Brain modelling

Publications related to brain modelling.

  1. E. I. Barakova and T. Lourens. Mirror neuron framework yields representations for robot interaction [858 KB pdf]. Neurocomputing, 72(4-6):895-900, 2009.

    Abstract

    Common coding is a functional principle that underlies the mirror neuron paradigm. It insures actual parity between perception and action, since the perceived and performed actions are equivalently and simultaneously represented within the mirror neuron system. Based on the parity of this representation we show how the mirror neuron system may facilitate the interaction between two robots. Synchronization between neuron groups in different structures of the mirror neuron system are on the basis of the interaction behavior. The robotic simulation is used to illustrate several interactions. The resulting synchronization and turn taking behaviors show the potential of the mirror neuron paradigm for designing of socially meaningful behaviors.

  2. T. Lourens and E. I. Barakova. Orientation Contrast Sensitive Cells in Primate V1 -a computational model [607 KB pdf]. Natural Computing, 6(3):241-252, September 2007.

    Abstract

    Many cells in the primary visual cortex respond differently when a stimulus is placed outside their classical receptive field (CRF) compared to the stimulus within the CRF alone, permitting integration of information at early levels in the visual processing stream that may play a key role in intermediate-level visual tasks, such a perceptual pop-out (Knierim and van Essen, 1992; Nothdurft et al., 1999), contextual modulation (Levitt and Lund, 1997; Das and Gilbert, 1999; Dragoi and Sur, 2000), and junction detection (Sillito et al., 1995; Das and Gilbert, 1999; Jones et al., 2002). In this paper we construct a computational model in programming environment TiViPE (Lourens, 2004) of orientation contrast type of cells and demonstrate that the model closely resembles the functional behavior of the neuronal responses of non orientation (within the CRF) sensitive 4CB cells (Jones et al., 2002), and give an explanation of the indirect information flow in V1 that explains the behavior of orientation contrast sensitivity. The computational model of orientation contrast cells demonstrates excitatory responses at edges near junctions that might facilitate junction detection, but the model does not reveal perceptual pop-out.

  3. E. I. Barakova and T. Lourens. Efficient episode encoding for spatial navigation [1347 KB pdf]. International Journal of Systems Science, 36(14):877-885, November 2005.

    Abstract

    A method for familiarity mediated encoding of episodic memories for their inferential use in spatial navigation task is proposed. The method is strongly inspired by the state-of-the-art understanding of the hippocampal functioning and especially its role in novelty detection and episodic memory formation in relation to spatial context. The model is constructed on the presumption that episodic memory formation has behavioral, as well as sensory and perceptual correlates. In addition, the findings regarding hippocampal involvement in the novelty/familiarity detection and episodic memory formation, together with the existence of a straightforward parallel between internal hippocampal and abstract spatial representations are incorporated in the model. A navigation task is used to provide an experimental setup for behavioral testing with a rat-like agent. For this purpose, a framework that connects robot navigation and episodic memory representation is suggested. The computations are adapted for a real-time application. Simulation results show encoding of episodes and their use for navigation.

  4. E. I. Barakova and T. Lourens. Spatial navigation based on novelty mediated autobiographical memory [364 KB pdf]. In J. Mira and J. R. Alvarez, editors, IWINAC 2005, number 3561 in Lecture Notes in Computer Science, pages 1-10, Las Palmas de Gran Canaria, Spain, June 2005. Springer-verlag.

    Abstract

    This paper presents a method for spatial navigation performed mainly on past experiences. The past experiences are remembered in their temporal context, i.e. as episodes of events. The learned episodes form an active autobiography that determines the future navigation behaviour. The episodic and autobiographical memories are modelled to resemble the memory formation process that takes place in the rat hippocampus. The method implies naturally inferential reasoning in the robotic framework that may make it more flexible for navigation in unseen environments. The relation between novelty and life-long exploratory (latent) learning is shown to be important and therefore is incorporated into the learning process. As a result, active autobiography formation depends on latent learning while individual trials might be reward driven. The experimental results show that learning mediated by novelty provides a flexible and efficient way to encode spatial information in its contextual relatedness and directionality. Therefore, performing a novel task is fast but solution is not optimal. In addition, learning becomes naturally a continuous process – encoding and retrieval phase have the same underlying mechanism, and thus do not need to be separated. Therefore, building a “life long” autobiography is feasible.

