Network Computation in Neural Systems (NETWORK-COMP NEURAL)

Publications in this journal

• Article: Asynchronous inputs and NMDA conductances predict excitatory responses in the cortical-cA1 pathway of the hippocampus.

  Kit D Longden, David J Willshaw
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  ABSTRACT: In the hippocampus, CA1 place cells are driven by a substantial input from CA3. There is a second pathway to CA1 from the entorhinal cortex. The mode of action of cortex on CA1 through this pathway is not known. The pathway supports CA1 place field activity after CA3 has been lesioned, yet stimulation of the pathway in rat slices results in strong feedforward inhibition that prevents pyramidal cell action potentials. We use a detailed conductance-based model of this pathway to simulate the response to cortical stimulation in slice experiments and in vivo spatial exploration. We find that the presence of NMDA conductances enable CA1 pyramidal cells to integrate cortical inputs over a time scale longer than that which is effective in recruiting the inhibitory response that can suppress action potentials. We then show that this asynchronous response mode supports place field formation in response to experimentally constrained spatially modulated cortical activity. Within this model, the inclusion of GABAB conductances and the hyperpolarisation activated current I(h) reduces the strength of the GABAA inputs required to balance the excitatory inputs, and this facilitates place field formation by reducing variability in the inhibitory inputs.
  Network Computation in Neural Systems 01/2008; 18(4):299-325.
• Article: A model for the thick, thin and pale stripe organization of primate V2.

  Nicholas V Swindale
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  ABSTRACT: Models based on the idea of dimension reduction have been successful in describing the patterns of ocular dominance, spatial frequency and orientation preference found in primate V1. It is shown here that this approach can be extended to describe the organization of thick, thin and pale cytochrome oxidase stripes of primate V2 given an appropriately constructed stimulus space which includes a 3-valued variable which co-varies with color, orientation and disparity. The model successfully describes several aspects of V2 organization, including the fact that there are two pale stripes for each thick and thin stripe and the strong tendency for stripes to run perpendicular to the V1 border. In addition it predicts the presence of reversals in the direction of mapping of retinal eccentricity which should be more common in the pale stripes than elsewhere.
  Network Computation in Neural Systems 01/2008; 18(4):327-42.
• Source

  Available from: psu.edu

  Article: Common-input models for multiple neural spike-train data.

  Jayant E Kulkarni, Liam Paninski
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  ABSTRACT: Recent developments in multi-electrode recordings enable the simultaneous measurement of the spiking activity of many neurons. Analysis of such multineuronal data is one of the key challenge in computational neuroscience today. In this work, we develop a multivariate point-process model in which the observed activity of a network of neurons depends on three terms: (1) the experimentally-controlled stimulus; (2) the spiking history of the observed neurons; and (3) a hidden term that corresponds, for example, to common input from an unobserved population of neurons that is presynaptic to two or more cells in the observed population. We consider two models for the network firing-rates, one of which is computationally and analytically tractable but can lead to unrealistically high firing-rates, while the other with reasonable firing-rates imposes a greater computational burden. We develop an expectation-maximization algorithm for fitting the parameters of both the models. For the analytically tractable model the expectation step is based on a continuous-time implementation of the extended Kalman smoother, and the maximization step involves two concave maximization problems which may be solved in parallel. The other model that we consider necessitates the use of Monte Carlo methods for the expectation as well as maximization step. We discuss the trade-off involved in choosing between the two models and the associated methods. The techniques developed allow us to solve a variety of inference problems in a straightforward, computationally efficient fashion; for example, we may use the model to predict network activity given an arbitrary stimulus, infer a neuron's ring rate given the stimulus and the activity of the other observed neurons, and perform optimal stimulus decoding and prediction. We present several detailed simulation studies which explore the strengths and limitations of our approach.
  Network Computation in Neural Systems 01/2008; 18(4):375-407.
• Source

  Available from: uni-muenster.de

  Article: Local statistics of retinal optic flow for self-motion through natural sceneries.

