Excitatory Lateral Connections Research Articles

The role of pattern coherence in interocular grouping during binocular rivalry: Insights from individual differences

Interocular grouping during binocular rivalry occurs when two images presented to each eye combine into a coherent pattern. The experience of interocular grouping is thought to be influenced by both eye-of-origin, which involves excitatory lateral connections among monocular neurons, and pattern coherence, which results from top-down intervention from higher visual areas. However, it remains unclear which factor plays a more significant role in the interocularly-grouped percepts during binocular rivalry. The current study employed an individual difference approach to investigate whether grouping dynamics are mainly determined by eye-of-origin or pattern coherence. We found that participants who perceived interocularly-driven coherent percepts for a longer duration also tended to experience longer periods of monocularly-driven coherent percepts. In contrast, participants who experienced non-coherent piecemeal percepts for an extended duration in conventional rivalry also had longer duration of non-coherent percepts in the interocular coherence setting. This individual differences in experiencing interocular grouping suggest that pattern coherence exerts a stronger influence on grouping dynamics during binocular rivalry compared to eye-of-origin factors.

Nonmonotonic spatial structure of interneuronal correlations in prefrontal microcircuits

Correlated fluctuations of single neuron discharges, on a mesoscopic scale, decrease as a function of lateral distance in early sensory cortices, reflecting a rapid spatial decay of lateral connection probability and excitation. However, spatial periodicities in horizontal connectivity and associational input as well as an enhanced probability of lateral excitatory connections in the association cortex could theoretically result in nonmonotonic correlation structures. Here, we show such a spatially nonmonotonic correlation structure, characterized by significantly positive long-range correlations, in the inferior convexity of the macaque prefrontal cortex. This functional connectivity kernel was more pronounced during wakefulness than anesthesia and could be largely attributed to the spatial pattern of correlated variability between functionally similar neurons during structured visual stimulation. These results suggest that the spatial decay of lateral functional connectivity is not a common organizational principle of neocortical microcircuits. A nonmonotonic correlation structure could reflect a critical topological feature of prefrontal microcircuits, facilitating their role in integrative processes.

The influence of astrocytes on the width of orientation hypercolumns in visual cortex: A computational perspective.

Orientation preference maps (OPMs) are present in carnivores (such as cats and ferrets) and primates but are absent in rodents. In this study we investigate the possible link between astrocyte arbors and presence of OPMs. We simulate the development of orientation maps with varying hypercolumn widths using a variant of the Laterally Interconnected Synergetically Self-Organizing Map (LISSOM) model, the Gain Control Adaptive Laterally connected (GCAL) model, with an additional layer simulating astrocytic activation. The synaptic activity of V1 neurons is given as input to the astrocyte layer. The activity of this astrocyte layer is now used to modulate bidirectional plasticity of lateral excitatory connections in the V1 layer. By simply varying the radius of the astrocytes, the extent of lateral excitatory neuronal connections can be manipulated. An increase in the radius of lateral excitatory connections subsequently increases the size of a single hypercolumn in the OPM. When these lateral excitatory connections become small enough the OPM disappears and a salt-and-pepper organization emerges.

A Theory of How Columns in the Neocortex Enable Learning the Structure of the World.

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.

Columnar-Organized Memory (COM): Brain-inspired associative memory with large capacity and robust retrieval

Abstract Brain-inspired computing is an interdisciplinary field, which aims to use the capabilities of the human brain. The brain is remarkable for large memory capacity and robust memory retrieval. These characteristics are due to the specific structure of the brain. In this paper, an associative memory with large capacity and robust retrieval is introduced that is inspired by the cortical structure. The proposed model is called Columnar-Organized Memory (COM). Its architecture is based on the spiking winner-take-all (WTA): There are lateral excitatory connections between the WTAs and lateral inhibitory connections between the neurons of a WTA. Training of COM involves a two-phase algorithm: pattern storage phase and pattern association phase. The pattern storage phase is based on a spike timing dependent plasticity (STDP) rule and the pattern association phase is compatible with the classic Hebbian rule. The key characteristics of the proposed model are storing a large number of messages and retrieving them accurately. These properties are related to its structure notably; robust retrieval is a consequence of the lateral excitatory connections. Also, the overall effect of the lateral inhibitory connections is large storage capacity.

Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition.

During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input.

The mapping of eccentricity and meridional angle onto orthogonal axes in the primary visual cortex: an activity-dependent developmental model.

Primate vision research has shown that in the retinotopic map of the primary visual cortex, eccentricity and meridional angle are mapped onto two orthogonal axes: whereas the eccentricity is mapped onto the nasotemporal axis, the meridional angle is mapped onto the dorsoventral axis. Theoretically such a map has been approximated by a complex log map. Neural models with correlational learning have explained the development of other visual maps like orientation maps and ocular-dominance maps. In this paper it is demonstrated that activity based mechanisms can drive a self-organizing map (SOM) into such a configuration that dilations and rotations of a particular image (in this case a rectangular bar) are mapped onto orthogonal axes. We further demonstrate using the Laterally Interconnected Synergetically Self Organizing Map (LISSOM) model, with an appropriate boundary and realistic initial conditions, that a retinotopic map which maps eccentricity and meridional angle to the horizontal and vertical axes respectively can be developed. This developed map bears a strong resemblance to the complex log map. We also simulated lesion studies which indicate that the lateral excitatory connections play a crucial role in development of the retinotopic map.

Contour detection based on horizontal interactions in primary visual cortex

Neurons in the primary visual cortex (V1) respond differently to a simple visual element presented in isolation from when it is embedded within a cluttered scene. The contextual influences are mediated by horizontal connections within V1. Inspired by these visual cortical mechanisms, a contour detection model is proposed. Different from previous researches, the facilitation and the suppression are unified modulated by surrounding elements in the image without separating regions of excitatory and inhibitory lateral connections specifically. The proposed model can selectively extract object contours while reducing non-meaningful texture edges and process angular visual features such as corners and T-junctions effectively. The experiments demonstrate the effectiveness of the proposed model.

Contribution of Intracolumnar Layer 2/3-to-Layer 2/3 Excitatory Connections in Shaping the Response to Whisker Deflection in Rat Barrel Cortex

This computational study integrates anatomical and physiological data to assess the functional role of the lateral excitatory connections between layer 2/3 (L2/3) pyramidal cells (PCs) in shaping their response during early stages of intracortical processing of a whisker deflection (WD). Based on in vivo and in vitro recordings, and 3D reconstructions of connected pairs of L2/3 PCs, our model predicts that: 1) AMPAR and NMDAR conductances/synapse are 0.52 ± 0.24 and 0.40 ± 0.34 nS, respectively; 2) following WD, connection between L2/3 PCs induces a composite EPSPs of 7.6 ± 1.7 mV, well below the threshold for action potential (AP) initiation; 3) together with the excitatory feedforward L4-to-L2/3 connection, WD evoked a composite EPSP of 16.3 ± 3.5 mV and a probability of 0.01 to generate an AP. When considering the variability in L4 spiny neurons responsiveness, it increased to 17.8 ± 11.2 mV; this 3-fold increase in the SD yielded AP probability of 0.35; 4) the interaction between L4-to-L2/3 and L2/3-to-L2/3 inputs is highly nonlinear; 5) L2/3 dendritic morphology significantly affects L2/3 PCs responsiveness. We conclude that early stages of intracortical signaling of WD are dominated by a combination of feedforward L4–L2/3 and L2/3–L2/3 lateral connections.