  5. T. Lourens and E. I. Barakova. Simulation of Orientation Contrast Sensitive Cell Behavior in TiViPE [455 KB pdf]. In J. Mira and J. R. Alvarez, editors, IWINAC 2005, number 3561 in Lecture Notes in Computer Science, pages 57-66, Las Palmas de Gran Canaria, Spain, June 2005. Springer-verlag.

    Abstract

    Many cells in the primary visual cortex respond differently when a stimulus is placed outside their classical receptive field (CRF) compared to the stimulus within the CRF alone, permitting integration of information at early levels in the visual processing stream that may play a key role in intermediate-level visual tasks, such a perceptual popout [11], contextual modulation [7, 3, 4], and junction detection [13, 3, 5]. In this paper we construct a computational model in programming environment TiViPE [9] of orientation contrast type of cells and demonstrate that the model closely resembles the functional behavior of the neuronal responses of non orientation (within the CRF) sensitive 4CB cells [5], and give an explanation of the indirect information flow in V1 that explains the behavior of orientation contrast sensitivity.

  6. T. Lourens and E. I. Barakova. TiViPE Simulation of a Cortical Crossing Cell Model. [1786 KB pdf]. In J. Cabastany, A. Prieto, and D. F. Sandoval, editors, IWANN 2005, number 3512 in Lecture Notes in Computer Science, pages 122-129, Barcelona, Spain, June 2005. Springer-verlag.

    Abstract

    Many cells in cat and monkey visual cortex (area V1 and area 17) respond to gratings and bar patterns of different orientation between center and surround [18]. It has been shown that these cells respond on average 3.3 times stronger to a crossing pattern than to a single bar [16]. In this paper a computational model for a group of neurons that respond solely to crossing patterns is proposed, and has been implemented in visual programming environment TiViPE [10]. Simulations show that the operator responds very accurately to crossing patterns that have an angular difference between 2 bars of 40 degrees or more, the operator responds appropriately to bar widths that are bound by 50 to 200 percent of the preferred bar width and is insensitive to non-uniform illumination conditions, which appear to be consistent with the experimental results.

  7. T. Lourens, E. I. Barakova, H. G. Okuno, and H. Tsujino. A computational model of monkey cortical grating cells [478 KB pdf]. Biological Cybernetics, 92(1):61-70, January 2005. DOI: 10.1007/s00422-004-0522-2.

    Abstract

    Grating cells were discovered in the V1 and V2 areas of the monkey visual cortex by von der Heydt et al. (1992). These cells responded vigorously to grating patterns of appropriate orientation and periodicity. Computational models inspired by these findings were used as texture operator (Kruizinga and Petkov 1995, 1999; Petkov and Kruizinga 1997) and for the emergence and self-organization of grating cells (Brunner et al. 1998; Bauer et al. 1999). The aim of this paper is to create a grating cell operator G that demonstrates similar responses to monkey grating cells by applying operator G to the same stimuli as in the experiments carried out by von der Heydt et al. (1992). Operator G will be tested on images that contain periodic patterns as suggested by De Valois and De Valois (1988). In order to learn more about the role of grating cells in natural vision, operator G is applied to 338 real-world images of textures obtained from three different databases. The results suggest that grating cells respond strongly to regular alternating periodic patterns of a certain orientation. Such patterns are common in images of human-made structures, like buildings, fabrics, and tiles, and to regular natural periodic patterns, which are relatively rare in nature.

  8. T. Lourens, E. I. Barakova, and H. Tsujino. Interacting Modalities through Functional Brain Modeling [225 KB pdf]. In J. Mira and J. R. Alvarez, editors, Proceedings of the International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003, volume 2686 of Lecture Notes in Computer Science, pages 102-109, Menorca, Spain, June 2003. Springer-Verlag.