  Dirk Calow, Markus Lappe
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  ABSTRACT: Image analysis in the visual system is well adapted to the statistics of natural scenes. Investigations of natural image statistics have so far mainly focused on static features. The present study is dedicated to the measurement and the analysis of the statistics of optic flow generated on the retina during locomotion through natural environments. Natural locomotion includes bouncing and swaying of the head and eye movement reflexes that stabilize gaze onto interesting objects in the scene while walking. We investigate the dependencies of the local statistics of optic flow on the depth structure of the natural environment and on the ego-motion parameters. To measure these dependencies we estimate the mutual information between correlated data sets. We analyze the results with respect to the variation of the dependencies over the visual field, since the visual motions in the optic flow vary depending on visual field position. We find that retinal flow direction and retinal speed show only minor statistical interdependencies. Retinal speed is statistically tightly connected to the depth structure of the scene. Retinal flow direction is statistically mostly driven by the relation between the direction of gaze and the direction of ego-motion. These dependencies differ at different visual field positions such that certain areas of the visual field provide more information about ego-motion and other areas provide more information about depth. The statistical properties of natural optic flow may be used to tune the performance of artificial vision systems based on human imitating behavior, and may be useful for analyzing properties of natural vision systems.
  Network Computation in Neural Systems 01/2008; 18(4):343-74.
• Source

  Available from: umd.edu

  Article: Estimating sparse spectro-temporal receptive fields with natural stimuli.

  Stephen V David, Nima Mesgarani, Shihab A Shamma
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  ABSTRACT: Several algorithms have been proposed to characterize the spectro-temporal tuning properties of auditory neurons during the presentation of natural stimuli. Algorithms designed to work at realistic signal-to-noise levels must make some prior assumptions about tuning in order to produce accurate fits, and these priors can introduce bias into estimates of tuning. We compare a new, computationally efficient algorithm for estimating tuning properties, boosting, to a more commonly used algorithm, normalized reverse correlation. These algorithms employ the same functional model and cost function, differing only in their priors. We use both algorithms to estimate spectro-temporal tuning properties of neurons in primary auditory cortex during the presentation of continuous human speech. Models estimated using either algorithm, have similar predictive power, although fits by boosting are slightly more accurate. More strikingly, neurons characterized with boosting appear tuned to narrower spectral bandwidths and higher temporal modulation rates than when characterized with normalized reverse correlation. These differences have little impact on responses to speech, which is spectrally broadband and modulated at low rates. However, we find that models estimated by boosting also predict responses to non-speech stimuli more accurately. These findings highlight the crucial role of priors in characterizing neuronal response properties with natural stimuli.
  Network Computation in Neural Systems 10/2007; 18(3):191-212.
• Source

  Available from: unr.edu

  Article: Seasonal variations in the color statistics of natural images.

  Michael A Webster, Yoko Mizokami, Shernaaz M Webster
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  ABSTRACT: We examined how the distribution of colors in natural images varies as the seasons change. Images of natural outdoor scenes were acquired at locations in the Western Ghats, India, during monsoon and winter seasons and in the Sierra Nevada, USA, from spring to fall. The images were recorded with an RGB digital camera calibrated to yield estimates of the L, M, and S cone excitations and chromatic and luminance contrasts at each pixel. These were compared across time and location and were analyzed separately for regions of earth and sky. Seasonal climate changes alter both the average color in scenes and how the colors are distributed around the average. Arid periods are marked by a mean shift toward the +L pole of the L vs. M chromatic axis and a rotation in the color distributions away from the S vs. LM chromatic axis and toward an axis of bluish-yellowish variation, both primarily due to changes in vegetation. The form of the change was similar at the two locations suggesting that the color statistics of natural images undergo a characteristic pattern of temporal variation. We consider the implications of these changes for models of both visual sensitivity and color appearance.
  Network Computation in Neural Systems 10/2007; 18(3):213-33.
• Article: A model for the thick, thin and pale stripe organization of primate V2.

  Nicholas Swindale
  [show abstract] [hide abstract]
  ABSTRACT: Models based on the idea of dimension reduction have been successful in describing the patterns of ocular dominance, spatial frequency and orientation preference found in primate V1. It is shown here that this approach can be extended to describe the organization of thick, thin and pale cytochrome oxidase stripes of primate V2 given an appropriately constructed stimulus space which includes a 3-valued variable which co-varies with color, orientation and disparity. The model successfully describes several aspects of V2 organization, including the fact that there are two pale stripes for each thick and thin stripe and the strong tendency for stripes to run perpendicular to the V1 border. In addition it predicts the presence of reversals in the direction of mapping of retinal eccentricity which should be more common in the pale stripes than elsewhere.
  Network Computation in Neural Systems 10/2007;
• Article: Hebbian learning in a model with dynamic rate-coded neurons: an alternative to the generative model approach for learning receptive fields from natural scenes.

