Self-organization in the olfactory system: one shot odor recognition in insects

Nowotny, Thomas; Huerta, Ramón; Abarbanel, Henry D. I.; Rabinovich, M. I.

doi:10.1007/s00422-005-0019-7

Cited by 83 publications

(72 citation statements)

References 56 publications

(60 reference statements)

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“…Therefore, the activity of the Kenyon cells KCs (see Fig. 2) appears to be heavily regulated and has been shown to generate sparse activity in Honeybees and Locust [94,95,96], which is consistent with the overwhelming predictions of associate memory and pattern recognition models [97,98,99,100,101,102,103,104,105,106,107].…”

Section: The Formation Of Memories In the Mushroom Bodiessupporting

confidence: 70%

“…The theoretical basis for discrimination on a large screen to discriminate more easily was already proposed by Thomas Cover [140] and later within the framework of support vector machines [141]. In addition, sparse code is a very useful component to achieving a powerful pattern recognition device as observed in the MBs and as already mentioned the theoretical support for sparse code is extensive [97,98,99,100,101,102,103,104,105,106,107].…”

Section: Information Conservation In the Mushroom Bodiesmentioning

confidence: 97%

“…The evidence of sparse code in the Calyx is found in the locust [95,96] and the honeybee [94,95]. The prevailing theoretical idea to make the code stable over time from the AL to the MB is using forward inhibition [142,106,96]. In what follows we will assume that neural circuits are placed in an stable sparse mode.…”

Section: Information Conservation In the Mushroom Bodiesmentioning

confidence: 99%

“…4 where small variations of the probability of activation of AL neurons can lead to a very sharp change in the MBs [145]. This unstability makes necessary to have gain control mechanisms to regulate the sparse activity as proposed in [142,106,96,146] via forward inhibition or by synaptic plasticity [147]. The regulation of sparseness via plasticity from the AL to the MB is an unlikely mechanism to generate sparseness because it actually reduces the information content on the KCs [148].…”

Section: Information Conservation In the Mushroom Bodiesmentioning

confidence: 99%

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Learning pattern recognition and decision making in the insect brain

Huerta¹

2013

AIP Conference Proceedings

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Abstract. We revise the current model of learning pattern recognition in the Mushroom Bodies of the insects using current experimental knowledge about the location of learning, olfactory coding and connectivity. We show that it is possible to have an efficient pattern recognition device based on the architecture of the Mushroom Bodies, sparse code, mutual inhibition and Hebbian leaning only in the connections from the Kenyon cells to the output neurons. We also show that despite the conventional wisdom that believes that artificial neural networks are the bioinspired model of the brain, the Mushroom Bodies actually resemble very closely Support Vector Machines (SVMs). The derived SVM learning rules are situated in the Mushroom Bodies, are nearly identical to standard Hebbian rules, and require inhibition in the output. A very particular prediction of the model is that random elimination of the Kenyon cells in the Mushroom Bodies do not impair the ability to recognize odorants previously learned.

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Section: The Formation Of Memories In the Mushroom Bodiessupporting

confidence: 70%

Section: Information Conservation In the Mushroom Bodiesmentioning

confidence: 97%

Section: Information Conservation In the Mushroom Bodiesmentioning

confidence: 99%

Section: Information Conservation In the Mushroom Bodiesmentioning

confidence: 99%

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Learning pattern recognition and decision making in the insect brain

Huerta¹

2013

AIP Conference Proceedings

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“…Odour mixtures are represented as a non-trivial combination of constituent odours' representations, and formation of long-term memories associated with such odour mixtures has been shown to induce volume changes in glomeruli indicating a cross-inhibitory effect between neural codings [2]. The modelling will also consider how mechanisms might implement known classification rules, such as in the models of insect olfactory classification by Huerta et al [3] and Nowotny et al [4].In this study, we present some benchmarking results. We perform performance and scalability tests on an NVI-DIA Tesla C2070 GPU with an Intel ® Xeon(R) E5-2609 CPU running Ubuntu 12.04 LTS.…”

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confidence: 99%

A modelling framework for the olfactory system of the honeybee using GeNN (GPU enhanced Neuronal Network simulation environment)

Yavuz

Nowotny

2014

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We present an example implementation of a minimal model of honeybee olfactory system on massively parallel GPU hardware using GPU-specific code generation with GeNN[1]. This will be a first step to provide a physiologically coherent model of the honeybee olfactory system to be implemented in real time on fying autonomous robots for the "Green Brain Project".The "Green Brain Project" will combine computational neuroscience modelling, learning and decision theory, modern parallel computing methods and robotics with data from state-of-the-art neurobiological experiments on cognition in the honeybee Apis mellifera, to build and deploy a modular model of the honeybee brain describing detection, classification and learning in the olfactory and optic pathways as well as multi-sensory integration across these sensory modalities. Unlike other brain models, which use expensive traditional supercomputing resources,the 'Green Brain' will be implemented on massively parallel, but affordable GPU technology. The 'Green Brain' will be deployed for the real-time control of a flying robot able to sense and act autonomously. This robot testbed will be used to demonstrate the development of new biomimetic control algorithms for artificial intelligence and robotics applications.The objective for modelling olfaction in the "Green Brain Project" will extend previous attempts to model the antennal lobes and their constituent glomeruli (which encode olfactory cues), the projection neurons and the mushroom bodies. Odours are known to have a distributed representation in the antennal lobe, encoded as differential activation levels of glomerular populations. Odour mixtures are represented as a non-trivial combination of constituent odours' representations, and formation of long-term memories associated with such odour mixtures has been shown to induce volume changes in glomeruli indicating a cross-inhibitory effect between neural codings [2]. The modelling will also consider how mechanisms might implement known classification rules, such as in the models of insect olfactory classification by Huerta et al. [3] and Nowotny et al. [4].In this study, we present some benchmarking results. We perform performance and scalability tests on an NVI-DIA Tesla C2070 GPU with an Intel ® Xeon(R) E5-2609 CPU running Ubuntu 12.04 LTS. Preliminary results show that as the network size increases, GPU simulations outperform CPU simulations. We suggest that implementation of realistic neural networks using GPUs will make real-time simulations of natural-like perception more efficient which would then provide a better understanding of sensory processing.

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