Change search
ReferencesLink to record
Permanent link

Direct link
Signature of an anticipatory response in area VI as modeled by a probabilistic model and a spiking neural network
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm Brain Institute, Karolinska Institute, Sweden .ORCID iD: 0000-0002-1213-4239
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm Brain Institute, Karolinska Institute, Sweden .
2014 (English)In: 2014 International Joint Conference on Neural Networks (IJCNN), IEEE , 2014, 3205-3212 p.Conference paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE , 2014. 3205-3212 p.
National Category
Bioinformatics (Computational Biology)
URN: urn:nbn:se:kth:diva-157983DOI: 10.1109/IJCNN.2014.6889847ScopusID: 2-s2.0-84908472205ISBN: 978-147991484-5OAI: diva2:773617
2014 International Joint Conference on Neural Networks, IJCNN 2014, 6 July 2014 through 11 July 2014, Beijing, China

QC 20141219

Available from: 2014-12-19 Created: 2014-12-18 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
Bioinformatics (Computational Biology)

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: 22 hits
ReferencesLink to record
Permanent link

Direct link