Perceptual Learning, Long-Range Horizontal Connections And Top-Down Influences In Primary Visual Cortex

The earliest cortical stage of visual processing, the primary visual cortex, has long been seen as a static preprocessor that finds local edges and their orientation like a linear filter bank, and passes this information on to downstream visual areas. This view has been challenged in recent years since the discovery of contextual influences, that is, interactions between the responses of neurons that encode for non-overlapping adjacent areas of visual space, and their anatomical substrate, long-range horizontal connections. These contextual interactions have been shown in awake behaving primates to be modulated depending on the task the animals are performing. A first set of electrophysiological experiments has shown with the help of information theory that when an animal performed one of two tasks on the same visual display, the contextual modulations of the task-relevant parts of the visual display contained more information about the stimulus position than when the same elements were task-irrelevant. A second set of experiments on contour integration was analyzed with ROC analysis to show that an ideal observer could predict the presence of an embedded contour from the spike count of a single neuron on a single trial as well as the animal’s behavioral performance. A final set of experiments showed that prior to learning the same contour integration task, the responses did not contain any information about the stimulus position, that the information in the response increased in parallel with the animals performance during learning, and that the enhanced response after learning disappeared during anesthesia, but is only weakened when performing an irrelevant task in a different part of visual space. Last, a neural network is presented that allows gating of long-range horizontal connections by top-down feedback. The stability and the dynamic behavior of the network have been established with phase-plane analysis. Large-scale simulations have been performed to confirm the stability and show the enhanced contour integration of realistic stimuli as a function of feedback gain. This model has fit quantitatively the electrophysiological experiments of contour integration

A mechanistic model of motion processing in the early visual system

A prerequisite for the perception of motion in primates is the transformation of varying intensities of light on the retina into an estimation of position, direction and speed of coherent objects. The neuro-computational mechanisms relevant for object feature encoding have been thoroughly explored, with many neurally plausible models able to represent static visual scenes. However, motion estimation requires the comparison of successive scenes through time. Precisely how the necessary neural dynamics arise and how other related neural system components interoperate have yet to be shown in a large-scale, biologically realistic simulation. The proposed model simulates a spiking neural network computation for representing object velocities in cortical areas V1 and middle temporal area (MT). The essential neural dynamics, hypothesized to reside in networks of V1 simple cells, are implemented through recurrent population connections that generate oscillating spatiotemporal tunings. These oscillators produce a resonance response when stimuli move in an appropriate manner in their receptive fields. The simulation shows close agreement between the predicted and actual impulse responses from V1 simple cells using an ideal stimulus. By integrating the activities of like V1 simple cells over space, a local measure of visual pattern velocity can be produced. This measure is also the linear weight of an associated velocity in a retinotopic map of optical flow. As a demonstration, the classic motion stimuli of drifting sinusoidal gratings and variably coherent dots are used as test stimuli and optical flow maps are generated. Vector field representations of this structure may serve as inputs for perception and decision making processes in later brain areas

Modelling the human perception of shape-from-shading

Shading conveys information on 3-D shape and the process of recovering this information is called shape-from-shading (SFS). This thesis divides the process of human SFS into two functional sub-units (luminance disambiguation and shape computation) and studies them individually. Based on results of a series of psychophysical experiments it is proposed that the interaction between first- and second-order channels plays an important role in disambiguating luminance. Based on this idea, two versions of a biologically plausible model are developed to explain the human performances observed here and elsewhere. An algorithm sharing the same idea is also developed as a solution to the problem of intrinsic image decomposition in the field of image processing. With regard to the shape computation unit, a link between luminance variations and estimated surface norms is identified by testing participants on simple gratings with several different luminance profiles. This methodology is unconventional but can be justified in the light of past studies of human SFS. Finally a computational algorithm for SFS containing two distinct operating modes is proposed. This algorithm is broadly consistent with the known psychophysics on human SFS

Neural models of inter-cortical networks in the primate visual system for navigation, attention, path perception, and static and kinetic figure-ground perception

Vision provides the primary means by which many animals distinguish foreground objects from their background and coordinate locomotion through complex environments. The present thesis focuses on mechanisms within the visual system that afford figure-ground segregation and self-motion perception. These processes are modeled as emergent outcomes of dynamical interactions among neural populations in several brain areas. This dissertation specifies and simulates how border-ownership signals emerge in cortex, and how the medial superior temporal area (MSTd) represents path of travel and heading, in the presence of independently moving objects (IMOs). Neurons in visual cortex that signal border-ownership, the perception that a border belongs to a figure and not its background, have been identified but the underlying mechanisms have been unclear. A model is presented that demonstrates that inter-areal interactions across model visual areas V1-V2-V4 afford border-ownership signals similar to those reported in electrophysiology for visual displays containing figures defined by luminance contrast. Competition between model neurons with different receptive field sizes is crucial for reconciling the occlusion of one object by another. The model is extended to determine border-ownership when object borders are kinetically-defined, and to detect the location and size of shapes, despite the curvature of their boundary contours. Navigation in the real world requires humans to travel along curved paths. Many perceptual models have been proposed that focus on heading, which specifies the direction of travel along straight paths, but not on path curvature. In primates, MSTd has been implicated in heading perception. A model of V1, medial temporal area (MT), and MSTd is developed herein that demonstrates how MSTd neurons can simultaneously encode path curvature and heading. Human judgments of heading are accurate in rigid environments, but are biased in the presence of IMOs. The model presented here explains the bias through recurrent connectivity in MSTd and avoids the use of differential motion detectors which, although used in existing models to discount the motion of an IMO relative to its background, is not biologically plausible. Reported modulation of the MSTd population due to attention is explained through competitive dynamics between subpopulations responding to bottom-up and top- down signals