Automatic Buildings Extraction From LiDAR Data in Urban Area by Neural Oscillator Network of Visual Cortex

Researchers have extensively applied Locally Excitatory Globally Inhibitory Oscillator Networks (LEGION) for segmentation. These networks are neural oscillator networks based on biological frameworks, in which each oscillator has excitatory lateral connections to the oscillators in its local neighbourhood, as well as a connection to a global inhibitor. In this paper, we develop a modified LEGION segmentation to extract buildings from high-quality digital surface models (DSMs). The extraction is implemented without assumptions on the underlying structures in the DSM data and without prior knowledge of the number of regions. For complex information hidden in the generated DSM of an urban area, grey level co-occurrence matrix homogeneity is used to measure DSM height texture. We then use this homogeneity to distinguish buildings from trees and identify major oscillator blocks in target buildings, instead of using lateral potential. To segment pixels into different groups, we calculate the weight of the global inhibitor (Wz) from DSM complexity. Building boundaries are traced and regularised after extraction from the segmented DSM. A least squares solution with perpendicular constraints for determining regularised rectilinear building boundaries is proposed, and arc line fitting is performed. This paper presents the concept, algorithms, and procedures of the proposed approach. Experimental results on the Vaihingen region studied in the ISPRS test project are also discussed.

LEGION SEMENTATION FOR BUILDING EXTRACTION FROM LIDAR BASED DSM DATA

Abstract. Recently, a neural oscillator network based on biologically framework named LEGION (Locally Excitatory Globally Inhibitory Oscillator Network), which each oscillator has excitatory lateral connections to the oscillators in its local neighbourhood as well as a connection with a global inhibitor, has been applied to segmentation field. The extended LEGION approach is constructed to extract buildings digital surface model (DSM) generated from LiDAR data. This approach is with no assumption about the underlying structures in DSM data and no prior knowledge regarding the number of regions. Instead of using lateral potential to find a major oscillator block in original way, Gray Level Co-occurence Matrix (GLCM) homogeneity measuring DSM height texture is applied to distinguish buildings from trees and assist to find LEGION leaders in building targets. Alongside the DSM height texture attribure, extended LEGION can extract buildings close to trees automatically. Then a solution of least squares with perpendicularity constraints is put forward to determine regularized rectilinear building boundaries, after tracing and connection the rough building boundaries. In general, the paper presents the concept, algorithms and procedures of the approach. It also gives experimental result of Vaihingen A2 region by the ISPRS text project and another result based on a DSM data of suburban area. The experiment result showed that the proposed method can effectively produce more accurate buildings boundary extraction.

A model of the response of visual area V2 to combinations of orientations

The role of the V2 area in visual processing is still almost entirely unexplored. Recently, several studies revealed the tuning of V2 neurons in the macaque to stimuli consisting of two segments with different orientations. By measuring orientation tuning inside subunits of the overall receptive field, units with non uniform orientation selectivity have been found. In this work, the emergence of a computational organization supporting similar responses is explored, using an artificial model of cortical maps. This model, called LISSOM (Laterally Interconnected Synergetically Self-Organizing Map) includes excitatory and inhibitory lateral connections. In this simulation two LISSOM maps are arranged as V1 and V2 areas. In the first area, the classical domains of orientation selectivity develop, while in V2 most neurons become sensitive to pairs of orientations. The overall activation of these units depend on the presence of oriented segments at a finer grain than the whole receptive fields, with complex nonlinear interactions.

Statistical structure of lateral connections in the primary visual cortex

BackgroundThe statistical structure of the visual world offers many useful clues for understanding how biological visual systems may understand natural scenes. One particularly important early process in visual object recognition is that of grouping together edges which belong to the same contour. The layout of edges in natural scenes have strong statistical structure. One such statistical property is that edges tend to lie on a common circle, and this 'co-circularity' can predict human performance at contour grouping. We therefore tested the hypothesis that long-range excitatory lateral connections in the primary visual cortex, which are believed to be involved in contour grouping, display a similar co-circular structure.ResultsBy analyzing data from tree shrews, where information on both lateral connectivity and the overall structure of the orientation map was available, we found a surprising diversity in the relevant statistical structure of the connections. In particular, the extent to which co-circularity was displayed varied significantly.ConclusionsOverall, these data suggest the intriguing possibility that V1 may contain both co-circular and anti-cocircular connections.