  Fred H Hamker, Jan Wiltschut
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  ABSTRACT: Most computational models of coding are based on a generative model according to which the feedback signal aims to reconstruct the visual scene as close as possible. We here explore an alternative model of feedback. It is derived from studies of attention and thus, probably more flexible with respect to attentive processing in higher brain areas. According to this model, feedback implements a gain increase of the feedforward signal. We use a dynamic model with presynaptic inhibition and Hebbian learning to simultaneously learn feedforward and feedback weights. The weights converge to localized, oriented, and bandpass filters similar as the ones found in V1. Due to presynaptic inhibition the model predicts the organization of receptive fields within the feedforward pathway, whereas feedback primarily serves to tune early visual processing according to the needs of the task.
  Network Computation in Neural Systems 10/2007; 18(3):249-66.
• Article: Artists portray human faces with the Fourier statistics of complex natural scenes.

  Christoph Redies, Jan Hänisch, Marko Blickhan, Joachim Denzler
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  ABSTRACT: When artists portray human faces, they generally endow their portraits with properties that render the faces esthetically more pleasing. To obtain insight into the changes introduced by artists, we compared Fourier power spectra in photographs of faces and in portraits by artists. Our analysis was restricted to a large set of monochrome or lightly colored portraits from various Western cultures and revealed a paradoxical result. Although face photographs are not scale-invariant, artists draw human faces with statistical properties that deviate from the face photographs and approximate the scale-invariant, fractal-like properties of complex natural scenes. This result cannot be explained by systematic differences in the complexity of patterns surrounding the faces or by reproduction artifacts. In particular, a moderate change in gamma gradation has little influence on the results. Moreover, the scale-invariant rendering of faces in artists' portraits was found to be independent of cultural variables, such as century of origin or artistic techniques. We suggest that artists have implicit knowledge of image statistics and prefer natural scene statistics (or some other rules associated with them) in their creations. Fractal-like statistics have been demonstrated previously in other forms of visual art and may be a general attribute of esthetic visual stimuli.
  Network Computation in Neural Systems 10/2007; 18(3):235-48.
• Source

  Available from: psu.edu

  Article: Complex cell pooling and the statistics of natural images.

  Aapo Hyvärinen, Urs Köster
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  ABSTRACT: In previous work, we presented a statistical model of natural images that produced outputs similar to receptive fields of complex cells in primary visual cortex. However, a weakness of that model was that the structure of the pooling was assumed a priori and not learned from the statistical properties of natural images. Here, we present an extended model in which the pooling nonlinearity and the size of the subspaces are optimized rather than fixed, so we make much fewer assumptions about the pooling. Results on natural images indicate that the best probabilistic representation is formed when the size of the subspaces is relatively large, and that the likelihood is considerably higher than for a simple linear model with no pooling. Further, we show that the optimal nonlinearity for the pooling is squaring. We also highlight the importance of contrast gain control for the performance of the model. Our model is novel in that it is the first to analyze optimal subspace size and how this size is influenced by contrast normalization.
  Network Computation in Neural Systems 07/2007; 18(2):81-100.
• Source

  Available from: psu.edu

  Article: Invariant object recognition with trace learning and multiple stimuli present during training.

  S M Stringer, E T Rolls, J M Tromans
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  ABSTRACT: Over successive stages, the ventral visual system develops neurons that respond with view, size and position invariance to objects including faces. A major challenge is to explain how invariant representations of individual objects could develop given visual input from environments containing multiple objects. Here we show that the neurons in a 1-layer competitive network learn to represent combinations of three objects simultaneously present during training if the number of objects in the training set is low (e.g. 4), to represent combinations of two objects as the number of objects is increased to for e.g. 10, and to represent individual objects as the number of objects in the training set is increased further to for e.g. 20. We next show that translation invariant representations can be formed even when multiple stimuli are always present during training, by including a temporal trace in the learning rule. Finally, we show that these concepts can be extended to a multi-layer hierarchical network model (VisNet) of the ventral visual system. This approach provides a way to understand how a visual system can, by self-organizing competitive learning, form separate invariant representations of each object even when each object is presented in a scene with multiple other objects present, as in natural visual scenes.
  Network Computation in Neural Systems 07/2007; 18(2):161-87.
• Source

  Available from: usc.edu

  Article: Salamander locomotion-induced head movement and retinal motion sensitivity in a correlation-based motion detector model.