Cognitive computing: algorithm design in the intersection of cognitive science and emerging computer architectures

For the first time in decades computers are evolving into a fundamentally new class of machine. Transistors are still getting smaller, more economical, and more power-efficient, but operating frequencies leveled off in the mid-2000's. Today, improving performance requires placing a larger number of slower processing cores on each of many chips. Software written for such machines must scale out over many cores rather than scaling up with a faster single core. Biological computation is an extreme manifestation of such a many-slow-core architecture and therefore offers a potential source of ideas for leveraging new hardware. This dissertation addresses several problems in the intersection of emerging computer architectures and biological computation, termed Cognitive Computing: What mechanisms are necessary to maintain stable representations in a large distributed learning system? How should complex biologically-inspired algorithms be tested? How do visual sensing limitations like occlusion influence performance of classification algorithms? Neurons have a limited dynamic output range, but must process real-world signals over a wide dynamic range without saturating or succumbing to endogenous noise. Many existing neural network models leverage spatial competition to address this issue, but require hand-tuning of several parameters for a specific, fixed distribution of inputs. Integrating spatial competition with a stabilizing learning process produces a neural network model capable of autonomously adapting to a non-stationary distribution of inputs. Human-engineered complex systems typically include a number of architectural features to curtail complexity and simplify testing. Biological systems do not obey these constraints. Biologically-inspired algorithms are thus dramatically more difficult to engineer. Augmenting standard tools from the software engineering community with features targeted towards biologically-inspired systems is an effective mitigation. Natural visual environments contain objects that are occluded by other objects. Such occlusions are under-represented in the standard benchmark datasets for testing classification algorithms. This bias masks the negative effect of occlusion on performance. Correcting the bias with a new dataset demonstrates that occlusion is a dominant variable in classification performance. Modifying a state-of-the-art algorithm with mechanisms for occlusion resistance doubles classification performance in high-occlusion cases without penalty for unoccluded objects

A Novel Circuit Model of Contextual Modulation and Normalization in Primary Visual Cortex

The response of a neuron encoding information about a sensory stimulus is influenced by the context in which that information is presented. In the primary visual cortex (area V1), neurons respond selectively to stimuli presented to a relatively constrained region of visual space known as the classical receptive field (CRF). These responses are influenced by stimuli in a much larger region of visual space known as the extra-classical receptive field (eCRF). In that they cannot directly evoke a response from the neuron, surround stimuli in the eCRF provide the context for the input to the CRF. Though the past few decades of research have revealed many details of the complex and nuanced interactions between the CRF and eCRF, the circuit mechanisms underlying these interactions are still unknown. In this thesis, we present a simple, novel cortical circuit model that can account for a surprisingly diverse array of eCRF properties. This model relies on extensive recurrent interactions between excitatory and inhibitory neurons, connectivity that is strongest between neurons with similar stimu- lus preferences, and an expansive input-output neuronal nonlinearity. There is substantial evidence for all of these features in V1. Through analytical and computational modeling techniques, we demonstrate how and why this circuit is able to account for such a comprehensive array of contextual modulations. In a linear network model, we demonstrate how surround suppression of both excitatory and inhibitory neurons is achieved through the selective amplification of spatially-periodic pat- terns of activity. This amplification relies on the network operating as an inhibition-stabilized network, a dynamic regime previously shown to account for the paradoxical decrease in in- hibition during surround suppression (Ozeki et al., 2009). With the addition of nonlinearity, effective connectivity strength scales with firing rate, and the network can transition be- tween different dynamic regimes as a function of input strength. By moving into and out of the inhibition-stabilized state, the model can reproduce a number of contrast-dependent changes in the eCRF without requiring any asymmetry in the intrinsic contrast-response properties of the cells. This same model also provides a biologically plausible mechanism for cortical normalization, an operation that has been shown to be ubiquitous in V1. Through a winner-take-all population response, we demonstrate how this network undergoes a strong reduction in trial-to-trial variability at stimulus onset. We also propose a novel mechanism for attentional modulation in visual cortex. We then go on to test several of the critical pre- dictions of the model using single unit electrophysiology. From these experiments, we find ample evidence for the spatially-periodic patterns of activity predicted by the model. Lastly, we show how this same circuit motif may underlie behavior in a higher cortical region, the lateral intraparietal area

