Local field potentials,spiking activity,and receptive fields in human visual cortex

The concept of receptive field(RF) is central to sensory neuroscience. Neuronal RF properties have been substantially studied in animals,while those in humans remain nearly unexplored. Here, we measured neuronal RFs with intracranial local field potentials(LFPs) and spiking activity in human visual cortex(V1/V2/V3). We recorded LFPs via macro-contacts and discovered that RF sizes estimated from lowfrequency activity(LFA, 0.5–30 Hz) were larger than those estimated from low-gamma activity(LGA, 30–60 Hz) and high-gamma activity(HGA, 60–150 Hz). We then took a rare opportunity to record LFPs and spiking activity via microwires in V1 simultaneously. We found that RF sizes and temporal profiles measured from LGA and HGA closely matched those from spiking activity. In sum, this study reveals that spiking activity of neurons in human visual cortex could be well approximated by LGA and HGA in RF estimation and temporal profile measurement, implying the pivotal functions of LGA and HGA in early visual information processing.

Learning adaptive receptive fields for deep image parsing networks

In this paper,we introduce a novel approach to automatically regulate receptive fields in deep image parsing networks.Unlike previous work which placed much importance on obtaining better receptive fields using manually selected dilated convolutional kernels,our approach uses two affine transformation layers in the network’s backbone and operates on feature maps.Feature maps are inflated or shrunk by the new layer,thereby changing the receptive fields in the following layers.By use of end-to-end training,the whole framework is data-driven,without laborious manual intervention.The proposed method is generic across datasets and different tasks.We have conducted extensive experiments on both general image parsing tasks,and face parsing tasks as concrete examples,to demonstrate the method’s superior ability to regulate over manual designs.

Coal rock image recognition method based on improved CLBP and receptive field theory

Rapid coal-rock identification is one of the key technologies for intelligent and unmanned coal mining.Currently,the existing image recognition algorithms cannot satisfy practical needs in terms of recognition speed and accuracy.In view of the evident differences between coal and rock in visual attributes such as color,gloss and texture,the complete local binary pattern(CLBP)image feature descriptor is introduced for coal and rock image recognition.Given that the original algorithm oversimplifies local texture features by ignoring imaging information from higher-order pixels and the concave and convex areas between adjacent sampling points,this paper proposes a higher-order differential median CLBP image feature descriptor to replace the original CLBP center pixel gray with a local gray median,and replace the binary differential with a second-order differential.Meanwhile,for the high dimensionality of CLBP descriptor histogram and feature redundancy,deep learning perceptual field theory is introduced to realize data nonlinear dimensionality reduction and deep feature extraction.With relevant experiments conducted,the following conclusion can be drawn:(1)Compared with that of the original CLBP,the recognition accuracy of the improved CLBP algorithm is greatly improved and finally stabilized above 94.3%under strong noise interference;(2)Compared with that of the original CLBP model,the single image recognition time of the coal rock image recognition model fusing the improved CLBP and the receptive field theory is 0.0035 s,a reduction of 71.0%;compared with the improved CLBP model(without the fusion of receptive field theory),it can shorten the recognition time by 97.0%,but the accuracy rate still maintains more than 98.5%.The method offers a valuable technical reference for the fields of mineral development and deep mining.

A New Method for Image Tamper Detection Based on an Improved U-Net

With the improvement of image editing technology,the threshold of image tampering technology decreases,which leads to a decrease in the authenticity of image content.This has also driven research on image forgery detection techniques.In this paper,a U-Net with multiple sensory field feature extraction(MSCU-Net)for image forgery detection is proposed.The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing.MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the features of different perceptual fields use residual propagation and residual feedback to recall the input feature information and consolidate the input feature information to make the difference in image attributes between the untampered and tampered regions more obvious,and introduce the channel coordinate confusion attention mechanism(CCCA)in skip-connection to further improve the segmentation accuracy of the network.In this paper,extensive experiments are conducted on various mainstream datasets,and the results verify the effectiveness of the proposed method,which outperforms the state-of-the-art image forgery detection methods.

Properties of tactile responses of neurons in rat thalamic ventroposterolateral nucleus

Objective To determine whether the convergences of tactile information also occur at thalamic ventroposterolateral nucleus in rats, we investigated the properties of tactile responses of the thalamic ventroposterolateral nucleus in rats. Methods Unit responses were recorded extracellularly from thalamic ventroposterolateral nucleus in anesthetized rats. Results Among 156 neurons examined, 140 neurons （89.7%） had the single, continual and small receptive fields, and 16 neurons （10.3%） had two discrete receptive fields. Some neurons exhibited different responses to the same intensity stimulation which delivered to different points in their receptive fields. In addition, 4.5% neurons （n -- 7） responded only to locomotive stimulation but not to a punctiform tactile stimulation. Conclusion The majority of neurons in ventroposterolateral nucleus of rats have the spatial, temporal and submodal characteristics of cutaneous receptors, while the minority of neurons exhibit the responses of interaction of different peripheral receptors. Therefore, it is con- cluded that there are convergences of tactile information at the ventroposterolateral nucleus of rats.

