Modeling the formation process of grouping stimuli sets through cortical columns and microcircuits to feature neurons

A computational model of a self-structuring neuronal net is presented in which repetitively applied pattern sets induce the formation of cortical columns and microcircuits which decode distinct patterns after a learning phase. In a case study, it is demonstrated how specific neurons in a feature classifier layer become orientation selective if they receive bar patterns of different slopes from an input layer. The input layer is mapped and intertwined by self-evolving neuronal microcircuits to the feature classifier layer. In this topical overview, several models are discussed which indicate that the net formation converges in its functionality to a mathematical transform which maps the input pattern space to a feature representing output space. The self-learning of the mathematical transform is discussed and its implications are interpreted. Model assumptions are deduced which serve as a guide to apply model derived repetitive stimuli pattern sets to in vitro cultures of neuron ensembles to condition them to learn and execute a mathematical transform

Anatomy and Physiology of Macaque Visual Cortical Areas V1, V2, and V5/MT : Bases for Biologically Realistic Models

The cerebral cortex of primates encompasses multiple anatomically and physiologically distinct areas processing visual information. Areas V1, V2, and VS/MT are conserved across mammals and are central for visual behavior. To facilitate the generation of biologically accurate computational models of primate early visual processing, here we provide an overview of over 350 published studies of these three areas in the genus Macaca, whose visual system provides the closest model for human vision. The literature reports 14 anatomical connection types from the lateral geniculate nucleus of the thalamus to V1 having distinct layers of origin or termination, and 194 connection types between V1, V2, and VS, forming multiple parallel and interacting visual processing streams. Moreover, within V1, there are reports of 286 and 120 types of intrinsic excitatory and inhibitory connections, respectively. Physiologically, tuning of neuronal responses to 11 types of visual stimulus parameters has been consistently reported. Overall, the optimal spatial frequency (SF) of constituent neurons decreases with cortical hierarchy. Moreover, VS neurons are distinct from neurons in other areas for their higher direction selectivity, higher contrast sensitivity, higher temporal frequency tuning, and wider SF bandwidth. We also discuss currently unavailable data that could be useful for biologically accurate models.Peer reviewe

Modeling the Emergence of Whisker Direction Maps in Rat Barrel Cortex

Based on measuring responses to rat whiskers as they are mechanically stimulated, one recent study suggests that barrel-related areas in layer 2/3 rat primary somatosensory cortex (S1) contain a pinwheel map of whisker motion directions. Because this map is reminiscent of topographic organization for visual direction in primary visual cortex (V1) of higher mammals, we asked whether the S1 pinwheels could be explained by an input-driven developmental process as is often suggested for V1. We developed a computational model to capture how whisker stimuli are conveyed to supragranular S1, and simulate lateral cortical interactions using an established self-organizing algorithm. Inputs to the model each represent the deflection of a subset of 25 whiskers as they are contacted by a moving stimulus object. The subset of deflected whiskers corresponds with the shape of the stimulus, and the deflection direction corresponds with the movement direction of the stimulus. If these two features of the inputs are correlated during the training of the model, a somatotopically aligned map of direction emerges for each whisker in S1. Predictions of the model that are immediately testable include (1) that somatotopic pinwheel maps of whisker direction exist in adult layer 2/3 barrel cortex for every large whisker on the rat's face, even peripheral whiskers; and (2) in the adult, neurons with similar directional tuning are interconnected by a network of horizontal connections, spanning distances of many whisker representations. We also propose specific experiments for testing the predictions of the model by manipulating patterns of whisker inputs experienced during early development. The results suggest that similar intracortical mechanisms guide the development of primate V1 and rat S1

Towards building a more complex view of the lateral geniculate nucleus: Recent advances in understanding its role

The lateral geniculate nucleus (LGN) has often been treated in the past as a linear filter that adds little to retinal processing of visual inputs. Here we review anatomical, neurophysiological, brain imaging, and modeling studies that have in recent years built up a much more complex view of LGN . These include effects related to nonlinear dendritic processing, cortical feedback, synchrony and oscillations across LGN populations, as well as involvement of LGN in higher level cognitive processing. Although recent studies have provided valuable insights into early visual processing including the role of LGN, a unified model of LGN responses to real-world objects has not yet been developed. In the light of recent data, we suggest that the role of LGN deserves more careful consideration in developing models of high-level visual processing

