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- Fonollosa, J., et al.
(författare)
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Biologically inspired computation for chemical sensing
- 2011
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Ingår i: Procedia Comput. Sci.. - : Elsevier BV. ; , s. 226-227
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Konferensbidrag (refereegranskat)abstract
- In this paper, we present how the achievements related to NEUROCHEM project (FP7, Bio-ICT, Grant number 216916) have increased the understanding of the olfactory system and helped to develop novel computing architectures and models for chemical sensing. We present the developed computational models of the olfactory pathway of vertebrates and insects to capture the mechanisms that underlie their chemical information processing abilities. To mimic the biological olfactory epithelium a large scale chemical sensor array has been developed.We also present a robot that demonstrates the chemical search task as a direct application of the computing paradigms extracted.
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| 6. |
- 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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- Marco, S., et al.
(författare)
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A biomimetic approach to machine olfaction, featuring a very large-scale chemical sensor array and embedded neuro-bio-inspired computation
- 2014
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Ingår i: Microsystem Technologies. - : Springer Science and Business Media LLC. - 0946-7076 .- 1432-1858. ; 20:4-5, s. 729-742
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Tidskriftsartikel (refereegranskat)abstract
- Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, in a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy and efficient combinatorial coding, with unmatched chemical information processing mechanisms. The last decade has seen important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. The EU-funded Project NEUROCHEM (Bio-ICT-FET- 216916) developed novel computing paradigms and biologically motivated artefacts for chemical sensing, taking its inspiration from the biological olfactory pathway. To demonstrate this approach, a biomimetic demonstrator has been built that features a very large-scale sensor array (65,536 elements) using conducting polymer technology which mimics the olfactory receptor neuron layer. It implements derived computational neuroscience algorithms in an embedded system that interfaces the chemical sensors and processes their signals in real-time. This embedded system integrates abstracted computational models of the main anatomic building blocks in the olfactory pathway: the olfactory bulb, and olfactory cortex in vertebrates (respectively, antennal lobe and mushroom bodies in the insect). For implementation in the embedded processor, an abstraction phase has been carried out in which their processing capabilities are captured by algorithmic solutions implemented in software. Finally, the algorithmic models are tested in mixed chemical plumes with an odour robot having navigation capabilities.
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| 10. |
- Marco, S., et al.
(författare)
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Biologically inspired large scale chemical sensor arrays and embedded data processing
- 2013
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Ingår i: Smart Sensors, Actuators, And Mems VI. - : SPIE - International Society for Optical Engineering. - 9780819495600 ; , s. 876303-
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Konferensbidrag (refereegranskat)abstract
- Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, to a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy, an efficient combinatorial coding along with unmatched chemical information processing mechanisms. The last decade has witnessed important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. EU Funded Project NEUROCHEM (Bio-ICT-FET- 216916) has developed novel computing paradigms and biologically motivated artefacts for chemical sensing taking inspiration from the biological olfactory pathway. To demonstrate this approach, a biomimetic demonstrator has been built featuring a large scale sensor array (65K elements) in conducting polymer technology mimicking the olfactory receptor neuron layer, and abstracted biomimetic algorithms have been implemented in an embedded system that interfaces the chemical sensors. The embedded system integrates computational models of the main anatomic building blocks in the olfactory pathway: The olfactory bulb, and olfactory cortex in vertebrates (alternatively, antennal lobe and mushroom bodies in the insect). For implementation in the embedded processor an abstraction phase has been carried out in which their processing capabilities are captured by algorithmic solutions. Finally, the algorithmic models are tested with an odour robot with navigation capabilities in mixed chemical plumes.
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