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.     

Spatiotemporal organization of simple-cell receptive fields in area 18 of cat''''s cortex

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A cooperation and competition based simple cell receptive field model and study of feed-forward linear and nonlinear contributions to orientation selectivity

We present a model for development of orientation selectivity in layer IV simple cells. Receptive field (RF) development in the model, is determined by diffusive cooperation and resource limited competition guided axonal growth and retraction in geniculocortical pathway. The simulated cortical RFs resemble experimental RFs. The receptive field model is incorporated in a three-layer visual pathway model consisting of retina, LGN and cortex. We have studied the effect of activity dependent synaptic scaling on orientation tuning of cortical cells. The mean value of hwhh (half width at half the height of maximum response) in simulated cortical cells is 58° when we consider only the linear excitatory contribution from LGN. We observe a mean improvement of 22.8° in tuning response due to the non-linear spiking mechanisms that include effects of threshold voltage and synaptic scaling factor.     

Spatiotemporal organization of simple-cell receptive fields in area 18 of cat''s cortex

Spatiotemporal structures of receptive-fields (RF) have been studied for simple cells in area 18 of eat by measuring the temporal transfer function (TTF) over different locations (subregions) within the RF. The temporal characteristics of different subregions differed from each other in the absolute phase shift (APS) to visual stimuli. Two types of relationships can be seen: (i)The APS varied continuously from one subregion to the next: (ii) A 180°-phase jump was seen as the stimulus position changed somewhere within the receptive field. Spatiotemporal receptive field profiles have been determined by applying reverse Fourier analysis to responses in the frequency domain. For the continuous type, spatial and temporal characteristics cannot be dissociated (space time inseparable) and the spatiotemporal structure is oriented. On the contrary, the spatial and temporal characteristics for the jumping type can be dissociated (space-time separable) and the structure is not oriented in the space-time plane. Based on the APSs measured at different subregions, the optimal direction of motion and optimal spatial frequency of neurons can be predicted. Project supported by the National Natural Science Foundation of China (Grant Nos. 39570206, 39330110) and the Laboratory of Visual Information Processing, Chinese Academy of Sciences.     

Prediction of orientation selectivity from receptive field architecture in simple cells of cat visual cortex

From the intracellularly recorded responses to small, rapidly flashed spots, we have quantitatively mapped the receptive fields of simple cells in the cat visual cortex. We then applied these maps to a feedforward model of orientation selectivity. Both the preferred orientation and the width of orientation tuning of the responses to oriented stimuli were well predicted by the model. Where tested, the tuning curve was well predicted at different spatial frequencies. The model was also successful in predicting certain features of the spatial frequency selectivity of the cells. It did not successfully predict the amplitude of the responses to drifting gratings. Our results show that the spatial organization of the receptive field can account for a large fraction of the orientation selectivity of simple cells.     

A neuronal network model of primary visual cortex explains spatial frequency selectivity

We address how spatial frequency selectivity arises in Macaque primary visual cortex (V1) by simulating V1 with a large-scale network model consisting of O(10⁴) excitatory and inhibitory integrate-and-fire neurons with realistic synaptic conductances. The new model introduces variability of the widths of subregions in V1 neuron receptive fields. As a consequence different model V1 neurons prefer different spatial frequencies. The model cortex has distributions of spatial frequency selectivity and of preference that resemble experimental findings from the real V1. Two main sources of spatial frequency selectivity in the model are the spatial arrangement of feedforward excitation, and cortical nonlinear suppression, a result of cortical inhibition. Action Editor: Jonathan D. Victor     

A neural network model for the development of direction selectivity in the visual cortex

A neural network model is proposed to explain the development of direction selectivity of cortical cells. The model is constructed under the following three hypotheses that are very plausible from recent neurophysiological findings. (1) Direction selectivity is developed by modifiable inhibitory synapses. (2) It results not from the direct convergence of many excitatory inputs from LGN cells but from cortical neural networks. (3) Direction-selective mechanism is independent of orientation-selective mechanism.—The model was simulated on a computer for a few kinds of inhibitory connections and initial conditions. The results were consistent with neurophysiological facts not only for normal cats but for cats reared in an abnormal visual environment.     

A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields

Computational models of primary visual cortex have demonstrated that principles of efficient coding and neuronal sparseness can explain the emergence of neurones with localised oriented receptive fields. Yet, existing models have failed to predict the diverse shapes of receptive fields that occur in nature. The existing models used a particular "soft" form of sparseness that limits average neuronal activity. Here we study models of efficient coding in a broader context by comparing soft and "bard" forms of neuronal sparseness. As a result of our analyses, we propose a novel network model for visual cortex. The model forms efficient visual representations in which the number of active neurones, rather than mean neuronal activity, is limited. This form of hard sparseness also economises cortical resources like synaptic memory and metabolic energy. Furthermore, our model accurately predicts the distribution of receptive field shapes found in the primary visual cortex of cat and monkey.     