A Linear Model of Phase-Dependent Power Correlations in Neuronal Oscillations

Recently, it has been suggested that effective interactions between two neuronal populations are supported by the phase difference between the oscillations in these two populations, a hypothesis referred to as “communication through coherence” (CTC). Experimental work quantified effective interactions by means of the power correlations between the two populations, where power was calculated on the local field potential and/or multi-unit activity. Here, we present a linear model of interacting oscillators that accounts for the phase dependency of the power correlation between the two populations and that can be used as a reference for detecting non-linearities such as gain control. In the experimental analysis, trials were sorted according to the coupled phase difference of the oscillators while the putative interaction between oscillations was taking place. Taking advantage of the modeling, we further studied the dependency of the power correlation on the uncoupled phase difference, connection strength, and topology. Since the uncoupled phase difference, i.e., the phase relation before the effective interaction, is the causal variable in the CTC hypothesis we also describe how power correlations depend on that variable. For uni-directional connectivity we observe that the width of the uncoupled phase dependency is broader than for the coupled phase. Furthermore, the analytical results show that the characteristics of the phase dependency change when a bidirectional connection is assumed. The width of the phase dependency indicates which oscillation frequencies are optimal for a given connection delay distribution. We propose that a certain width enables a stimulus-contrast dependent extent of effective long-range lateral connections

A Cortical Sparse Distributed Coding Model Linking Mini- and Macrocolumn-Scale Functionality

No generic function for the minicolumn – i.e., one that would apply equally well to all cortical areas and species – has yet been proposed. I propose that the minicolumn does have a generic functionality, which only becomes clear when seen in the context of the function of the higher-level, subsuming unit, the macrocolumn. I propose that: (a) a macrocolumn's function is to store sparse distributed representations of its inputs and to be a recognizer of those inputs; and (b) the generic function of the minicolumn is to enforce macrocolumnar code sparseness. The minicolumn, defined here as a physically localized pool of ∼20 L2/3 pyramidals, does this by acting as a winner-take-all (WTA) competitive module, implying that macrocolumnar codes consist of ∼70 active L2/3 cells, assuming ∼70 minicolumns per macrocolumn. I describe an algorithm for activating these codes during both learning and retrievals, which causes more similar inputs to map to more highly intersecting codes, a property which yields ultra-fast (immediate, first-shot) storage and retrieval. The algorithm achieves this by adding an amount of randomness (noise) into the code selection process, which is inversely proportional to an input's familiarity. I propose a possible mapping of the algorithm onto cortical circuitry, and adduce evidence for a neuromodulatory implementation of this familiarity-contingent noise mechanism. The model is distinguished from other recent columnar cortical circuit models in proposing a generic minicolumnar function in which a group of cells within the minicolumn, the L2/3 pyramidals, compete (WTA) to be part of the sparse distributed macrocolumnar code

Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision

To appear in CVIUStudies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision

網膜における視覚情報の符号化

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 横澤 一彦, 東京大学教授 今水 寛, 東京大学准教授 村上 郁也, 立命館大学教授 立花 政夫, 日本医科大学教授 金田 誠University of Tokyo(東京大学

A Large-Scale Model of Spatio-Temporal Patterns of Excitation and Inhibition Evoked by the Horizontal Network in Layer 2/3 of the Visual Cortex