Representation and learning in cerebellum-like structures

Animals use their nervous system to translate signals from their sensory environment into appropriate behavioral responses. In some cases, these responses are hard-wired through genetic sculpting of neural circuits, such that certain stimuli drive innate behavioral responses in the absence of prior experience [Ewert, Burghagen, and Schurg Pfeiffer 1983; Yilmaz and Meister 2013; Wu et al 2014]. But most often, responses to stimuli are modified over the course of an organism's lifetime via associative learning, in which past experience is used to adaptively modify the neural circuits controlling behavior. The remarkable regularity of cerebellar circuitry made it an early target of experiments seeking a link between neural circuit structure and computational function (Eccles, Ito, and Szentgothai, 1967). These efforts led to a first generation of models describing cerebellar cortex as a device for associative learning, remarkable for their focus on linking each cell type of cerebellar cortex to a computational aspect of associative memory formation and adaptive control ([Marr 1969; Albus 1971; Ito 1972). In subsequent decades, specialized neural architecture resembling that of the cerebellum has been identified in several other brain regions, including the dorsal cochlear nucleus of most mammals (Oertel and Young, 2004), the mushroom body of the insect olfactory system (Farris, 2011), and a region evolutionarily and developmentally related to the cerebellum in the brains of weakly electric fish, the electrosensory lobe (Bell, Han, and Sawtell, 2008). This has raised the hope that a similar computational mechanism is at work in these structures. It is not easy to find behavioral paradigms that isolate learning in the cerebellum, and a complete mechanistic account of learning during commonly studied behaviors has remained elusive. In this thesis, I analyze two cerebellum like structures, the electrosensory lobe of the mormyrid fish and the mushroom body of the fly olfactory system, in which mapping out associative learning is more tractable, due to the availability of well controlled learning paradigms and the development of powerful biochemical and genetic techniques. With the help of my experimental collaborators, I constructed computational models of the electrosensory lobe and mushroom body from electrophysiological and anatomical data, and studied the process of associative learning in these models. In both systems, an initial sensory representation is first projected up into a high dimensional space, and then read out via convergent input onto individual neurons. Learning adjusts the input to readout neurons, causing changes in their responses to future stimuli that alters their drive to downstream nuclei. Two details shape how each circuit handles associative learning: the way in which sensory inputs are represented, and the mechanism of learning. Together, these two pieces determine what transformations each circuit is able to learn and how it generalizes after learning. In the four chapters of this thesis I present four related projects dealing with sensory representation and learning in cerebellum-like structures. The first chapter has previously been published as a paper and describes a model for cancellation of self generated sensory input in the passive electrosensory system of the mormyrid fish. In the second chapter, I adapt this model to a more high dimensional cancellation problem in the fish's active electrosensory system, which deals with the effects of the fish's body on the electric fields it generates. In the next two chapters, I construct a network model of odor representation in fly olfactory system, terminating at the mushroom body. Finally, I use this model in conjunction with recent experimental findings on the output of the mushroom body, to build a model of associative odor learning in the fly

Approximate Spatial Layout Processing in the Visual System: Modeling Texture-Based Segmentation and Shape Estimation

Moving through the environment, grasping objects, orienting oneself, and countless other tasks all require information about spatial organization. This in turn requires determining where surfaces, objects and other elements of a scene are located and how they are arranged. Humans and other animals can extract spatial organization from vision rapidly and automatically. To better understand this capability, it would be useful to know how the visual system can make an initial estimate of the spatial layout. Without time or opportunity for a more careful analysis, a rough estimate may be all that the system can extract. Nevertheless, rough spatial information may be sufficient for many purposes, even if it is devoid of details that are important for tasks such as object recognition. The human visual system uses many sources of information for estimating layout. Here I focus on one source in particular: visual texture. I present a biologically reasonable, computational model of how the system can exploit patterns of texture for performing two basic tasks in spatial layout processing: locating possible surfaces in the visual input, and estimating their approximate shapes. Separately, these two tasks have been studied extensively, but they have not previously been examined together in the context of a model grounded in neurophysiology and psychophysics. I show that by integrating segmentation and shape estimation, a system can share information between these processes, allowing the processes to constrain and inform each other as well as save on computations. The model developed here begins with the responses of simulated complex cells of the primary visual cortex, and combines a weak membrane/functional minimization approach to segmentation with a shape estimation method based on tracking changes in the average dominant spatial frequencies across a surface. It includes mechanisms for detecting untextured areas and flat areas in an input image. In support of the model, I present a software simulation that can perform texture-based segmentation and shape estimation on images containing multiple, curved, textured surfaces.Ph.D.Applied SciencesBiological SciencesCognitive psychologyComputer scienceNeurosciencesPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/131446/2/9909908.pd

The role of posterior parietal cortex in multisensory decision-making

Making a decision consists of committing to a plan of action, usually selected between two or more competing alternatives. Numerous fields have studied the processes involved in decision-making including psychology, economics, philosophy and statistics, to name only a few. In neuroscience the study of decision-making has been extremely fruitful in recent years and has focused on two main aspects: (1) perceptual decisionmaking, interested in understanding how external information is perceived by the sensory systems and used to make decisions; (2) value based decision-making, interested in the mechanisms that cause and result from the association of subjective values to the possible outcomes of a decision.(...