An Oscillatory Neural Model of Multiple Object Tracking
Yakov Kazanovich1 and Roman Borisyuk1,2
1
Institute of Mathematical Problems in Biology, Pushchino, Russia
2
Centre of Theoretical and Computational Neuroscience, University of Plymouth, UK
yakov_k@impb.psn.ru, rborisyuk@plymouth.ac.uk
Multiple object tracking (MOT) is a paradigm in psychological study of object-based
attentional selection. In the canonical MOT experiments [1], a subject viewed the display with
several identical objects moving independently and unpredictably without overlap. In these
experiments it has been shown that subjects can simultaneously track up to five targets.
Biologically plausible oscillatory neural network model of MOT has been developed.
The model design is based on our earlier published basic Attention Model with a Central
Oscillator (AMCO) [2-3]. AMCO contains a one-layer network of locally coupled oscillators,
the so-called Peripheral Oscillators (POs), whose dynamics is controlled by a special Central
Oscillator (CO) through global feedforward and feedback connections. Each PO receives an
external signal from a topographically identical pixel of the visual field. The incoming signal
to a PO, if it is generated by a pixel belonging to an object, activates the PO and determines
the value of its natural frequency which is a monotonic function of the contrast of the pixel
relative to the background. Each object is represented in the network by a connected set of
active POs. An object is considered as being included in the focus of attention if all POs that
represent this object work coherently with the CO. It has been shown in our previous work
that using the principles of synchronization and resonance in the interaction between the
oscillators, the model is able to select into the attention focus the most salient object in the
visual field or to select objects one by one with preferences to more salient objects.
Our oscillatory model of MOT with k targets is designed as a network consisting of k
copies of AMCO with each copy of AMCO tracking one particular object independently of
others. Dynamical object tracking represents a difficult task for conventional connectionist
models of attention since in this case the adaptation of connection strengths must be repeated
for any new location of an object included in the attention focus. We overcome this difficulty
by using an oscillatory neural network with constant values of connection strengths. To
prevent the situation when the same object is simultaneously selected and tracked by different
AMCOs we use strong synchronizing couplings between POs that occupy the same location
but belong to different AMCOs. Hence all POs representing one object will work as a
synchronous assembly. We also introduce desynchronizing connections between all
CO i belonging to different AMCOs (i=1, 2, .., k), which is aimed to prevent the
synchronization of different CO i with the same synchronous assembly of POs. As a result,
different targets will be coded in the network by non-coherent oscillatory activity of different
assemblies of POs. The connection architecture between AMCOs reflects the columnar neural
organization of neural connections in the visual cortex.
We show that the results of model simulations are in a good correspondence with
experimental findings. We discuss different applications of the model including modeling of
divided attention.
References
[1] Pylyshin, Z. W., and Storm, R. W. (1988) Tracking multiple indepenedent targets: evidence for a
parallel tracking mechanism. Spatial Vision, 3, 179-197.
[2] Kazanovich, Y., and Borisyuk, R. (2002) Object selection by an oscillatory neural network.
BioSystems, 67, 103-111.
[3] Borisyuk, R., and Kazanovich, Y. (2004) Oscillatory model of attention-guided object selection
and novelty detection. Neural Networks (in press).