The responses to illusory contours of neurons in cortex areas 17 and 18 of the cats

Responses to illusory contours (ICs) were sampled from neurons in cortical areas 17 and 18 of the anesthetized cats. For ICs sensitive cells, the differences of receptive field properties were compared when ICs and real contour stimuli were applied. Two hundred orientation or direction selective cells were studied. We find that about 42 percent of these cells were the ICs sensitive cells. Although their orientation or direction tuning curves to ICs bar and real bars were similar, the response modes (especially latency and time course) were different. The cells' responses to ICs were independent of the spatial phases of sinusoidal gratings, which composed the ICs. The cells' optimal spatial frequency to composing gratings the ICs was much higher than the one to moving gratings. Therefore, these cells really responded to the ICs rather than the line ends of composing gratings. For some kinds of velocity-tuning cells, the optimal velocity to moving ICs bar was much lower than the optimal velocity to moving     

The responses to illusory contours of neurons in cortex areas 17 and 18 of the cats

Responses to illusory contours (ICs) were sampled from neurons in cortical areas 17 and 18 of the anesthetized cats. For ICs sensitive cells, the differences of receptive field properties were compared when ICs and real contour stimuli were applied. Two hundred orientation or direction selective cells were studied. We find that about 42 percent of these cells were the ICs sensitive cells. Although their orientation or direction tuning curves to ICs bar and real bars were similar, the response modes (especially latency and time course) were different. The cells’ responses to ICs were independent of the spatial phases of sinusoidal gratings, which composed the ICs. The cells’ optimal spatial frequency to composing gratings the ICs was much higher than the one to moving gratings. Therefore, these cells really responded to the ICs rather than the line ends of composing gratings. For some kinds of velocity-tuning cells, the optimal velocity to moving ICs bar was much lower than the optimal velocity to moving bars. The present results demonstrate that some cells in areas 17 and 18 of cats have the ability to respond to ICs and have different response properties of the receptive fields to ICs and luminance boundaries via different neural mechanisms.     

A possible mechanism of stochastic resonance in the light of an extra-classical receptive field model of retinal ganglion cells

Traditionally the intensity discontinuities in an image are detected as zero-crossings of the second derivative with the help of a Laplacian of Gaussian (LOG) operator that models the receptive field of retinal Ganglion cells. Such zero-crossings supposedly form a raw primal sketch edge map of the external world in the primary visual cortex of the brain. Based on a new operator which is a linear combination of the LOG and a Dirac-delta function that models the extra-classical receptive field of the ganglion cells, we find that zero-crossing points thus generated, store in presence of noise, apart from the edge information, the shading information of the image in the form of density variation of these points. We have also shown that an optimal image contrast produces best mapping of the shading information to such zero-crossing density variation for a given amount of noise contamination. Furthermore, we have observed that an optimal amount of noise contamination reproduces the minimum optimal contrast and hence gives rise to the best representation of the original image. We show that this phenomenon is similar in nature to that of stochastic resonance phenomenon observed in psychophysical experiments.     

电刺激大鼠内侧额叶前皮质对听皮层神经元频率感受野可塑性的调制

本文应用常规电生理学技术,研究电刺激大鼠内侧额叶前皮质（medial prefrontal cortex,mPFC）对初级听皮层神经元频率感受野（receptive field,RF）可塑性的调制。电刺激mPFC,137个听皮层神经元（72．8％）RF可塑性受到影响,其中抑制性调制71个神经元（37．7％）,易化性调制66个神经元（35．1％）,其余51个神经元（27．2％）不受影响。mPFC的抑制性调制效应表现为,RF的偏移时间延长,恢复时间缩短。相反,mPFC的易化性调制效应表现为,RF的偏移时间缩短,恢复时间延长。电刺激mPFC对RF可塑性的调制与声、电刺激之间的时间间隔有关,最佳时间间隔介于5-30ms之间。结果提示,大鼠mPFC可以调制听皮层神经元的功能活动,可能参与听觉学习记忆过程。     

Multi-finger receptive field properties in primary somatosensory cortex: A revised account of the spatiotemporal integration functions of area 3b

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A computational neural model of orientation detection based on multiple guesses: comparison of geometrical and algebraic models

The implementation of Hubel-Wiesel hypothesis that orientation selectivity of a simple cell is based on ordered arrangement of its afferent cells has some difficulties. It requires the receptive fields (RFs) of those ganglion cells (GCs) and LGN cells to be similar in size and sub-structure and highly arranged in a perfect order. It also requires an adequate number of regularly distributed simple cells to match ubiquitous edges. However, the anatomical and electrophysiological evidence is not strong enough to support this geometry-based model. These strict regularities also make the model very uneconomical in both evolution and neural computation. We propose a new neural model based on an algebraic method to estimate orientations. This approach synthesizes the guesses made by multiple GCs or LGN cells and calculates local orientation information subject to a group of constraints. This algebraic model need not obey the constraints of Hubel-Wiesel hypothesis, and is easily implemented with a neural network. By using the idea of a satisfiability problem with constraints, we also prove that the precision and efficiency of this model are mathematically practicable. The proposed model makes clear several major questions which Hubel-Wiesel model does not account for. Image-rebuilding experiments are conducted to check whether this model misses any important boundary in the visual field because of the estimation strategy. This study is significant in terms of explaining the neural mechanism of orientation detection, and finding the circuit structure and computational route in neural networks. For engineering applications, our model can be used in orientation detection and as a simulation platform for cell-to-cell communications to develop bio-inspired eye chips.     

