dc.contributor.author
Schmuker, Michael
dc.contributor.author
Häusler, Chris
dc.contributor.author
Nawrot, Martin Paul
dc.date.accessioned
2018-06-08T02:57:40Z
dc.date.available
2013-03-07
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/14204
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-18401
dc.description.abstract
A large body of behavioral discrimination experiments demonstrates that the
honeybee can quickly and reliably identify odorant stimuli [1]. The neuronal
circuits involved in odor discrimination are well described on the structural
level. Here, we decompose the insect olfactory pathway into local circuits
that represent successive processing stages. We infer their specific
functional role in odor discrimination using spiking neuronal network models,
measuring their contribution to the performance of a neuronal implementation
of a probabilistic classifier, which we train in a supervised manner [2,3]. In
the insect olfactory system, primary receptor neurons project to the antennal
lobe (AL). The AL is organized in compartments called glomeruli. Each
glomerulus receives input only from one type of receptor neurons. Each odorant
activates many different receptor types, inducing a spatial pattern across the
AL. Strong lateral inhibitory interactions between glomeruli make an impact on
information processing [4]. We illustrate how lateral inhibition enhances
linear separability of stimulus patterns by increasing contrast between input
dimensions. From the glomeruli, uniglomerular projection neurons (PNs) send
their axons to Kenyon cells (KCs) in the mushroom body, a central brain
structure where stimulus associations are being formed from multimodal input
[5]. Connections between PNs and KCs are realized within small local
microcircuits, where PNs and KCs interact with an inhibitory cell population
[6]. We show how these microcircuits can create non-linear transformations of
the input patterns. Moreover, this stage is anatomically characterized by a
massive 'fan-out' of connections: In the honeybee, about 950 PNs synapse onto
about 100.000 KCs. Taken together, this organization resembles the working
principle of a support vector machine, transforming data which is not linearly
separable into a higher-dimensional representation, in which linear separation
is possible. At each stage of the model, we use a two-dimensional toy data set
to illustrate the classification problem and the processing principle.
Currently, we test the performance of the neuronal classifier on benchmark
data sets and a real-world odorant data set [7]. In addition, we test
implementations of our models on neuromorphic hardware. This is a first step
towards implementations of fast and powerful neuromorphic classification
devices, applicable to a wide range of sensor data. References: [1] Giurfa
(2007) J Comp Physiol A 193:801-24. [2] Fusi, Asaad, Miller and Wang (2007)
Neuron 54:319-333. [3] Soltani and Wang (2010) Nat Neurosci 13:112-9. [4]
Wilson and Mainen (2006) Annu Rev Neurosci 29:163-201. [5] Heisenberg (1998)
Learn Mem 5:1-10. [6] Ganeshina and Menzel (2001) J Comp Neurol 437:335-349.
[7] Schmuker and Schneider (2007) PNAS 104:20285-20289.
de
dc.rights.uri
http://www.fu-berlin.de/sites/refubium/rechtliches/Nutzungsbedingungen
dc.subject.ddc
500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie
dc.title
Insect olfactory microcircuits for better neuromorphic classification devices
dc.type
Wissenschaftlicher Artikel
dc.title.subtitle
Abstract for a talk presented at the ESF-EMBO symposium: Functional
Neurobiology in Minibrains, Hotel Eden Roc, Oct 17-22 2010, Sant Feliu de
Guíxols, Spain.
dcterms.bibliographicCitation.url
http://www.esf.org/index.php?id=6460
refubium.affiliation
Biologie, Chemie, Pharmazie
de
refubium.mycore.fudocsId
FUDOCS_document_000000016253
refubium.resourceType.isindependentpub
no
refubium.mycore.derivateId
FUDOCS_derivate_000000002330
dcterms.accessRights.openaire
open access