    Abstract

    This paper proposes a concept for modeling modalities and understanding the interaction between modalities through functional brain modeling (FBM). FBM proves to be a powerful method for functional behavior prediction of a group of neuronal cells with equivalent functional behavior. An example of interacting groups of neuronal cells, utilizing FBM, in early vision is given. A broad setup of functional behavior and interaction between different groups of cells in early vision has similar conceptual properties as cells that process other sensory information or multi modal sensory information.

  9. T. Lourens, H. G. Okuno, and H. Kitano. Detection of Oriented Repetitive Alternating Patterns in Color Images -A Computational Model of Monkey Grating Cells [5531 KB pdf]. In J. Mira, editor, Proceedings of the International Workshop on Artificial Neural Networks, IWANN 2001, volume 1676, Part I of Lecture Notes in Computer Science, pages 95-107, Granada, Spain, June 2001. Springer-Verlag.

    Abstract

    In 1992 neurophysiologists [20] found a new type of cells in areas V1 and V2 of the monkey primary visual cortex, which they called grating cells. These cells respond vigorously to a grating pattern of appropriate orientation and periodicity. Three years later a computational model inspired by these findings was published [9]. The study of this paper is to create a grating cell operator that has similar response profiles as monkey grating cells have. Three different databases containing a total of 338 real world images of textures are applied to the new operator to get better a insight to which natural patterns grating cells respond. Based on these images, our findings are that grating cells respond best to repetitive alternating patterns of a specific orientation. These patterns are in common human made structures, like buildings, fabrics, and tiles.

  10. T. Lourens, K. Nakadai, H. G. Okuno, and H. Kitano. Graph extraction from color images [149 KB pdf]. In Proceedings of the 9th European Symposium on Artificial Neural Networks, ESANN 2001, pages 329-334, Brugge, Belgium, April 2001.

    Abstract

    An approach to symbolic contour extraction will be described that consists of three stages: enhancement, detection, and extraction of edges and corners. Edges and corners are enhanced by models of monkey cortical complex and end-stopped cells. Detection of corners and local edge maxima is performed by selection of local maxima in both edge and corner enhanced images. These maxima form the anchor points of a greedy contour following algorithm that extracts the edges. This algorithm is based on an idea of spatially linking neurons along the edge that will fire in synchrony to indicate an extracted edge. The extracted edges and detected corners represent the symbolic representation of the image. The advantage of the proposed model over other models is that the same low constant thresholds for corner and local edge maxima detection are used for different images. Closed contours are guaranteed by the contour following algorithm to yield a fully symbolic representation which is more suitable for reasoning and recognition. In this respect our methodology is unique, and clearly different from the standard edge detection methods.

  11. T. Lourens, K. Nakadai, H. G. Okuno, and H. Kitano. A Computational Model of Monkey Grating Cells for Oriented Repetitive Alternating Patterns [192 KB pdf]. In Proceedings of the 9th European Symposium on Artificial Neural Networks, ESANN 2001, pages 315-322, Brugge, Belgium, April 2001.

    Abstract

    In 1992 neurophysiologists [5] found an new type of cells in areas V1 and V2 of the monkey primary visual cortex, which they called grating cells. These cells respond vigorously to a grating pattern of appropriate orientation and periodicity. A few years later a computational model inspired by these findings was published [3]. The study of this paper is to model a grating cell operator that responds in a very similar way as these grating cells do. Three different databases containing a total of 338 real world images of textures were applied to the operator. Based on these images, our findings were that grating cells respond best to repetitive alternating patterns of a specific orientation. These patterns are mostly human made structures, like buildings, fabrics, and tiles.

  12. R. P. Wurtz and T. Lourens. Corner detection in color images through a multiscale combination of end-stopped cortical cells. [1127 KB pdf]. Image and Vision Computing, 18(6-7):531-541, April 2000.