Local intracortical circuitry not only for feature binding but also for rapid neuronal responses

Neurons of primary sensory cortices are known to have specific responsiveness to elemental features. To express more complex sensory attributes that are embedded in objects or events, the brain must integrate them. This is referred to as feature binding and is reflected in correlated neuronal activity. We investigated how local intracortical circuitry modulates ongoing-spontaneous neuronal activity, which would have a great impact on the processing of subsequent combinatorial input, namely, on the correlating (binding) of relevant features. We simulated a functional, minimal neural network model of primary visual cortex, in which lateral excitatory connections were made in a diffusive manner between cell assemblies that function as orientation columns. A pair of bars oriented at specific angles, expressing a visual corner, was applied to the network. The local intracortical circuitry contributed not only to inducing correlated neuronal activation and thus to binding the paired features but also to making membrane potentials oscillate at firing-subthreshold during an ongoing-spontaneous time period. This led to accelerating the reaction speed of principal cells to the input. If the lateral excitatory connections were selectively (instead of "diffusively") made, hyperpolarization in ongoing membrane potential occurred and thus the reaction speed was decelerated. We suggest that the local intracortical circuitry with diffusive connections between cell assemblies might endow the network with an ongoing subthreshold neuronal state, by which it can send the information about combinations of elemental features rapidly to higher cortical stages for their full and precise analyses.

The Gamma Slideshow: Object-Based Perceptual Cycles in a Model of the Visual Cortex

While recent studies have shed light on the mechanisms that generate gamma (>40 Hz) oscillations, the functional role of these oscillations is still debated. Here we suggest that the purported mechanism of gamma oscillations (feedback inhibition from local interneurons), coupled with lateral connections implementing “Gestalt” principles of object integration, naturally leads to a decomposition of the visual input into object-based “perceptual cycles,” in which neuron populations representing different objects within the scene will tend to fire at successive cycles of the local gamma oscillation. We describe a simple model of V1 in which such perceptual cycles emerge automatically from the interaction between lateral excitatory connections (linking oriented cells falling along a continuous contour) and fast feedback inhibition (implementing competitive firing and gamma oscillations). Despite its extreme simplicity, the model spontaneously gives rise to perceptual cycles even when faced with natural images. The robustness of the system to parameter variation and to image complexity, together with the paucity of assumptions built in the model, support the hypothesis that perceptual cycles occur in natural vision.

Dynamics of non-linear cortico-cortical interactions during motion integration in early visual cortex: a spiking neural network model of an optical imaging study in the awake monkey

Introduction Lateral interactions are crucial mechanisms in contextual modulation of visual processing, including visual motion. We have recently shown, using voltage-sensitive dye imaging (VSDI), that a local static stimulus (Gaussian blob) first activates a restricted cortical area, followed by slow horizontal propagation of activity along the cortex [1]. In a sequence of two local static stimuli, two-stroke apparent motion, the two waves of horizontal activation interact non-linearly in V1, giving rise to a gradual and smooth wave of normalized activity. This signature of non-linear integration was a wave of suppression traveling from the representation of the second stimulus towards the first stimulus. To investigate the cellular and network mechanisms underlying these non-linear lateral interactions, we constructed a two-dimensional cortical network model using spiking neurons with conductance-based synapses. The model represents cortical layer 2/3, the main source of the VSDI signals. The connectivity of the inhibitory neurons was restricted to the local neighborhood, whereas the excitatory neurons could, in addition, also make long-range horizontal connections. The physiology of these horizontal connections was adjusted to induce balanced excitation/inhibition at high activity levels [2]. To compare the model dynamics to the in vivo VSDI signals, we extracted a model VSDI signal by recording the membrane potentials of many neurons arranged on a fine rectangular grid. The model was written in PyNN [3] using NEST [4] as a simulator. Our simulations reproduced the experimental observations of slow horizontal propagation when stimulating the network by a single static stimulus. In the two-stroke apparent paradigm, the model reproduced the non-linear integration by a wave of suppression. The origin and dynamics of this suppression were caused by the activity-dependent amount of local inhibition balancing the effect of the excitatory lateral connections. Physiological data shows that similar wave of suppression could be observed with a wide range of spatio-temporal two stroke input, but also with real motion stimuli. We therefore generalized our study to conditions during which the stimulus speed varies according to the suppressive spread velocity. Thus, our model suggests that the wave of non-linear integration observed in vivo could be caused by local inhibition balancing the integration of horizontal inputs. Moreover, it highlights the importance of contextual modulation for visual processing.