  Jeffrey R Begley, Michael A Arbib
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  ABSTRACT: We report on a computational model of retinal motion sensitivity based on correlation-based motion detectors. We simulate object motion detection in the presence of retinal slip caused by the salamander's head movements during locomotion. Our study offers new insights into object motion sensitive ganglion cells in the salamander retina. A sigmoidal transformation of the spatially and temporally filtered retinal image substantially improves the sensitivity of the system in detecting a small target moving in place against a static natural background in the presence of comparatively large, fast simulated eye movements, but is detrimental to the direction-selectivity of the motion detector. The sigmoid has insignificant effects on detector performance in simulations of slow, high contrast laboratory stimuli. These results suggest that the sigmoid reduces the system's noise sensitivity.
  Network Computation in Neural Systems 07/2007; 18(2):101-28.
• Source

  Available from: psu.edu

  Article: First-order and second-order statistical analysis of 3D and 2D image structure.

  S Kalkan, F Wörgötter, N Krüger
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  ABSTRACT: In the first part of this article, we analyze the relation between local image structures (i.e., homogeneous, edge-like, corner-like or texture-like structures) and the underlying local 3D structure (represented in terms of continuous surfaces and different kinds of 3D discontinuities) using range data with real-world color images. We find that homogeneous image structures correspond to continuous surfaces, and discontinuities are mainly formed by edge-like or corner-like structures, which we discuss regarding potential computer vision applications and existing assumptions about the 3D world. In the second part, we utilize the measurements developed in the first part to investigate how the depth at homogeneous image structures is related to the depth of neighbor edges. For this, we first extract the local 3D structure of regularly sampled points, and then, analyze the coplanarity relation between these local 3D structures. We show that the likelihood to find a certain depth at a homogeneous image patch depends on the distance between the image patch and a neighbor edge. We find that this dependence is higher when there is a second neighbor edge which is coplanar with the first neighbor edge. These results allow deriving statistically based prediction models for depth interpolation on homogeneous image structures.
  Network Computation in Neural Systems 07/2007; 18(2):129-60.
• Source

  Available from: srce.hr

  Article: Book Review

  P.-Y. Dietrich
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  ABSTRACT: The material for this review article can usefully be divided into two areas: firstly, two books on access; and, secondly, four on governance and management.
  Network Computation in Neural Systems 07/2007;
• Article: Randomness and variability of the neuronal activity described by the Ornstein-Uhlenbeck model.

  Lubomir Kostal, Petr Lansky, Cristina Zucca
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  ABSTRACT: Normalized entropy as a measure of randomness is explored. It is employed to characterize those properties of neuronal firing that cannot be described by the first two statistical moments. We analyze randomness of firing of the Ornstein-Uhlenbeck (OU) neuronal model with respect either to the variability of interspike intervals (coefficient of variation) or the model parameters. A new form of the Siegert's equation for first-passage time of the OU process is given. The parametric space of the model is divided into two parts (sub-and supra-threshold) depending upon the neuron activity in the absence of noise. In the supra-threshold regime there are many similarities of the model with the Wiener process model. The sub-threshold behavior differs qualitatively both from the Wiener model and from the supra-threshold regime. For very low input the firing regularity increases (due to increase of noise) cannot be observed by employing the entropy, while it is clearly observable by employing the coefficient of variation. Finally, we introduce and quantify the converse effect of firing regularity decrease by employing the normalized entropy.
  Network Computation in Neural Systems 04/2007; 18(1):63-75.
• Source

  Available from: cmu.edu

  Article: Evolution of visually guided behavior in artificial agents.

