of their response properties has grown exponentially. We will review the current understanding of RGC RFs mostly from studies in mammals, but including work from other vertebrates as well. We will argue for a new paradigm that embraces the fluidity of RGC RFs with an eye toward the neuroethology of vision. Specifically, we will focus on (1) different methods for measuring RGC RFs, (2) RF models, (3) feature selectivity and the distinction between fluid and stable RF properties, and (4) ideas about the future of understanding RGC RFs. [ABSTRACT FROM AUTHOR]

Abstract We introduce a probabilistic (Bayesian) framework and associated software toolbox for mapping population receptive fields (pRFs) based on fMRI data. This generic approach is intended to work with stimuli of any dimension and is demonstrated and validated in the context of 2D retinotopic mapping. The framework enables the experimenter to specify generative (encoding) models of fMRI timeseries, in which experimental stimuli enter a pRF model of neural activity, which in turns drives a nonlinear model of neurovascular coupling and Blood Oxygenation Level Dependent (BOLD) response. The neuronal and haemodynamic parameters are estimated together on a voxel-by-voxel or region-of-interest basis using a Bayesian estimation algorithm (variational Laplace). This offers several novel contributions to receptive field modelling. The variance/covariance of parameters are estimated, enabling receptive fields to be plotted while properly representing uncertainty about pRF size and location. Variability in the haemodynamic response across the brain is accounted for. Furthermore, the framework introduces formal hypothesis testing to pRF analysis, enabling competing models to be evaluated based on their log model evidence (approximated by the variational free energy), which represents the optimal tradeoff between accuracy and complexity. Using simulations and empirical data, we found that parameters typically used to represent pRF size and neuronal scaling are strongly correlated, which is taken into account by the Bayesian methods we describe when making inferences. We used the framework to compare the evidence for six variants of pRF model using 7 T functional MRI data and we found a circular Difference of Gaussians (DoG) model to be the best explanation for our data overall. We hope this framework will prove useful for mapping stimulus spaces with any number of dimensions onto the anatomy of the brain. Highlights • We introduce a Bayesian toolbox for population receptive field (pRF) mapping. • Neuronal and haemodynamic parameters are estimated per voxel or per region. • Hypotheses can be tested by comparing pRF models based on their evidence. • The uncertainty over parameters (such as pRF size) is estimated and visualised. • We establish face validity using simulations and test-rest reliability with 7 T fMRI. [ABSTRACT FROM AUTHOR]

Abstract Visual processing in cortex relies on feedback projections contextualising feedforward information flow. Primary visual cortex (V1) has small receptive fields and processes feedforward information at a fine-grained spatial scale, whereas higher visual areas have larger, spatially invariant receptive fields. Therefore, feedback could provide coarse information about the global scene structure or alternatively recover fine-grained structure by targeting small receptive fields in V1. We tested if feedback signals generalise across different spatial frequencies of feedforward inputs, or if they are tuned to the spatial scale of the visual scene. Using a partial occlusion paradigm, functional magnetic resonance imaging (fMRI) and multivoxel pattern analysis (MVPA) we investigated whether feedback to V1 contains coarse or fine-grained information by manipulating the spatial frequency of the scene surround outside an occluded image portion. We show that feedback transmits both coarse and fine-grained information as it carries information about both low (LSF) and high spatial frequencies (HSF). Further, feedback signals containing LSF information are similar to feedback signals containing HSF information, even without a large overlap in spatial frequency bands of the HSF and LSF scenes. Lastly, we found that feedback carries similar information about the spatial frequency band across different scenes. We conclude that cortical feedback signals contain information which generalises across different spatial frequencies of feedforward inputs. Highlights • The influence of spatial frequency on cortical feedback was investigated. • Both high and low spatial frequency surround information led to meaningful feedback. • Feedback signals can generalise over spatial frequency in the same scene. • Feedback carries similar information about the spatial frequency band across scenes. [ABSTRACT FROM AUTHOR]