A neural network dynamic model reproducing the output signal of the ganglion cell

A functional model of a neural network reproducing the output signal of the ganglion cell is proposed. The model assumes that receptive fields with antagonistic center and periphery are formed.     

Dose distribution correction for the influence of magnetic field using a deep convolutional neural network for online MR-guided adaptive radiotherapy

PurposeThis study aimed to develop a deep convolutional neural network (CNN)-based dose distribution conversion approach for the correction of the influence of a magnetic field for online MR-guided adaptive radiotherapy.MethodsOur model is based on DenseNet and consists of two 2D input channels and one 2D output channel. These three types of data comprise dose distributions without a magnetic field (uncorrected), electron density (ED) maps, and dose distributions with a magnetic field. These data were generated as follows: both types of dose distributions were created using 15-field IMRT in the same conditions except for the presence or absence of a magnetic field with the GPU Monte Carlo dose in Monaco version 5.4; ED maps were acquired with planning CT images using a clinical CT-to-ED table at our institution. Data for 50 prostate cancer patients were used; 30 patients were allocated for training, 10 for validation, and 10 for testing using 4-fold cross-validation based on rectum gas volume. The accuracy of the model was evaluated by comparing 2D gamma-indexes against the dose distributions in each irradiation field with a magnetic field (true).ResultsThe gamma indexes in the body for CNN-corrected uncorrected dose against the true dose were 94.95% ± 4.69% and 63.19% ± 3.63%, respectively. The gamma indexes with 2%/2-mm criteria were improved by 10% in most test cases (99.36%).ConclusionsOur results suggest that the CNN-based approach can be used to correct the dose-distribution influences with a magnetic field in prostate cancer treatment.     

Lateral spike conduction velocity in the visual cortex affects spatial range of synchronization and receptive field size without visual experience: a learning model with spiking neurons

Classical receptive fields (cRF) increase in size from the retina to higher visual centers. The present work shows how temporal properties, in particular lateral spike velocity and spike input correlation, can affect cRF size and position without visual experience. We demonstrate how these properties are related to the spatial range of cortical synchronization if Hebbian learning dominates early development. For this, a largely reduced model of two successive levels of the visual cortex is developed (e.g., areas V1 and V2). It consists of retinotopic networks of spiking neurons with constant spike velocity in lateral connections. Feedforward connections between level 1 and 2 are additive and determine cRF size and shape, while lateral connections within level 1 are modulatory and affect the cortical range of synchronization. Input during development is mimicked by spike trains with spatially homogeneous properties and a confined temporal correlation width. During learning, the homogeneous lateral coupling shrinks to limited coupling structures defining synchronization and related association fields (AF). The size of level-1 synchronization fields determines the lateral coupling range of developing level-1-to-2 connections and, thus, the size of level-2 cRFs, even if the feedforward connections have distance-independent delays. AFs and cRFs increase with spike velocity in the lateral network and temporal correlation width of the input. Our results suggest that AF size of V1 and cRF size of V2 neurons are confined during learning by the temporal width of input correlations and the spike velocity in lateral connections without the need of visual experience. During learning from visual experience, a similar influence of AF size on the cRF size may be operative at successive levels of processing, including other parts of the visual system.     

Prediction of ground reaction forces during gait based on kinematics and a neural network model

Kinetic information during human gait can be estimated with inverse dynamics, which is based on anthropometric, kinematic, and ground reaction data. While collecting ground reaction data with a force plate is useful, it is costly and requires regulated space. The goal of this study was to propose a new, accurate methodology for predicting ground reaction forces (GRFs) during level walking without the help of a force plate. To predict GRFs without a force plate, the traditional method of Newtonian mechanics was used for the single support phase. In addition, an artificial neural network (ANN) model was applied for the double support phase to solve statically indeterminate structure problems. The input variables of the ANN model, which were selected to have both dependency and independency, were limited to the trajectory, velocity, and acceleration of the whole segment's mass centre to minimise errors. The predicted GRFs were validated with actual GRFs through a ten-fold cross-validation method, and the correlation coefficients (R) for the ground forces were 0.918 in the medial–lateral axis, 0.985 in the anterior–posterior axis, and 0.991 in the vertical axis during gait. The ground moments were 0.987 in the sagittal plane, 0.841 in the frontal plane, and 0.868 in the transverse plane during gait. The high correlation coefficients(R) are due to the improvement of the prediction rate in the double support phase. This study also proved the possibility of calculating joint forces and moments based on the GRFs predicted with the proposed new hybrid method. Data generated with the proposed method may thus be used instead of raw GRF data in gait analysis and in calculating joint dynamic data using inverse dynamics.