  Byron Boots, Surajit Nundy, Dale Purves
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  ABSTRACT: Recent work on brightness, color, and form has suggested that human visual percepts represent the probable sources of retinal images rather than stimulus features as such. Here we investigate the plausibility of this empirical concept of vision by allowing autonomous agents to evolve in virtual environments based solely on the relative success of their behavior. The responses of evolved agents to visual stimuli indicate that fitness improves as the neural network control systems gradually incorporate the statistical relationship between projected images and behavior appropriate to the sources of the inherently ambiguous images. These results: (1) demonstrate the merits of a wholly empirical strategy of animal vision as a means of contending with the inverse optics problem; (2) argue that the information incorporated into biological visual processing circuitry is the relationship between images and their probable sources; and (3) suggest why human percepts do not map neatly onto physical reality.
  Network Computation in Neural Systems 04/2007; 18(1):11-34.
• Article: Time and space are complementary encoding dimensions in the moth antennal lobe.

  Philipp Knüsel, Mikael A Carlsson, Bill S Hansson, Tim C Pearce, Paul F M J Verschure
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  ABSTRACT: The contribution of time to the encoding of information by the nervous system is still controversial. The olfactory system is one of the standard preparations where this issue is empirically investigated. For instance, output neurons of the antennal lobe or the olfactory bulb display odor stimulus induced temporal modulations of their firing rate at a scale of hundreds of milliseconds. The role of these temporal patterns in the encoding of odor stimuli, however, is not yet known. Here, we use optical imaging of the projection neurons of the moth antennal lobe to address this question. First, we present a biophysically derived model that provides an accurate description of the calcium response of projection neurons. On the basis of this model, we subsequently show that the calcium response of the projection neurons displays a stimulus specific temporal structure. Finally, we demonstrate that an encoding scheme that includes this temporal information boosts classification performance by 60% as compared to a purely spatial encoding. Although the putative role of combinatorial spatio-temporal encoding strategies has been the subject of debate, our results for the first time establish quantitatively that such an encoding strategy is used by the insect brain.
  Network Computation in Neural Systems 04/2007; 18(1):35-62.
• Source

  Available from: psu.edu

  Article: Entorhinal cortex grid cells can map to hippocampal place cells by competitive learning.

  Edmund T Rolls, Simon M Stringer, Thomas Elliot
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  ABSTRACT: 'Grid cells' in the dorsocaudal medial entorhinal cortex (dMEC) are activated when a rat is located at any of the vertices of a grid of equilateral triangles covering the environment. dMEC grid cells have different frequencies and phase offsets. However, cells in the dentate gyrus (DG) and hippocampal area CA3 of the rodent typically display place fields, where individual cells are active over only a single portion of the space. In a model of the hippocampus, we have shown that the connectivity from the entorhinal cortex to the dentate granule cells could allow the dentate granule cells to operate as a competitive network to recode their inputs to produce sparse orthogonal representations, and this includes spatial pattern separation. In this paper we show that the same computational hypothesis can account for the mapping of EC grid cells to dentate place cells. We show that the learning in the competitive network is an important part of the way in which the mapping can be achieved. We further show that incorporation of a short term memory trace into the associative learning can help to produce the relatively broad place fields found in the hippocampus.
  Network Computation in Neural Systems 01/2007; 17(4):447-65.
• Source

  Available from: psu.edu

  Article: Theoretical understanding of the early visual processes by data compression and data selection.

  Li Zhaoping
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  ABSTRACT: Early vision is best understood in terms of two key information bottlenecks along the visual pathway -- the optic nerve and, more severely, attention. Two effective strategies for sampling and representing visual inputs in the light of the bottlenecks are (1) data compression with minimum information loss and (2) data deletion. This paper reviews two lines of theoretical work which understand processes in retina and primary visual cortex (V1) in this framework. The first is an efficient coding principle which argues that early visual processes compress input into a more efficient form to transmit as much information as possible through channels of limited capacity. It can explain the properties of visual sampling and the nature of the receptive fields of retina and V1. It has also been argued to reveal the independent causes of the inputs. The second theoretical tack is the hypothesis that neural activities in V1 represent the bottom up saliencies of visual inputs, such that information can be selected for, or discarded from, detailed or attentive processing. This theory links V1 physiology with pre-attentive visual selection behavior. By making experimentally testable predictions, the potentials and limitations of both sets of theories can be explored.
  Network Computation in Neural Systems 01/2007; 17(4):301-34.