Change search
ReferencesLink to record
Permanent link

Direct link
A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-1213-4239
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
2014 (English)In: Frontiers in Neural Circuits, ISSN 1662-5110, Vol. 8, no Feb, 5- p.Article in journal (Refereed) Published
Abstract [en]

Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin-Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian-Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian-Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.

Place, publisher, year, edition, pages
2014. Vol. 8, no Feb, 5- p.
Keyword [en]
BCPNN, Concentration invariance, Large-scale neuromorphic systems, Olfactory bulb, Pattern recognition, Pattern rivalry, Piriform cortex, Spiking neural network
National Category
Bioinformatics (Computational Biology) Neurology
URN: urn:nbn:se:kth:diva-142800DOI: 10.3389/fncir.2014.00005ISI: 000332714100001ScopusID: 2-s2.0-84893603482OAI: diva2:704719
EU, FP7, Seventh Framework Programme, 237955 FP7-269921 FP7-216916

QC 20140313

Available from: 2014-03-13 Created: 2014-03-12 Last updated: 2015-05-04Bibliographically approved
In thesis
1. Modeling prediction and pattern recognition in the early visual and olfactory systems
Open this publication in new window or tab >>Modeling prediction and pattern recognition in the early visual and olfactory systems
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Our senses are our mind's window to the outside world and determine how we perceive our environment.Sensory systems are complex multi-level systems that have to solve a multitude of tasks that allow us to understand our surroundings.However, questions on various levels and scales remain to be answered ranging from low-level neural responses to behavioral functions on the highest level.Modeling can connect different scales and contribute towards tackling these questions by giving insights into perceptual processes and interactions between processing stages.In this thesis, numerical simulations of spiking neural networks are used to deal with two essential functions that sensory systems have to solve: pattern recognition and prediction.The focus of this thesis lies on the question as to how neural network connectivity can be used in order to achieve these crucial functions.The guiding ideas of the models presented here are grounded in the probabilistic interpretation of neural signals, Hebbian learning principles and connectionist ideas.The main results are divided into four parts.The first part deals with the problem of pattern recognition in a multi-layer network inspired by the early mammalian olfactory system with biophysically detailed neural components.Learning based on Hebbian-Bayesian principles is used to organize the connectivity between and within areas and is demonstrated in behaviorally relevant tasks.Besides recognition of artificial odor patterns, phenomena like concentration invariance, noise robustness, pattern completion and pattern rivalry are investigated.It is demonstrated that learned recurrent cortical connections play a crucial role in achieving pattern recognition and completion.The second part is concerned with the prediction of moving stimuli in the visual system.The problem of motion-extrapolation is studied using different recurrent connectivity patterns.The main result shows that connectivity patterns taking the tuning properties of cells into account can be advantageous for solving the motion-extrapolation problem.The third part focuses on the predictive or anticipatory response to an approaching stimulus.Inspired by experimental observations, particle filtering and spiking neural network frameworks are used to address the question as to how stimulus information is transported within a motion sensitive network.In particular, the question if speed information is required to build up a trajectory dependent anticipatory response is studied by comparing different network connectivities.Our results suggest that in order to achieve a dependency of the anticipatory response to the trajectory length, a connectivity that uses both position and speed information seems necessary.The fourth part combines the self-organization ideas from the first part with motion perception as studied in the second and third parts.There, the learning principles used in the olfactory system model are applied to the problem of motion anticipation in visual perception.Similarly to the third part, different connectivities are studied with respect to their contribution to anticipate an approaching stimulus.The contribution of this thesis lies in the development and simulation of large-scale computational models of spiking neural networks solving prediction and pattern recognition tasks in biophysically plausible frameworks.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. xiv, 185 p.
TRITA-CSC-A, ISSN 1653-5723 ; 2015:10
spiking neural networks, pattern recognition, self-organization, prediction, anticipation, visual system, olfactory system, modeling
National Category
Bioinformatics (Computational Biology)
Research subject
Computer Science
urn:nbn:se:kth:diva-166127 (URN)978-91-7595-532-2 (ISBN)
Public defence
2015-05-27, F3, Lindstedtsv. 26, KTH, Stockholm, 10:00 (English)
EU, FP7, Seventh Framework Programme

QC 20150504

Available from: 2015-05-04 Created: 2015-05-02 Last updated: 2015-05-04Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Kaplan, Bernhard A.Lansner, Anders
By organisation
Computational Biology, CB
In the same journal
Frontiers in Neural Circuits
Bioinformatics (Computational Biology)Neurology

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 90 hits
ReferencesLink to record
Permanent link

Direct link