Unsupervised learning of head-centered representations in a network of spiking neurons

Movement planning based on visual information requires a transformation from a retina-centered into a heador body-centered frame of reference. It has been shown that such transformations can be achieved via basis function networks [1,2]. We investigated whether basis functions for coordinate transformations can be learned by a biologically plausible neural network. We employed a model network of spiking neurons that learns invariant representations based on spatio-temporal stimulus correlations [3]. The model consists of a three-stage network of leaky integrate-and-fire neurons with biologically realistic conductances. The network has two input layers, corresponding to neurons representing the retinal image and neurons representing the direction of gaze. These inputs are represented in the map layer via excitatory or modulatory connections, respectively, that exhibit Hebbian-like spiketiming dependent plasticity (STDP). Neurons within the map layer are connected via short-range lateral excitatory connections and unspecific lateral inhibition. We trained the network with stimuli corresponding to typical viewing situations when a visual scene is explored by saccadic eye movements, with gaze direction changing on a faster time scale than object positions in space. After learning, each neuron in the map layer was selective for a small subset of the stimulus space, with excitatory and modulatory connections adapted to achieve a topographic map of the inputs. Neurons in the output layer with a localized receptive field in the map layer were selective for positions in head-centered space, invariant to changes in retinal image due to changes in gaze direction. Our results show that coordinate transformations via basis function networks can be learned in a biologically plausible way by exploiting the spatio-temporal correlations between visual stimulation and eye position signals under natural viewing conditions.

The model of ocular dominance pattern formation in the presence of gradients of chemical labels.

