| 1. |
- Kaplan, Bernhard A., et al.
(författare)
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Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network
- 2014
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Ingår i: PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN). - : IEEE. - 9781479914845 ; , s. 3205-3212
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Konferensbidrag (refereegranskat)abstract
- As it is confronted to inherent neural delays, how does the visual system create a coherent representation of a rapidly changing environment? In this paper, we investigate the role of motion-based prediction in estimating motion trajectories compensating for delayed information sampling. In particular, we investigate how anisotropic diffusion of information may explain the development of anticipatory response as recorded in a neural populations to an approaching stimulus. We validate this using an abstract probabilistic framework and a spiking neural network (SNN) model. Inspired by a mechanism proposed by Nijhawan [1], we first use a Bayesian particle filter framework and introduce a diagonal motion-based prediction model which extrapolates the estimated response to a delayed stimulus in the direction of the trajectory. In the SNN implementation, we have used this pattern of anisotropic, recurrent connections between excitatory cells as mechanism for motion-extrapolation. Consistent with recent experimental data collected in extracellular recordings of macaque primary visual cortex [2], we have simulated different trajectory lengths and have explored how anticipatory responses may be dependent on the information accumulated along the trajectory. We show that both our probabilistic framework and the SNN model can replicate the experimental data qualitatively. Most importantly, we highlight requirements for the development of a trajectory-dependent anticipatory response, and in particular the anisotropic nature of the connectivity pattern which leads to the motion extrapolation mechanism.
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| 2. |
- Kaplan, Bernhard, 1984-, et al.
(författare)
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Anisotropic connectivity implements motion-basedprediction in a spiking neural network
- 2013
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Ingår i: Frontiers in Computational Neuroscience. - : Frontiers Media SA. - 1662-5188.
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Tidskriftsartikel (refereegranskat)abstract
- Predictive coding hypothesizes that the brain explicitly infers upcoming sensory inputto establish a coherent representation of the world. Although it is becoming generallyaccepted, it is not clear on which level spiking neural networks may implementpredictive coding and what function their connectivity may have. We present a networkmodel of conductance-based integrate-and-fire neurons inspired by the architectureof retinotopic cortical areas that assumes predictive coding is implemented throughnetwork connectivity, namely in the connection delays and in selectiveness for the tuningproperties of source and target cells. We show that the applied connection pattern leadsto motion-based prediction in an experiment tracking a moving dot. In contrast to ourproposed model, a network with random or isotropic connectivity fails to predict the pathwhen the moving dot disappears. Furthermore, we show that a simple linear decodingapproach is sufficient to transform neuronal spiking activity into a probabilistic estimatefor reading out the target trajectory.
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| 3. |
- Oskarsson, Magnus, et al.
(författare)
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Nightvision Based on a Biological Model
- 2015
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Ingår i: Biologically Inspired Computer Vision: Fundamentals and Applications. - Weinheim, Germany : Wiley-VCH Verlag GmbH & Co. KGaA. - 9783527412648 - 9783527680863 ; , s. 377-404
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Bokkapitel (refereegranskat)abstract
- The colors and contrasts of the nocturnal world are just as rich as those found in the diurnal world. This chapter describes a recent biomimetic advance inspired by the visual systems of nocturnal insects. Since the underlying principles of both animal and camera vision are similar, it is natural to try to mimic the neural processes of nocturnal animals in order to construct efficient computer vision algorithms. The chapter explains both the underlying biological principles and the computer vision approach in detail. It discusses the specific characteristics of different types of noise that are present in digital images and relate them to their biological counterparts. The “dark noise” in photoreceptors is described. This thermal effect is also present in digital sensors and is called dark current noise. In order to produce a digital image, the electrical signal is quantized into a digital signal with a fixed number of bits.
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