Hui Wei

A group-decision making model of orientation detection

The feedforward model proposed by Hubel and Wiesel partially explained orientation selectivity in simple cells. This classical hypothesis attributed orientation preference to idealized alignment of geniculate cell receptive fields. Many scholars have been either revising this model or putting forward new theories to account for more related phenomenon such as contrast invariant tuning. None of the previous neural models is complete in implementation details or involves strict computational strategies. This paper mathematically studied a detailed but vital question which has long been neglected: the possibility of massive variable-sized, unaligned geniculate cell receptive fields producing the orientation selectivity of a simple cell. The response curve of each afferent neuron is fully utilized to obtain a local constraint and a group-decision making approach is then applied to solve the constraint satisfaction problem. Our new model does not achieve just consistent experimental results with physiological data, but consistent interpretations of several illusions with observers perceptions. The current work, which supplemented the previous models with necessary computational details, is based on ensemble coding in essence. This underlying mechanism helps to understand how visual information is processed in from the retina to the cortex.

Prediction of rat behavior outcomes in memory tasks using functional connections among neurons.

Background Analyzing the neuronal organizational structures and studying the changes in the behavior of the organism is key to understanding cognitive functions of the brain. Although some studies have indicated that spatiotemporal firing patterns of neuronal populations have a certain relationship with the behavioral responses, the issues of whether there are any relationships between the functional networks comprised of these cortical neurons and behavioral tasks and whether it is possible to take advantage of these networks to predict correct and incorrect outcomes of single trials of animals are still unresolved. Methodology/Principal Findings This paper presents a new method of analyzing the structures of whole-recorded neuronal functional networks (WNFNs) and local neuronal circuit groups (LNCGs). The activity of these neurons was recorded in several rats. The rats performed two different behavioral tasks, the Y-maze task and the U-maze task. Using the results of the assessment of the WNFNs and LNCGs, this paper describes a realization procedure for predicting the behavioral outcomes of single trials. The methodology consists of four main parts: construction of WNFNs from recorded neuronal spike trains, partitioning the WNFNs into the optimal LNCGs using social community analysis, unsupervised clustering of all trials from each dataset into two different clusters, and predicting the behavioral outcomes of single trials. The results show that WNFNs and LNCGs correlate with the behavior of the animal. The U-maze datasets show higher accuracy for unsupervised clustering results than those from the Y-maze task, and these datasets can be used to predict behavioral responses effectively. Conclusions/Significance The results of the present study suggest that a methodology proposed in this paper is suitable for analysis of the characteristics of neuronal functional networks and the prediction of rat behavior. These types of structures in cortical ensemble activity may be critical to information representation during the execution of behavior.

A decision-making model based on a spiking neural circuit and synaptic plasticity

To adapt to the environment and survive, most animals can control their behaviors by making decisions. The process of decision-making and responding according to cues in the environment is stable, sustainable, and learnable. Understanding how behaviors are regulated by neural circuits and the encoding and decoding mechanisms from stimuli to responses are important goals in neuroscience. From results observed in Drosophila experiments, the underlying decision-making process is discussed, and a neural circuit that implements a two-choice decision-making model is proposed to explain and reproduce the observations. Compared with previous two-choice decision making models, our model uses synaptic plasticity to explain changes in decision output given the same environment. Moreover, biological meanings of parameters of our decision-making model are discussed. In this paper, we explain at the micro-level (i.e., neurons and synapses) how observable decision-making behavior at the macro-level is acquired and achieved.

V4 neural network model for visual saliency and discriminative local representation of shapes

Visual area V4 lies in the middle of the ventral visual pathway in the primate brain. It is an intermediate stage in the visual processing for object discrimination. It plays an important role in the neural mechanism of visual attention and shape recognition. V4 neurons exhibit selectivity for salient features of contour conformation. In this paper, we propose a novel model of V4 neurons based on a multilayer neural network inspired by recent studies on V4. Its low-level layers consist of computational units simulating simple cells and complex cells in the primary visual cortex. These layers extract preliminary visual features including edges and orientations. The V4 computational units calculate the entropy of the extracted features as a measure of visual saliency. The salient features are then selected and encoded with a layer of Restricted Boltzmann Machine to generate an intermediate representation of object shapes. The model was evaluated in shape distinction, handwritten digits classification, feature detection, and feature matching experiments. The results demonstrate that this model generates discriminative local representation of object shapes. It provides clues to understand the high level representation of visual stimuli in the brain.

