Comparison of recordings from microelectrode arrays and single electrodes in the visual cortex.
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Action Potentials↗
Biomedical subjects
Tai Sing Lee
Publications and source records attributed to Tai Sing Lee.
Preference of sensory neural coding for 1/f signals.
We investigated the influences of different types of temporal correlations in the input signal on the functions and coding properties of neurons in the primary visual cortex (V1). We found that the temporal transfer functions of V1 neurons exhibit higher gain, and the spike responses exhibit higher coding efficiency and information transmission rates, for the 1/f (natural long-term correlation) signals than for 1/f(0) (no correlation) and 1/f(2) (stronger long-term correlation) signals. These results suggest that the intermediate long-term correlation ubiquitous to natural signals may play an important role in shaping and optimizing the machinery of neurons in their adaptation to the natural environment.
Action Potentials↗
The role of spiking nonlinearity in contrast gain control and information transmission.
Threshold and saturation are two nonlinear features common to almost all spiking neurons. How these nonlinearities affect the performance gain of the transfer function and coding properties of the neurons has attracted much attention. Here, we deduce basic analytical relationships among these nonlinearities (threshold and saturation), performance gain and information transmission in neurons. We found that performance gain and information transmission can be maximized by input signals with optimal variance. The threshold and saturation inside the model determines the gain tuning property and maximum coding capacity. This framework provides an understanding of some basic design principles underlying information processing systems that can be adjusted to match the statistics of signals in the environment. This study also isolates the exact contributions of the nonlinearities on the contrast adaptation phenomena observed in real visual neurons.
Contrast Sensitivity↗
Optimal synchrony state for maximal information transmission.
This work investigated the roles of spontaneous synchronized rhythmic oscillations in information processing in a globally coupled neural network. We found that in the encoding of input aperiodic signals, there exists a range of optimal synchronous states in the network, where the information transmission rate and coding efficiency of the network are maximized. Our results indicate that it is not the weaker or stronger but an appropriate synchronous state may be of more functional significance in sensory encoding.
Cortical Synchronization↗
The role of early visual cortex in visual integration: a neural model of recurrent interaction.
This paper presents a model on the potential functional roles of the early visual cortex in the primate visual system. Our hypothesis is that early visual areas, such as V1, are important for continual interaction among various higher order visual areas during visual processing. The interaction is mediated by recurrent connections between higher order visual areas and V1, manifested in the long-latency context-sensitive activities often observed in neurophysiological experiments, and is responsible for the re-integration of information analysed by the higher visual areas. Specifically, we considered the case of integrating 'what' and 'where' information from the ventral and dorsal streams. We found that such a cortical architecture provides simple solutions and fresh insights into the problems of attentional routing and visual search. The computational viability of this architecture was tested by simulating a large-scale neural dynamical network.
Attention↗
Dynamical mechanisms underlying contrast gain control in single neurons.
Recent neurophysiological experiments have revealed that the linear and nonlinear kernels of the transfer function in sensory neurons are not static. Rather, they are adaptive to the contrast or the variance of time-varying input stimuli, exhibiting a contrast gain control phenomenon. We investigated the underlying biophysical causes of this phenomenon by simulating and analyzing the leaky integrate-and-fire and the Hodgkin-Huxley neuronal models. Our findings indicate that contrast gain control may result from the synergistic cooperation of the nonlinear dynamics of spike generation and the statistical properties of the stimuli. The resulting statistics-dependent stimulus threshold is shown to be a key factor underlying the adaptation of frequency tuning and amplitude gain of a neuron's transfer function in different stimulus environments.
Animals↗
Statistical correlations between two-dimensional images and three-dimensional structures in natural scenes.
In spite of the recent surge in the popularity of statistical approaches to vision, the joint statistics of coregistered range and light-intensity images have gone relatively unexplored. We investigate statistical correlations between images and the surface shapes that produced them. We determine which linear properties of range images can be best predicted from simple computations on intensity information, and we determine those properties of intensity images that best predict range information. We find that significant (up to p = 0.45) and potentially exploitable correlations exist between linear properties of range and intensity images, and we explore the structure of these correlations.