Cortical processing of even the most elementary visual stimuli can result in the propagation of information over significant spatiotemporal scales. To fully understand the impact of such phenomena it is essential to consider the influence of both the neural circuitry beyond the immediate retinotopic location of the stimulus, including pre-cortical areas, and the temporal components of stimulus driven activity that may persist over significant periods. Two computational modelling studies have been performed to explore these phenomena and are reported in this thesis. I) The plexus of long and short range lateral connections is a prominent feature of the layer 2/3 microcircuit in primary visual cortex. Despite the scope for possible functionality, the interdependence of local and long range circuits is still unclear. Spatiotemporal patterns of activity appear to be shaped by the underlying connectivity architecture and strong inhibition. A modelling study has been conducted to capture population activity that has been observed in vitro using voltage sensitive dyes. The model demonstrates that the precise spatiotemporal spread of activity seen in the cortical slice results from long range connections that target specific orientation domains whilst distinct regions of suppressed activity are shown to arise from local isotropic axonal projections. Distal excitatory activity resulting from long range axons is shaped by local interneurons similarly targeted by such connections. It is shown that response latencies of distal excitation are strongly influenced by frequency dependent facilitation and low threshold characteristics of interneurons. Together, these results support hypotheses made following experimental observations in vitro and clearly illustrate the underlying mechanisms. However, predictions by the model suggest that in vivo conditions give rise to markedly different spatiotemporal activity. Furthermore, opposing data in the literature regarding inter-laminar connectivity give rise to profoundly different spatiotemporal patterns of activity in cortex. 2) The second computational modelling study considers simple moving stimuli. These stimuli are implicated in the 'motion streak' phenomenon whereby the movement of a visual feature can give rise to trajectory information that is not explicitly present. Published experimental data of an in vivo study in the cat has shown that a single small light square moving stimulus elicits activity in populations of neurons in primary visual cortex that are selective for orientations parallel to stimulus trajectory (Jancke 2000). In more recent, unpublished data, this work is extended to consider long term persistent cortical activity that is generated by similar stimuli. These data indicate that following initial cortical activation that appears to result directly from the stimulus, iso-orientation domains display persistent activity. Furthermore, initial activity is broadly tuned with respect to orientation whilst later activity is strongly selective for orientations that are parallel to the stimulus trajectory. Currently the generative processes involved have not been clearly defined. Hence the proposed thesis will contribute to a more complete understanding of the mechanisms responsible for such cortical representations of moving visual stimuli. More specifically this will be achieved by a large scale mean field model that will enable a thorough investigation of the anatomical and electrophysiological elements concerned with the observed spatiotemporal dynamic behaviour and will represent a significant region of cortex. In conjunction, an existing computational model of the retina will be integrated. In doing so this thesis will offer the notion that certain cortical representations are inextricably linked with earlier stages of the visual pathway. As such consideration of retinal processing is fundamental to the understanding cortical functions and failure to do so can only result in erroneous conclusions

Intrinsic and Visually-Evoked Properties of Layer 2/3 Neurons in Mouse Primary Visual Cortex and Their Dependence on Sensory Experience

Neurons in Primary Visual Cortex (V1) are known to respond strongly to visual stimuli. Studies of neuronal responses in V1, carried out first in cats, but later primates and other mammals, have demonstrated that bars of light at particular orientations evoke strong, reliable responses in terms of increased firing rate of action potentials. Tuning of neuronal responses to certain stimulus parameters, such as orientation but also spatial and temporal frequencies, as well as the apparent dichotomy between simple and complex responses, have given rise to a number of influential models not just of V1 function, but more generally in the field of cortical physiology and computer vision. Owing to its small size and the plethora of available molecular and genetic tools, the visual cortex of the mouse may be a more tractable model system than that of much larger animals. Recent studies of neuronal responses in mouse V1 have shown that these are broadly similar to those of primates and carnivores, although not identical in all aspects. My thesis aims firstly to characterise intrinsic and sensory-evoked properties in regularspiking, putative pyramidal neurons in L2/3 of the mouse visual cortex using whole-cell patch clamp recording in vivo . In addition, the anatomical connectivity of individual neurons is characterised using virus-assisted circuit mapping. The majority of these neurons are found to be simple cells. Orientation tuning (the degree to which neuronal responses are selective to stimuli of a preferred orientation) is found to be quite variable, even within this singular group of neurons. The potential roles of intrinsic diversity, functional connectivity, and sensory experience in setting the orientation tuning of a particular neuron are investigated. These findings provide an insight in to how diverse responses to sensory stimuli can be generated in an apparently homogenous group of neurons