    Abstract

    We assess the corner-detection capabilities of a model for end-stopped cells in the visual cortex (F. Heitger, L. Rosenthaler, R. von der Heydt, E. Peterhans, O. Kubler, Simulation of neural contour mechanisms: from simple to end-stopped cells, Vision Research 32(5) (1992) 963–981). The responses of one of these cells alone cannot account for the percept of a corner. This shortcoming can be greatly alleviated by a combination over several scales. The resulting corner detection method can deal with high frequency texture, low contrast, and rounded corners and is competitive in comparison with other corner detectors. Starting from known cortical cell types we hypothesize a colorsensitive equivalent of simple cells. This allows to extend corner detection to color-sensitive channels. The combination of grey-scale and color corner-detection yields a biologically plausible model of corner perception and may also be of interest for computer vision applications.

  13. T. Lourens. A Biologically Plausible Model for Corner-based Object Recognition from Color Images [5296 KB pdf]. Shaker Publishing B.V., Maastricht, The Netherlands, March 1998.

    1.1 Outline of the thesis

    The flow of visual information via the M or magno-cellular (from magnus = large) pathway is basically used to order the chapters in the thesis, with one exception: Chapter 7 is about color which belongs to the P or parvo-cellular (from parvus = small) pathway.
    We know that our visual system detects and locates corners and edges accurately. In Chapter 2 we take a closer view of the main flow of visual information from eye to primary visual cortex. This information flow is known as early vision.
    Chapter 3 gives a general overview of the most widely used models for different cells in early vision. The modeled properties of most of these cells form the basis for our approach of artificial vision. We model the so-called center-surround, simple, and complex cell types. In the visual system these cell types can differ in their spatial (and temporal) resolution. Some cells only respond to very small parts of the visual field and are involved with highly detailed vision, while other cells respond to large parts of the visual field. These cells interact at different levels of accuracy (scales). Interaction at different scales raises the questions which scales are useful and how these scales can be ordered. In natural vision systems the spatial accuracy drops with eccentricity, but in most static artificial systems accuracy is uniform.We do pay attention to this phenomenon, however, for the sake of completeness. The decrease in spatial accuracy with eccentricity highly reduces the amount of information. A reduction of information is important since it makes the system relatively fast when motion is involved. Motion is not included in this thesis but it will be added to the model in future research.
    In Chapter 4 a corner operator based on responses of cortical end-stopped cells proposed by Heitger et al. [71] will be modeled (with some minor but important changes) and compared to six standard corner enhancing operators. We are aiming at a robust operator with respect to the position under different conditions (e.g. rotation of the image), since it forms the basis for the graph. Rotation and position at different scales are analyzed to determine the robustness of the operator.
    Chapter 5 improves the end-stopped operator by using multiple scales. This is necessary since the operator is noise sensitive at small scales and does not respond to all different corners at a single scale. The response of the operator will be examined for different scales at convex corners, rounded corners, and several junctions.We discuss the choice for a proper operator to combine multiple scales and motivate the choice for the number of scales. Finally the multiple scale corner operator will be compared with standard operators at both single and multiple scales.
    Chapter 6 describes a line-segment extraction algorithm where the corners obtained with the end-stopped operator at multiple scales are used together with the “edge enhanced image”. Edges between a pair of corner points will be extracted only, hence edges without two corner points are not detected. The content of this chapter is meant as an intermediate step towards a graph representation and should be regarded as preprocessing for graph matching (see Chapter 8).
    Chapter 7 extends the model with color. We use two “color-opponent” channels which are found in natural color vision. In previous chapters we gave a model which is based on achromatic vision. In this chapter we use this model but apply it to two different opponent channels. Hence models for biologically plausible color-opponent cells are proposed: one opponent cell type which responds to edges of a preferred orientation and a type which responds to corners. In natural vision two opponent color channels are found; in combination with the achromatic channel, every color can be reconstructed. The three channels are combined to yield the final edge and corner detection model.
    Chapter 8 gives a graph matching algorithm which searches copies of different known objects in the input graph. The representation of these objects accomplishes scale, rotation, and translation invariance. The matching algorithm is based on a standard back-tracking algorithm which is a time consuming (NP-hard) problem. Hence angle and length ratio attributes which are found in every two-dimensional graph are added to speed up the search. Appendix A describes some functions used in linear filtering, such as the Gaussian, Laplacian of a Gaussian, and the difference of Gaussians. This appendix aims at the reader who is interested in the differences and properties of these functions.
    Parts of Chapter 3 and Appendix A have been published in [120, 121], parts of Chapters 4, 5, and 7 in [198], and parts of Chapters 6 and 8 in [122].