Journal Article↗
Hierarchical Bayesian inference in the visual cortex.
Traditional views of visual processing suggest that early visual neurons in areas V1 and V2 are static spatiotemporal filters that extract local features from a visual scene. The extracted information is then channeled through a feedforward chain of modules in successively higher visual areas for further analysis. Recent electrophysiological recordings from early visual neurons in awake behaving monkeys reveal that there are many levels of complexity in the information processing of the early visual cortex, as seen in the long-latency responses of its neurons. These new findings suggest that activity in the early visual cortex is tightly coupled and highly interactive with the rest of the visual system. They lead us to propose a new theoretical setting based on the mathematical framework of hierarchical Bayesian inference for reasoning about the visual system. In this framework, the recurrent feedforward/feedback loops in the cortex serve to integrate top-down contextual priors and bottom-up observations so as to implement concurrent probabilistic inference along the visual hierarchy. We suggest that the algorithms of particle filtering and Bayesian-belief propagation might model these interactive cortical computations. We review some recent neurophysiological evidences that support the plausibility of these ideas.
Animals↗
The nature of illusory contour computation.
Neural correlates of illusory contour perception have been found in both the early and the higher visual areas. But the locus and the mechanism for its computation remain elusive. Psychophysical evidence provided in this issue of Neuron shows that perceptual contour completion is likely done in the early visual cortex in a cascade manner using horizontal connections.
Animals↗
Top-down influence in early visual processing: a Bayesian perspective.
Traditional views of visual processing suggest that early visual neurons are static spatiotemporal filters that extract local features by feedforward computation. The extracted information is then fed forward through a chain of modules to successively higher visual areas for further analysis. Recording from early visual neurons in awake behaving monkeys, we revealed there are many levels of complexity in the information processing of the early visual cortex. We found that the early visual neurons not only are sensitive to features within their receptive fields (RFs) but also to the global context of a visual scene, the behavioral relevance of the stimuli and the experience of the animals. These findings suggest that the early visual cortex (V1 and V2) is tightly coupled to and highly interactive with the rest of the visual system. The top-down interaction, mediated by recurrent feedback connections, introduces contextual information to influence the perceptual inference in the early visual cortex.
Algorithms↗
Neural activity in early visual cortex reflects behavioral experience and higher-order perceptual saliency.
We report here that shape-from-shading stimuli evoked a long-latency contextual pop-out response in V1 and V2 neurons of macaque monkeys, particularly after the monkeys had used the stimuli in a behavioral task. The magnitudes of the pop-out responses were correlated to the monkeys' behavioral performance, suggesting that these signals are neural correlates of perceptual pop-out saliency. The signals changed with the animal's behavioral adaptation to stimulus contingencies, indicating that perceptual saliency is also a function of experience and behavioral relevance. The evidence that higher-order stimulus attributes and task experience can influence early visual processing supports the notion that perceptual computation is an interactive and plastic process involving multiple cortical areas.
Adaptation, Psychological↗
Computations in the early visual cortex.
This paper reviews some of the recent neurophysiological studies that explore the variety of visual computations in the early visual cortex in relation to geometric inference, i.e. the inference of contours, surfaces and shapes. It attempts to draw connections between ideas from computational vision and findings from awake primate electrophysiology. In the classical feed-forward, modular view of visual processing, the early visual areas (LGN, V1 and V2) are modules that serve to extract local features, while higher extrastriate areas are responsible for shape inference and invariant object recognition. However, recent findings in primate early visual systems reveal that the computations in the early visual cortex are rather complex and dynamic, as well as interactive and plastic, subject to influence from global context, higher order perceptual inference, task requirement and behavioral experience. The evidence argues that the early visual cortex does not merely participate in the first stage of visual processing, but is involved in many levels of visual computation.
Animals↗