Unsupervised Visual Feature Learning with Spike-timing-dependent Plasticity: How Far are we from Traditional Feature Learning Approaches?

Spiking neural networks (SNNs) equipped with latency coding and spike-timing dependent plasticity rules offer an alternative to solve the data and energy bottlenecks of standard computer vision approaches: they can learn visual features without supervision and can be implemented by ultra-low power hardware architectures. However, their performance in image classification has never been evaluated on recent image datasets. In this paper, we compare SNNs to auto-encoders on three visual recognition datasets, and extend the use of SNNs to color images. The analysis of the results helps us identify some bottlenecks of SNNs: the limits of on-center/off-center coding, especially for color images, and the ineffectiveness of current inhibition mechanisms. These issues should be addressed to build effective SNNs for image recognition

A Neural Model of How the Cortical Subplate Coordinates the Laminar Development of Orientation and Ocular Dominance Maps

Air Force Office of Scientific Research (F49620-98-1-0108, F49620-0 1-1-0397); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IIS-97-20333); Office of Naval Research (N00014-01-1-0624

Evidence for Perceptual “Trapping” and Adaptation in Multistable Binocular Rivalry

AbstractWhen a different pattern is presented to each eye, the perceived image spontaneously alternates between the two patterns (binocular rivalry); the dynamics of these bistable alternations are known to be stochastic. Examining multistable binocular rivalry (involving four dominant percepts), we demonstrated path dependence and on-line adaptation, which were equivalent whether perceived patterns were formed by single-eye dominance or by mixed-eye dominance. The spontaneous perceptual transitions tended to get trapped within a pair of related global patterns (e.g., opponent shapes and symmetric patterns), and during such trapping, the probability of returning to the repeatedly experienced patterns gradually decreased (postselection pattern adaptation). These results suggest that the structure of global shape coding and its adaptation play a critical role in directing spontaneous alternations of visual awareness in perceptual multistability

Modeling the self-organization of color selectivity in the visual cortex

How does the visual cortex represent and process color? Experimental evidence from macaque monkey suggests that cells selective for color are organized into small, spatially separated blobs in V1, and stripes in V2. This organization is strikingly different from that of orientation and ocular dominance maps, which consist of large, spatially contiguous patterns. In this dissertation, a self-organizing model of the early visual cortex is constructed using natural color image input. The modeled V1 develops realistic color-selective receptive fields, ocular dominance stripes, orientation maps, and color-selective regions, while the modeled V2 also creates realistic colorselective and orientation-selective neurons. V1 color-selective regions are generally located in the center of ocular dominance stripes as they are in biological maps; the model predicts that color-selective regions become more widespread in both cortical regions when the amount of color in the training images is increased. The model also predicts that in V1 there are three types of color-selective regions (red-selective, greenselective, and blue-selective), and that a unique cortical activation pattern exists for each of the HSV colors. In both V1 and V2, when regions of different color-selectivity are located nearby, bands of color form with gradually changing color preferences. The model also develops lateral connections between cells that are selective for similar orientations, matching previous experimental results, and predicts that cells selective for color primarily connect to other cells with similar chromatic preferences. Thus the model replicates the known data on the organization of color preferences in V1 and V2, provides a detailed explanation for how this structure develops and functions, and leads to concrete predictions to test in future experiments.Computer Science

Mechanisms of spatiotemporal selectivity in cortical area MT

Cortical sensory neurons are characterized by selectivity to stimulation. This selectivity was originally viewed as a part of the fundamental “receptive field” characteristic of neurons. This view was later challenged by evidence that receptive fields are modulated by stimuli outside of the classical receptive field. Here we show that even this modified view of selectivity needs revision. We measured spatial frequency selectivity of neurons in cortical area MT of alert monkeys and found that their selectivity strongly depends on luminance contrast, shifting to higher spatial frequencies as contrast increases. The changes of preferred spatial frequency are large at low temporal frequency and they decrease monotonically as temporal frequency increases. That is, even interactions among basic stimulus dimensions of luminance contrast, spatial frequency and temporal frequency strongly influence neuronal selectivity. This dynamic nature of neuronal selectivity is inconsistent with the notion of stimulus preference as a stable characteristic of cortical neurons

The representation of auditory space in the auditory cortex of anesthetized and awake mice

The ability to localize sounds is of profound importance for animals, as it enables them to detect prey and predators. In the horizontal plane, sound localization is achieved by means of binaural cues, which are processed and interpreted by the ascending auditory pathway. The auditory cortex (AC), as its primary cortical relay station, has traditionally been thought to broadly and stationary represent the contralateral hemifield of auditory space. Because prior research on space representation in the mammalian AC heavily relied on anesthetized preparations, the manner in which anesthesia influences this representation has remained elusive. Performing chronic two-photon-calcium imaging in the AC of awake and anesthetized mice, I characterized the effects of anesthesia on auditory space representation. First, anesthesia was found to impair the spatial sensitivity of neurons. Second, anesthesia constantly suppressed the representation of frontal locations biasing spatial tuning to the contralateral side. In both conditions (awake and anesthetized), the population of neurons endured a stable representation of auditory space, while single-cell spatial tuning was found to be extremely dynamic. Importantly, under both conditions no evidence for a topographical map of auditory space was found. This study is the first to chronically probe spatial tuning in the AC and likewise the first to directly assess effects of anesthesia on single-cell spatial tuning and the population code emphasizing the need for a shift towards awake preparations

Photographic tone reproduction for digital images

technical reportA classic photographic task is the mapping of the potentially high dynamic range of real world luminances to the low dynamic range of the photographic print. This tone reproduction problem is also faced by computer graphics practitioners who must map digital images to a low dynamic range print or screen. The work presented in this paper leverages the time-tested techniques of photographic practice to develop a new tone reproduction operator. In particular, we use and extend the techniques developed by Ansel Adams to deal with digital images. The resulting algorithm is simple and is shown to produce good results for the wide variety of images that we have tested