    1.2 Contributions

    In this section we give the contributions of this thesis.

    1. An overview of early vision from a computational point of view is given. With this overview an artificial vision system based on line and corner enhancement can be constructed (Chapter 3).
    2. The corner detecting qualities of the model of end-stopped cells, proposed by Heitger et al. [71], are assessed (Chapter 4).
    3. We propose a new corner detector by a multi-scale combination of the modeled end-stopped cells (Chapter 5), which yields:
      • a physiological model for the percept of a corner and
      • a useful corner operator for computer vision.
    4. Edge, and corner enhancement algorithms are generalized to color channels. We use the properties of the complex and end-stopped cells and assume that these cells respond excitatory to one color and inhibitory to another color (Chapter 6).
    5. We develop a line detection algorithm, based on the assumption that corners are more stable than lines, and use it to extract line-segments by following enhanced edges from one corner to another (Chapter 7).
    6. We develop an attributed graph format for views of objects, which is suited for objects in which all edges are spanned by corners.
    7. A graph matching algorithm will be used for object recognition (Chapter 8), where the choice of attributes leads to:
      • invariance under translation, rotation and scaling,
      • robustness under small perspective changes and undetected lines, and
      • reduction of evaluations from N! to less than N3.

    Points 4-6 apply also to physiologically motivated color channels and complete color images. This is still done rarely in computer vision.

  14. T. Lourens. A Model of Spatial Filtering by Visual Cortical Simple Cells. [336 KB pdf]. In R Moreno-Diaz and J. Mira-Mira, editors, Brain Processes, Theories, and Models. An International Conference in Honor of W.S. McCulloch 25 Years after his Death, pages 391-400. MIT press, Cambridge USA, November 12-17 1995.

    Abstract

    In this paper a filter is modeled, based on the visual system of mammals. In the filter the retinal ganglion receptive fields and simple cortical receptive fields are used as described by neurophysiologists. The functionality of a ganglion cell and a simple cell are modeled with a gaussian and Gabor function respectively. In the filter also the linear magnification function is included which gives the size of the receptive field with retinal eccentricity. With modeling the filter we hope to get more insights in the human visual system and in the primary visual cortex especially.

  15. T. Lourens. Modeling retinal high and low contrast sensitivity filters [1325 KB pdf]. In J. Mira and F. Sandoval, editors, Proceedings of the International Workshop on Artificial Neural Networks, IWANN ’95, volume 930 of Lecture Notes in Computer Science, pages 61-68. Springer-Verlag, June 7-9 1995.

    Abstract

    In this paper two types of ganglion cells in the visual system of mammals (monkey) are modeled. A high contrast sensitive type, the so called M-cells, which project to the two magno-cellular layers of the lateral geniculate nucleus (LGN) and a low sensitive type, the P-cells, which project to the four parvo-cellular layers of the LGN. The results will be compared with the ganglion cells as described by Kuffler.

  16. T. Lourens, N. Petkov, and P. Kruizinga. Large scale natural vision simulations [342 KB pdf]. Future Generation Computer Systems, Issue: High Performance Computing and Networking (HPCN), 10:351-358, June 1994.

    Abstract

    A computationally intensive approach to pattern recognition in images is developed and applied to face recognition. Similarly to previous work, we compute functional inner products of a two-dimensional input signal (image) with a set of two-dimensional Gabor functions which fit the receptive fields of simple cells in the primary visual cortex of mammals. The proposed model includes non-linearities, such as thresholding, orientation competition, and lateral inhibition. The output of the model is a set of cortical images each ofwhich contains only edge lines of a particular orientation in a particular light-to-dark transition direction. In this way the information of the original image is split into different channels. The cortical images are used to compute a lower-dimension space representation for object recognition. The method was implemented on the Connection Machine CM-5 and achieved a recognition rate of 97% when applied to a large database of face images.