The archerfish is a predator with highly unusual visually guided behavior. It is most famous for its ability to hunt by shooting water jets at static or dynamic insect prey, up to two meters above the water's surface. In the lab, the archerfish can learn to distinguish and shoot at artificial targets presented on a computer screen, thus enabling well-controlled experiments. In recent years, these capacities have turned the archerfish into a model animal for studying a variety of visual functions, from visual saliency and visual search, through fast visually guided prediction, and all the way to higher level visual processing such as face recognition. Here we review these recent developments and show how they fall into two emerging lines of research on this animal model. The first is ethologically motivated and emphasizes how the natural environment and habitat of the archerfish interact with its visual processing during predation. The second is driven by parallels to the primate brain and aims to determine whether the latter's characteristic visual information processing capacities can also be found in the qualitatively different fish brain, thereby underscoring the functional universality of certain visual processes. We discuss the differences between these two lines of research and possible future directions. [ABSTRACT FROM AUTHOR]

The two paralogs of the calcium-dependent activator protein for secretion (CAPS) are priming factors for synaptic vesicles (SVs) and neuropeptide containing large dense-core vesicles (LDCVs). Yet, it is unclear whether CAPS1 and CAPS2 regulate exocytosis of these two vesicle types differentially in dorsal root ganglion (DRG) neurons, wherein synaptic transmission and neuropeptide release are of equal importance. These sensory neurons transfer information from the periphery to the spinal cord (SC), releasing glutamate as the primary neurotransmitter, with co-transmission via neuropeptides in a subset of so called peptidergic neurons. Neuropeptides are key components of the information-processing machinery of pain perception and neuropathic pain generation. Here, we compared the ability of CAPS1 and CAPS2 to support priming of both vesicle types in single and double knock-out mouse (DRG) neurons using a variety of high-resolution live cell imaging methods. While CAPS1 was localized to synapses of all DRG neurons and promoted synaptic transmission, CAPS2 was found exclusively in peptidergic neurons and mediated LDCV exocytosis. Intriguingly, ectopic expression of CAPS2 empowered non-peptidergic neurons to drive LDCV fusion, thereby identifying CAPS2 as an essential molecular determinant for peptidergic signaling. Our results reveal that these distinct functions of both CAPS paralogs are based on their differential subcellular localization in DRG neurons. Our data suggest a major role for CAPS2 in neuropathic pain via control of neuropeptide release. [ABSTRACT FROM AUTHOR]

Developing animals must begin to interact with the world before their neural development is complete. This means they must build neural codes appropriate for turning sensory inputs into motor outputs adaptively as their neural hardware matures. We review some recent progress in the understanding of the relationship between neural coding and neural circuit development. We focus particularly on neural coding in the context of topographic maps and spontaneous activity, as well as receptive field and circuit development, drawing on examples from both mammalian visual cortex and fish optic tectum. Overall we suggest that neural coding strategies during development may be highly dynamic. [ABSTRACT FROM AUTHOR]

Our perception of where touch occurs on our skin shapes our interactions with the world. Most accounts of cutaneous localisation emphasise spatial transformations from a skin-based reference frame into body-centred and external egocentric coordinates. We investigated another possible method of tactile localisation based on an intrinsic perception of 'skin space'. The arrangement of cutaneous receptive fields (RFs) could allow one to track a stimulus as it moves across the skin, similarly to the way animals navigate using path integration. We applied curved tactile motions to the hands of human volunteers. Participants identified the location midway between the start and end points of each motion path. Their bisection judgements were systematically biased towards the integrated motion path, consistent with the characteristic inward error that occurs in navigation by path integration. We thus showed that integration of continuous sensory inputs across several tactile RFs provides an intrinsic mechanism for spatial perception. [ABSTRACT FROM AUTHOR]

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 neuron's 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. [ABSTRACT FROM AUTHOR]