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 new model to simulate the formation of orientation columns map in visual cortex

The new model presented in the paper is used to simulate the development process of the orientation selectivity in primary visual cortex. The model combines mechanisms such as receptive field control, lateral connections, function columns into a network and then trained with random samples, can be regarded as a first stone of other feature maps. The model attempts to verify the basic features of the orientation map such as singularities, continuity and diversity. Meanwhile the model can be expanded with increasing columns or hyper-columns easily in order to process lager scope of stimulus. Another point is fault-tolerance, if some column is not successfully trained, the map can still perform well. After fast training process, the image of finished orientation map displaying with topology function is similar with the biological cortex orientation map, and the formed map can be used to extra the orientation information of the input quickly for the further visual process.

A neural circuit model for nCRF's dynamic adjustment and its application on image representation

According to Biology there is a large disinhibitory area outside the classical receptive field (CRF), which is called as non-classical receptive field (nCRF). Combining CRF with nCRF could increase the sparseness, reliability and precision of the neuronal responses. This paper is aimed at the realization of the neural circuit and the dynamic adjustment mechanism of the receptive field (RF) with respect to nCRF. On the basis of anatomical and electrophysiological evidence, we constructed a neural computational model, which can represent natural images faithfully, simply and rapidly. And the representation can significantly improve the subsequent operation efficiency such as segmentation or integration. This study is of particular significance in the development of efficient image processing algorithms based on neurobiological mechanisms. The RF mechanism of ganglion cell (GC) is the result of a long term of evolution and optimization of self-adaptability and high representation efficiency. So its performance evaluation in natural image processing is worthy of further study.

A plausible neural circuit for decision making and its formation based on reinforcement learning

A human’s, or lower insects’, behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit’s constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit’s output layer, which generates a stable spike firing rate to encode flight commands, controls the insect’s angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit’s information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee’s behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit’s generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control. Finally, this study also helps establish a transitional bridge between the microscopic activity of the nervous system and macroscopic animal behavior.

A neurocomputing model for ganglion cell's color opponency mechanism and its application in image analysis

The vision system of primates could process colorful scenes very efficiently. This is because, in biological retina, there are three types of cone cells and several types of ganglion cells that possess highly complicated receptive fields. The central and the surrounding areas of a receptive field are usually composed of different types of cones. Typically, they form two classes, namely the red-green opponency and the blue-yellow opponency. In order to develop a new representation schema for colorful images, we simulated some physiological mechanisms in retina, such as the opponent color theory. Based on anatomical and electrophysiological findings of ganglion cells, we proposed a bio-inspired color processing method. We designed a neural network simulating retinal ganglion cells (GCs) and their classical receptive fields (CRF), and also raised a dynamic procedure to control receptive fields self-adjustment according to the characteristics of an image. A great number of experiments were conducted on natural images. The results showed that this new method could reserve crucial structural information of an image and suppress trivial information at the same time. Depending on these new representations, some upcoming processing, such as image segmentation, could be improved significantly. Image segmentation is very critical to ultimate image understanding. However, actual image stimuli are a little bit far from biological studies. Our work integrated them together and explained how the physiological opponent-color theory could facilitate image processing in real applications.

Discovering the multi-neuronal firing patterns based on a new binless spike trains measure

In this paper, we proposed a method which presented a new definition of different multi-step interval ISI-distance distribution of single neuronal spike trains and formed a new feature vector to represent the original spike trains. It is a binless spike trains measure method. We used spectral clustering algorithm on new multi-dimensional feature vectors to detect the multiple neuronal firing patterns. We tested this method on standard data set in machine learning, neuronal surrogate data set and in vivo multi-electrode recordings respectively. Results shown that the method proposed in this paper can effectively improve the clustering accuracy in standard data set and detect the firing patterns in neuronal spike trains.