  17. N. Petkov and T. Lourens. Interacting cortical filters for object recognition [150 KB pdf]. In K. Sugihara, editor, Proceedings of Asian Conference on Computer Vision, ACCV ’93, pages 583-586, Nov. 23-25 1993.

    Abstract

    It is shown how cortical filters can be used for image analysis and object recognition. Similarly to previous work in this area, we compute functional inner products of a two-dimensional input signal (image) with a set of two-dimensional Gabor functions which fit the receptive fields of simple cells in the primary visual cortex of mammals. We propose a method in which these inner products become the subject of thresholding orientation competition and lateral inhibition. Each of the resulting cortical images contains only edge lines of a particular orientation and a particular light-to-dark transition direction. In this way, the information which is present in the original image is split in different channels and we show how this splitting can be used for object recognition. The method discriminates between simple geometrical figures, e.g. polygons with different numbers of edges, with reliability of 100% and a recognition rate of 99% has been achieved when the method was applied to a large database of face images.

  18. N. Petkov, P. Kruizinga, and T. Lourens. Orientation Competition in Cortical Filters -an Application to Face Recognition [468 KB pdf]. In H.A. Wijshoff, editor, Proceedings of Computing Science in The Netherlands, CSN ’93, pages 285-296, Nov. 9-10 1993.

    Abstract

    A biologically motivated, computationally intensive approach to computer vision is developed and applied to the problem of automatic face recognition. The approach is based on the use of two-dimensional Gabor functions which model the receptive eld functions of simple cells in the primary visual cortex of mammals. The convolutions of an input image with a set of antisymmetric visual receptive field functions (imaginary parts of Gabor functions) become the subject of thresholding and orientation competition. The developed cortical lters deliver highly structured information which is used for efficient feature extraction and representation in a lower dimension space. Applied to face recognition, the method gives a recognition rate of 98.5% on a large database of face images.

  19. N. Petkov, T. Lourens, and P. Kruizinga. Lateral inhibition in cortical filters [303 KB pdf]. In A.G. Constantinides, V. Cappellini, C.S. Pattichis, and C.N. Schizas, editors, Proceedings of the International Conference on Digital Signal Processing and International Conference on Computer Applications to Engineering Systems, pages 122-129, July 14-16 1993.

    Abstract

    This work presents explorations in the microstructure of natural vision systems based on large scale computer simulations. Similarly to previous work in this area, we compute the functional inner products of a two-dimensional input signal image with a set of two-dimensional Gabor functions which have been shown to fit the receptive fields of simple cells in the primary visual cortex of mammals. These inner products are then considered as net inputs to the cortical cells and used to compute the cell activations as non-linear functions. A previously used model is extended with a pixel-wise winner-takes-all competition between different Gabor filters which is introduced in order to model lateral inhibition between cortical cells. The effect of lateral inhibition is qualitatively estimated by visualization of computed cortical images and quantitatively evaluated by applying the model to a face recognition problem. Recognition rate of 97% was achieved on a database of 205 face images of 30 persons vs. 94% achieved with a previously used model.

  20. N. Petkov, P. Kruizinga, and T. Lourens. Biologically motivated approach to face recognition [512 KB pdf]. In J. Mira, J. Cabestany, and A. Prieto, editors, New Trends in Neural Computation, Proceedings of the International Workshop on Artificial Neural Networks, IWANN ’93, volume 686 of Lecture Notes in Computer Science, pages 68-77. Springer-Verlag, June 9-11 1993.

    Abstract

    A biologically motivated compute intensive approach to computer vision is developed and applied to the problem of face recognition. The approach is based on the use of two-dimensional Gabor functions that fit the receptive fields of simple cells in the primary visual cortex of mammals. A descriptor set that is robust against translations is extracted by a global reduction operation and used for a search in an image database. The method was applied on a database of 205 face images of 30 persons and a recognition rate of 94% was achieved.

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