The need for fast and dynamic processing of relevant information imposes high demands onto the flexibility and efficiency of the nervous system. A good example for such flexibility is the attention-dependent selection of relevant sensory information. Studies investigating attentional modulations of neuronal responses to simultaneously arriving input showed that neurons respond, as if only the attended stimulus would be present within their receptive fields (RF). However, attention also improves neuronal representation and behavioral performance, when only one stimulus is present. Thus, attention serves for selecting relevant input and changes the neuronal processing of signals representing selected stimuli, ultimately leading to a more efficient behavioral performance. Here, we tested the hypothesis that attention configures the strength of functional coupling between a local neuronal network's neurons specifically for effective processing of signals representing attended stimuli. This coupling is measured as the strength of γ-synchronization between these neurons. The hypothesis predicts that the pattern of synchronization in local networks should depend on which stimulus is attended. Furthermore, we expect this pattern to be similar for the attended stimulus presented alone or together with irrelevant stimuli in the RF. To test these predictions, we recorded spiking-activity and local field potentials (LFP) with closely spaced electrodes in area V4 of monkeys performing a demanding attention task. Our results show that the γ-band phase coherence (γ-PhC) between spiking-activity and the LFP, as well as the spiking-activity of two groups of neurons, strongly depended on which of the two stimuli in the RF was attended. The γ-PhC was almost identical for the attended stimulus presented either alone or together with a distractor. The functional relevance of dynamic γ-band synchronization is further supported by the observation of strongly degraded γ-PhC before behavioral errors, while firing rates were barely affected. These qualitatively different results point toward a failure of attention-dependent top-down mechanisms to correctly synchronize the local neuronal network in V4, even though this network receives the correctly selected input. These findings support the idea of a flexible, demand-dependent dynamic configuration of local neuronal networks, for performing different functions, even on the same sensory input. [ABSTRACT FROM AUTHOR]

Background: The timing of action potentials ("spikes") of cortical neurons has been shown to be aligned to the phase of low-frequency (< 10 Hz) local field potentials (LFPs) in several cortical areas. However, across the areas, this alignment varies and the role of this spike-phase coupling (SPC) in cognitive functions is not well understood. Results: Here, we propose a role in the coordination of neural activity by selective attention. After refining previous analytical methods for measuring SPC, we show that first, SPC is present along the dorsal processing pathway in macaque visual cortex (area MT); second, spikes occur in falling phases of the low-frequency LFP independent of the location of spatial attention; third, switching spatial attention into the receptive field (RF) of MT neurons decreases this coupling; and finally, the LFP phase causally influences the spikes. Conclusions: Here, we show that spikes are coupled to the phase of low-frequency LFP along the dorsal visual pathway. Our data suggest that attention harnesses this spike-LFP coupling to de-synchronize neurons and thereby enhance the neural representation of the attended stimuli. [ABSTRACT FROM AUTHOR]

Visual spatial attention concentrates neural resources at the attended location. Recently, we demonstrated that voluntary spatial attention attracts population receptive fields (pRFs) toward its location throughout the visual hierarchy. Theoretically, both a feed forward or feedback mechanism could underlie pRF attraction in a given cortical area. Here, we use sub-millimeter ultra-high field functional MRI to measure pRF attraction across cortical depth and assess the contribution of feed forward and feedback signals to pRF attraction. In line with previous findings, we find consistent attraction of pRFs with voluntary spatial attention in V1. When assessed as a function of cortical depth, we find pRF attraction in every cortical portion (deep, center and superficial), although the attraction is strongest in deep cortical portions (near the gray-white matter boundary). Following the organization of feed forward and feedback processing across V1, we speculate that a mixture of feed forward and feedback processing underlies pRF attraction in V1. Specifically, we propose that feedback processing contributes to the pRF attraction in deep cortical portions. [ABSTRACT FROM AUTHOR]