Bilateral U-Net semantic segmentation with spatial attention mechanism

Aiming at the problem that the existing models have a poor segmentation effect on imbalanced data sets with small-scale samples,a bilateral U-Net network model with a spatial attention mechanism is designed.The model uses the lightweight MobileNetV2 as the backbone network for feature hierarchical extraction and proposes an Attentive Pyramid Spatial Attention(APSA)module compared to the Attenuated Spatial Pyramid module,which can increase the receptive field and enhance the information,and finally adds the context fusion prediction branch that fuses high-semantic and low-semantic prediction results,and the model effectively improves the segmentation accuracy of small data sets.The experimental results on the CamVid data set show that compared with some existing semantic segmentation networks,the algorithm has a better segmentation effect and segmentation accuracy,and its mIOU reaches 75.85%.Moreover,to verify the generality of the model and the effectiveness of the APSA module,experiments were conducted on the VOC 2012 data set,and the APSA module improved mIOU by about 12.2%.

Feature-Enhanced RefineDet: Fast Detection of Small Objects

Object detection has been studied for many years.The convolutional neural network has made great progress in the accuracy and speed of object detection.However,due to the low resolution of small objects and the representation of fuzzy features,one of the challenges now is how to effectively detect small objects in images.Existing target detectors for small objects:one is to use high-resolution images as input,the other is to increase the depth of the CNN network,but these two methods will undoubtedly increase the cost of calculation and time-consuming.In this paper,based on the RefineDet network framework,we propose our network structure RF2Det by introducing Receptive Field Block to solve the problem of small object detection,so as to achieve the balance of speed and accuracy.At the same time,we propose a Medium-level Feature Pyramid Networks,which combines appropriate high-level context features with low-level features,so that the network can use the features of both the low-level and the high-level for multi-scale target detection,and the accuracy of the small target detection task based on the low-level features is improved.Extensive experiments on the MS COCO dataset demonstrate that compared to other most advanced methods,our proposed method shows significant performance improvement in the detection of small objects.

Visual response characteristics of neurons in the second visual area of marmosets

The physiological characteristics of the marmoset second visual area(V2) are poorly understood compared with those of the primary visual area(V1). In this study, we observed the physiological response characteristics of V2 neurons in four healthy adult marmosets using intracortical tungsten microelectrodes. We recorded 110 neurons in area V2, with receptive fields located between 8° and 15° eccentricity. Most(88.2%) of these neurons were orientation selective, with half-bandwidths typically ranging between 10° and 30°. A significant proportion of neurons(28.2%) with direction selectivity had a direction index greater than 0.5. The vast majority of V2 neurons had separable spatial frequency and temporal frequency curves and, according to this criterion, they were not speed selective. The basic functional response characteristics of neurons in area V2 resemble those found in area V1. Our findings show that area V2 together with V1 are important in primate visual processing, especially in locating objects in space and in detecting an object's direction of motion. The methods used in this study were approved by the Monash University Animal Ethics Committee, Australia(MARP 2009-2011) in 2009.

Visuospatial properties of caudal area 7b in Macaca fascicularis

To proceed from sensation to movement,integration and transformation of information from different senses and reference frames are required.Several brain areas are involved in this transformation process,but previous neuroanatomical and neurophysiological studies have implicated the caudal area 7b as one particular component of this transformation system.In this study,we present the first quantitative report on the spatial coding properties of caudal area 7b.The results showed that neurons in this area had intermediate component characteristics in the transformation system;the area contained bimodal neurons,and neurons in this area encode spatial information using a hybrid reference frame.These results provide evidence that caudal area 7b may belong to the reference frame transformation system,thus contributing to our general understanding of the transformation system.

Visuospatial properties of caudal area 7b in Macaca fascicularis

To proceed from sensation to movement, integration and transformation of information from different senses and reference frames are required. Several brain areas are involved in this transformation process, but previous neuroanatomical and neurophysiological studies have implicated the caudal area 7b as one particular component of this transformation system. In this study, we present the first quantitative report on the spatial coding properties of caudal area 7b. The results showed that neurons in this area had intermediate component characteristics in the transformation system; the area contained bimodal neurons, and neurons in this area encode spatial information using a hybrid reference frame. These results provide evidence that caudal area 7b may belong to the reference frame transformation system, thus contributing to our general understanding of the transformation system.

