A multistage neural model is proposed for an auditory scene analysis task---segregating speech from interfering sound sources. The core of the model is a two-layer oscillator network that performs stream segregation on the basis of oscillatory correlation. In the oscillatory correlation framework, a stream is represented by a population of synchronized relaxation oscillators, each of which corresponds to an auditory feature, and different streams are represented by desynchronized oscillator populations. Lateral connections between oscillators encode harmonicity, and proximity in frequency and time. Prior to the oscillator network are a model of the auditory periphery and a stage in which mid-level auditory representations are formed. The model has been systematically evaluated using a corpus of voiced speech mixed with interfering sounds, and produces improvements in terms of signal-to-noise ratio for every mixture. The performance of our model is compared with other studies on computa...

Two types of mechanisms have dominated theoretical accounts of efficient visual search. First are bottom-up processes related to the hypothesized characteristics of retinotopic feature maps. Second are top-down mechanisms related to feature selection. Little effort has been made to understand visual search in terms of perceptual grouping despite its acknowledged importance in general visual perception. To examine the possible role of perceptual grouping we employ a new search paradigm whereby a target is defined only in a context-dependent manner by multiple conjunctions of feature dimensions. Because targets in a multi-conjunction task cannot be distinguished from distractors either by bottom-up guidance or top-down guidance, current theories of visual search predict inefficient search. While inefficient search does occur for the multiple conjunctions of orientation with color or luminance, we found efficient search for multiple conjunctions of luminance with size, shape, and topological properties. We also show that repeated presentations of either targets or a set of distractors result in much faster performance. Our results suggest that perceptual organization can play a decisive role in visual search, and theories of visual attention need to take this into account. Furthermore, multiconjunction search may provide a new vehicle for investigating perceptual grouping and scene analysis.

Zero--lag synchrony arises between two points on the cerebral cortex when these receive concurrent independent inputs and has generally been ascribed to nonlinear mechanisms. We report results obtained by Principal Component Analysis (PCA) applied to simulations of cerebral cortex which exhibit zero--lag synchrony and realistic spectral content, and show that synchrony can arise by distinct and separable linear and nonlinear mechanisms. For lower levels of cortical activation synchrony between the sites of input can be accounted for by the eigenmodes associated with the wave activity generated by the inputs. The first spatial eigenmode arises from even 2 Clare L. Chapman et al. components in the independent input signals and the second spatial eigenmode arises from odd components in the inputs. Together these account for most of the signal variance, while the predominance of the first mode over the second within the near--field of the inputs accounts for zero--lag synchrony in the ne...

Abstract—Selective visual attention in a natural environment can be seen as the interaction between the external visual stimulus and task specific knowledge of the required behavior. This interaction between the bottom-up stimulus and the top-down, task-related knowledge is crucial for what is selected in the space and time within the scene. In this paper, we propose a computational model for selective attention for a visual search task. We go beyond simple saliency-based attention models to model selective attention guided by top-down visual cues, which are dynamically integrated with the bottom-up information. In this way, selection of a location is accomplished by interaction between bottom-up and top-down information. First, the general structure of our model is briefly introduced and followed by a description of the top-down processing of task-relevant cues. This is then followed by a description of the processing of the external images to give three feature maps that are combined to give an overall bottom-up map. Second, the development of the formalism for our novel interactive spiking neural network (ISNN) is given, with the interactive activation rule that calculates the integration map. The learning rule for both bottom-up and top-down weight parameters are given, together with some further analysis of the properties of the resulting ISNN. Third, the model is applied to a face detection task to search for the location of a specific face that is cued. The results show that the trajectories of attention are dramatically changed by interaction of information and variations of cues, giving an appropriate, task-relevant search pattern. Finally, we discuss ways in which these results can be seen as compatible with existing psychological evidence. Index Terms—Attention, bottom-up map, computer vision, cueguided search, top-down map.

This paper studies new spike-based models for winner-take-all computation and coincidence detection. In both cases, very fast convergence is achieved independent of initial conditions, and network complexity is linear in the number of inputs. Fully distributed versions can be derived by using groups of interneurons connected through electrical synapses. 1

An integrated model of conscious image processing in human cortex is proposed based on the Holonomic Brain Theory by Karl Pribram and related models. After optimal delineation of the neural and quantum ingredients, the model combines the predominantly (sub)neuronal image processing and the essentially quantum-based repetitive act of becoming-conscious of the resulting phenomenal image. The model optimally incorporates contemporary limited knowledge starting from a systematic search for fit between existing computational models, and between available experimental data, and between data and models. Since we are not yet able to tackle qualitative conscious experience directly, processes for making an image (or result of image processing, respectively) conscious are discussed. A quantum implementation of holography-like processing of images in the striate cortex (V1) is proposed using a computational model called quantum associative network. The input to the quantum net could be the Gabor wavelets, together with their coefficients, which are infomax-constrained spectral and sparse neural codes produced in the convolutional

Abstract—Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. The proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real images and the simulation results show its effectiveness. I.

Abstract—We present an automatic method for road extraction from satellite imagery. The core of the proposed method is Locally Excitatory Globally Inhibitory Oscillator Networks (LEGION). We decompose the road extraction task into three stages. The first stage is image segmentation by LEGION. In the second stage, we compute the medial axis of each segment and select the segments with narrow widths. The third is the road grouping stage. With the medial axes, alignment-dependent connections between medial axis points are established and LEGION is utilized to group the well-aligned medial axes, which represent extracted road segments. Due to the selective gating mechanism of LEGION, different roads in an image are grouped separately. Experimental results on synthetic and real images show the effectiveness of this method. I.

The winner-take-all (WTA) computation in networks of recurrently connected neurons is an important decision element of many models of cortical processing. However, analytical studies of the WTA performance in recurrent networks have generally addressed rate-based models. Very few have addressed networks of spiking neurons, which are relevant for understanding the biological networks themselves and also for the development of neuromorphic electronic neurons that commmunicate by action potential like address-events. Here, we make steps in that direction by using a simplified Markov model of the spiking network to examine analytically the ability of a spike-based WTA network to discriminate the statistics of inputs ranging from stationary regular to nonstationary Poisson events. Our work extends previous theoretical results showing that a WTA recurrent network receiving regular spike inputs can select the correct winner within one interspike interval. We show first for the case of spike rate inputs that input discrimination and the effects of selfexcitation and inhibition on this discrimination are consistent with results obtained from the standard rate-based WTA models. We also extend this discrimination analysis of spiking WTAs to nonstationary inputs with time-varying spike rates resembling statistics of real-world sensory stimuli. We conclude that spiking WTAs are consistent with their continuous counterparts for steady-state inputs, but they also exhibit high discrimination performance with nonstationary inputs.

Visual input is processed in parallel in the early stages of the visual system. Later, object recognition processes are also massively parallel, matching a visual object with a vast array of stored representation. A tight bottleneck in processing lies between these stages. It permits only one or a few visual objects at any one time to be submitted for recognition. That bottleneck limits performance on visual search tasks when an observer looks for one object in a field containing distracting objects. Guided Search is a model of the workings of that bottleneck. It proposes that a limited set of attributes, derived from early vision, can be used to guide the selection of visual objects. The bottleneck and recognition processes are modeled using an asynchronous version of a diffusion process. The current version (Guided Search 4.0) captures a wide range of empirical findings. Page 2