Subjective assessments of spatial regularity are common in everyday life and also in science, for example in developmental biology. It has recently been shown that regularity is an adaptable visual dimension. It was proposed that regularity is coded via the peakedness of the distribution of neural responses across receptive field size. Here, we test this proposal for jittered square lattices of dots. We examine whether discriminability correlates with a simple peakedness measure across different presentation conditions (dot number, size, and average spacing). Using a filter-rectify-filter model, we determined responses across scale. Consistently, two peaks are present: a lower frequency peak corresponding to the dot spacing of the regular pattern and a higher frequency peak corresponding to the pattern element (dot). We define the "peakedness" of a particular presentation condition as the relative heights of these two peaks for a perfectly regular pattern constructed using the corresponding dot size, number and spacing. We conducted two psychophysical experiments in which observers judged relative regularity in a 2-alternative forced-choice task. In the first experiment we used a single reference pattern of intermediate regularity and, in the second, Thurstonian scaling of patterns covering the entire range of regularity. In both experiments discriminability was highly correlated with peakedness for a wide range of presentation conditions. This supports the hypothesis that regularity is coded via peakedness of the distribution of responses across scale. [ABSTRACT FROM AUTHOR]

The neurophysiological mechanisms underlying the central processing of incoming visual information is still a fundamental problem in the visual neuroscience. The response patterns of visually sensitive neurons in extrastriate area 21a of the cat cortex to the stationary and moving visual stimuli were studied in detail. The results of experiments show significant modulations of neuronal response patterns at the introduction of a moving visual stimulus, which could not be explained by the neuron receptive field (RF) stationary structure. The results of presented experiments have shown that neurons having homogenous stationary RFs structure may reveal multimodal patterns of responses to moving visual stimuli and such response profiles could be evoked from the isolated RF subfields too. Thus, with great probability, mutual interactions among simultaneously activated neighbouring neuron networks become to be of great importance in central processing of visual information, concerning the precise diversification and perception of visual images. [ABSTRACT FROM AUTHOR]

The cat perigeniculate nucleus (PGN) is a visual sector of the thalamic reticular nucleus that consists of GABAergic neurons. It receives excitatory axon-collateral input from relay neurons of the dorsal lateral geniculate nucleus (LGN) to which it provides inhibitory input. Thus, it is usually argued that the PGN works as feedback inhibition to the LGN. At the single neuron level, however, this circuit can also provide lateral inhibition. Which inhibition dominates in the visual circuit of the thalamus has yet to be well characterized. In this study, we conducted cross-correlation analysis of single spike trains simultaneously recorded from PGN and LGN neurons in anesthetized cats. For 12 pairs of functionally connected PGN and LGN neurons with overlapped receptive fields (RF), we quantitatively compared RF properties including the spatial frequency (SF) and temporal frequency (TF) tunings of each neuron. We found the SF and TF tunings of PGN neurons and LGN neurons were similar when there was only excitatory input from the LGN neuron to the PGN neuron, but different when the PGN neuron returned inhibitory inputs back, suggesting the circuit between PGN and LGN neurons works as lateral inhibition for these properties. [ABSTRACT FROM AUTHOR]

Pyramidal neural networks (PNN) are computational systems inspired by the concept of receptive fields from the human visual system. These neural networks are designed for implicit feature extraction and have been applied in pattern recognition applications. In the original approach, the size of the receptive field within the same 2D layer is a constant parameter, while in the human visual system, the receptive field size is variable. This paper proposes a PNN with variable receptive fields determined by an evolutionary algorithm, called variable pyramidal neural network with evolutionary algorithms. We observed from experiments aiming at detecting faces in images that our approach can achieve better classification rates than the original PNN. We also observed that regions with more information (such as nose and eyes) are more emphasized by variable receptive fields. These results confirm the application of intelligent algorithms to determine adjustable receptive fields in neural networks is useful to find out relevant information for recognition task. Besides, the model is comparable to biological systems regarding the flexibility assigned to receptive fields. [ABSTRACT FROM AUTHOR]