A neural network model on self-organizing emergence of simple-cell receptive field with orientation selectivity in visual cortex

In order to probe into the self-organizing emergence of simple cell orientation selectivity,we tried to construct a neural network model that consists of LGN neurons and simple cells in visual cortex and obeys the Hebbian learning rule. We investigated the neural coding and representation of simple cells to a natural image by means of this model. The results show that the structures of their receptive fields are determined by the preferred orientation selectivity of simple cells.However, they are also decided by the emergence of self-organization in the unsupervision learning process. This kind of orientation selectivity results from dynamic self-organization based on the interactions between LGN and cortex.

A novel spiking neural network of receptive field encoding with groups of neurons decision

Human information processing depends mainly on billions of neurons which constitute a complex neural network,and the information is transmitted in the form of neural spikes.In this paper,we propose a spiking neural network(SNN),named MD-SNN,with three key features:(1) using receptive field to encode spike trains from images;(2) randomly selecting partial spikes as inputs for each neuron to approach the absolute refractory period of the neuron;(3) using groups of neurons to make decisions.We test MD-SNN on the MNIST data set of handwritten digits,and results demonstrate that:(1) Different sizes of receptive fields influence classification results significantly.(2) Considering the neuronal refractory period in the SNN model,increasing the number of neurons in the learning layer could greatly reduce the training time,effectively reduce the probability of over-fitting,and improve the accuracy by 8.77%.(3) Compared with other SNN methods,MD-SNN achieves a better classification;compared with the convolution neural network,MD-SNN maintains flip and rotation invariance(the accuracy can remain at 90.44% on the test set),and it is more suitable for small sample learning(the accuracy can reach 80.15%for 1000 training samples,which is 7.8 times that of CNN).

High glucose levels impact visual response properties of retinal ganglion cells in C57 mice—An in vitro physiological study

This study investigated visual response properties of retinal ganglion cells(RGCs) under high glucose levels. Extracellular single-unit responses of RGCs from mouse retinas were recorded. And the eyecup was prepared as a flat mount in a recording chamber and superfused with Ames medium. The averaged RF size of the ON RGCs(34.1±2.9, n=14) was significantly smaller than the OFF RGCs under the HG(49.3±0.3, n=12)(P<0.0001) conditions. The same reduction pattern was also observed in the osmotic control group(HM) between ON and OFF RGCs(P<0.0001). The averaged luminance threshold(LT) of ON RGCs increased significantly under HG or HM(HG: P<0.0001; HM: P<0.0002). OFF RGCs exhibited a similar response pattern under the same conditions(HG: P<0.01; HM: P<0.0002). The averaged contrast gain of ON cells was significantly lower than that of OFF cells with the HM treatment(P<0.015, unpaired Student's t test). The averaged contrast gain of ON cells was significantly higher than OFF cells with the HG treatment(P<0.0001). The present results suggest that HG reduced receptive field center size, suppressed luminance threshold, and attenuated contrast gain of RGCs. The impact of HG on ON and OFF RGCs may be mediated via different mechanisms.

Spikes with short inter-spike intervals in frog retinal ganglion cells are more correlated with their adjacent neurons’activities

Correlated firings among neurons have been extensively investigated;however,previous studies on retinal ganglion cell(RGC)population activities were mainly based on analyzing the correlated activities between the entire spike trains.In the present study,the correlation properties were explored based on burst-like activities and solitary spikes separately.The results indicate that:(1)burst-like activities were more correlated with other neurons’activities;(2)burst-like spikes correlated with their neighboring neurons represented a smaller receptive field than that of correlated solitary spikes.These results suggest that correlated burst-like spikes should be more efficient in signal transmission,and could encode more detailed spatial information.

Influence of GABAergic Pathway on Retinal Adaptation-Related Response Changes

Retinal ganglion cells(RGCs) exhibit adaptive changes in response to sustained light stimulation,which include decrease in firing rate, tendency to shrink in receptive field(RF) size and reduction in synchronized activities. Gamma-aminobutyric acid-ergic(GABAergic) pathway is an important inhibitory pathway in retina.In the present study, the effects of GABAergic pathway on the contrast adaptation process of bullfrog RGCs were studied using multi-electrode recording technique. It was found that the application of bicuculline(BIC), a gamma-aminobutyric acid A(GABAA) receptor antagonist, caused a number of changes in the RGCs' response characteristics, including attenuation in adaptation-dependent firing rate decrease and the adaptation-dependent weakening in synchronized activities between adjacent neuron-pairs, whereas intensified the adaptation-dependent RF size shrinkage. These results suggest that GABAAreceptors are involved in the modulation of the firing activity and synchronized activities in contrast adaptation process of the RGCs, whereas the adaptation-related RF property changes involve more complicated mechanisms.