Event Abstract Back to Event The model of ocular dominance pattern formation in the presence of gradients of chemical labels. Dmitry Tsigankov1, 2* and Alexei Koulakov3 1 Bernstein Center for Computational Neuroscience, Germany 2 Max Planck Institute for Dynamics and Self-organization, Germany 3 Cold Spring Harbor Laboratory, United States The formation of ocular dominance (OD) columns in the mammalian visual cortex is thought to be driven by electrical activity of the projecting neurons. Indeed, theoretical modeling have shown that lateral cortical activity-dependent interactions are capable of producing such a periodic pattern of regions dominated by inputs from left or right eye. This scenario for OD pattern formation based on self-organization of inputs due to excitatory and inhibitory lateral interactions of Mexican-hat shape was recently confronted by several lines of experimental observations. First, the anatomical data on primary visual cortex indicate that the inhibition can have a shorter range than excitation, the regime in which the classical model fails to produce OD structure. Second, the measurements of the width of OD regions following the manipulations with the strength of the inhibitory connections are inconsistent with the predictions of the model. When the strength of inhibitory connections is increased the OD width is found to increase and when inhibition is decreased the OD width also decreases. This behavior is opposite to one predicted by the classical model. Finally, the sole role of activity-dependent self-organization in the formation of OD structure was questioned as it was observed that OD patterns can be formed in optic tectum in the presence of other factors such as gradients of interacting chemical labels.Here we present theoretical analysis of the possible interplay between genetically encoded labeling and experience-driven reorganization of the projections in the formation of OD patterns. We show that in the presence of single gradient of chemical marker the projections from two eyes are segregated into OD columns for a wide class of lateral interaction profiles. We obtain that depending on the range and strength of inhibitory and excitatory lateral connections the projecting neurons may prefer to form segregated or mixed inputs. We find the regimes when OD structure emerges for short-range inhibition and long-range excitation. We also investigate the role of lateral inhibition and excitation for different interaction profiles to find a novel regime when increase in the inhibition strength increases the width of OD columns in agreement with the experiment. Conference: Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009. Presentation Type: Poster Presentation Topic: Learning and plasticity Citation: Tsigankov D and Koulakov A (2009). The model of ocular dominance pattern formation in the presence of gradients of chemical labels.. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.neuro.10.2009.14.146 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 28 Aug 2009; Published Online: 28 Aug 2009. * Correspondence: Dmitry Tsigankov, Bernstein Center for Computational Neuroscience, Göttingen, Germany, dmitry@nld.ds.mpg.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Dmitry Tsigankov Alexei Koulakov Google Dmitry Tsigankov Alexei Koulakov Google Scholar Dmitry Tsigankov Alexei Koulakov PubMed Dmitry Tsigankov Alexei Koulakov Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Unsupervised learning of gain-field like interactions to achieve head-centered representations

Event Abstract Back to Event Unsupervised learning of gain-field like interactions to achieve head-centered representations Sebastian Thomas-Philipp1*, Frank Michler2 and Thomas Wachtler3 1 Ludwig-Maximilian-University, Computational Neuroscience, Department Biologie II, Germany 2 Philipps-Universitat, Neurophysik, Germany 3 Ludwig-Maximilians University Munich, Fakultat fur Biologie, Germany Movement planning based on visual information requires a transformation from a retina-centered into a head- or body-centered frame of reference. It has been shown that such transformations can be achieved via basis function networks [1,2]. We investigated whether basis functions for coordinate transformations can be learned by a biologically plausible neural network. We employed a model network of spiking neurons that learns invariant representations based on spatio-temporal stimulus correlations [3]. The model consists of a three-stage network of leaky integrate-and-fire neurons with biologically realistic conductances. The network has two input layers, corresponding to neurons representing the retinal image and neurons representing the direction of gaze. These inputs are represented in the map layer via excitatory or modulatory connections, respectively, that exhibit Hebbian-like spike-timing dependent plasticity. Neurons within the map layer are connected via short range lateral excitatory connections and unspecific lateral inhibition. We trained the network with stimuli corresponding to typical viewing situations when a visual scene is explored by saccadic eye movements, with gaze direction changing on a faster time scale than object positions in space. After learning, each neuron in the map layer was selective for a small subset of the stimulus space, with excitatory and modulatory connections adapted to achieve a topographic map of the inputs. Neurons in the output layer with a localized receptive field in the map layer were selective for positions in head-centered space, invariant to changes in retinal image due to changes in gaze direction. Our results show that coordinate transformations via basis function networks can be learned in a biologically plausible way by exploiting the spatio-temporal correlations between visual stimulation and eye position signals under natural viewing conditions. Acknowledgements:Supported by DFG Forschergruppe 560 and BCCN Munich

Processing visual stimuli using hierarchical spiking neural networks

Based on spiking neuron models and different receptive field models, hierarchical networks are proposed to process visual stimuli, in which multiple overlapped objects are represented by different orientation bars. The main purpose of this paper is to show that hierarchical spiking neural networks are able to segment the objects and bind their pixels to form shapes of objects using local excitatory lateral connections. The presented architecture is based on biologically inspired hierarchical structures. Segmentation is achieved through temporal correlation of neuron activities. The properties of these networks are demonstrated using a series of visual scenes representing different stimuli settings.