[Comp-neuro] four neural modeling articles on visual and spatial navigation

Stephen Grossberg steve at cns.bu.edu
Tue Jul 21 17:37:31 CEST 2009

The following articles about visual and spatial navigation are now  
available at http://cns.bu.edu/~steve :

Elder, D., Grossberg, S., and Mingolla, M
A neural model of visually guided steering, obstacle avoidance, and  
route selection.
Journal of Experimental Psychology: Human Perception & Performance, in  

A neural model is developed to explain how humans can approach a goal  
object on foot while steering around obstacles to avoid collisions in  
a cluttered environment. The model uses optic flow from a 3D virtual  
reality environment to determine the position of objects based on  
motion discontinuities, and computes heading direction, or the  
direction of self-motion, from global optic flow. The cortical  
representation of heading interacts with the representations of a goal  
and obstacles such that the goal acts as an attractor of heading,  
while obstacles act as repellers. In addition the model maintains  
fixation on the goal object by generating smooth pursuit eye  
movements. Eye rotations can distort the optic flow field,  
complicating heading perception, and the model uses extraretinal  
signals to correct for this distortion and accurately represent  
heading. The model explains how motion processing mechanisms in  
cortical areas MT, MST, and posterior parietal cortex can be used to  
guide steering. The model quantitatively simulates human  
psychophysical data about visually-guided steering, obstacle  
avoidance, and route selection.
Key Words: Heading perception, steering, optic flow, obstacle, goal,  
pursuit eye movement, gain fields, peak shift, V2, MT, MST, PPC, LIP

Browning, A., Grossberg, S., and Mingolla, M.
A neural model of how the brain computes heading from optic flow in  
realistic scenes.
Cognitive Psychology, in press.

Visually-based navigation is a key competence during spatial  
cognition. Animals avoid obstacles and approach goals in novel  
cluttered environments using optic flow to compute heading with  
respect to the environment. Most navigation models try either explain  
data, or to demonstrate navigational competence in real-world  
environments without regard to behavioral and neural substrates. The  
current article develops a model that does both. The ViSTARS neural  
model describes interactions among neurons in the primate  
magnocellular pathway, including V1, MT+, and MSTd.  Model outputs are  
quantitatively similar to human heading data in response to complex  
natural scenes.  The model estimates heading to within 1.5° in random  
dot or photo-realistically rendered scenes, and within 3° in video  
streams from driving in real-world environments.  Simulated rotations  
of less than 1 degree per second do not affect heading estimates, but  
faster simulated rotation rates do, as in humans.  The model is part  
of a larger navigational system that identifies and tracks objects  
while navigating in cluttered environments.
Key Words:  navigation, optic flow, heading, motion, visual cortex,  
V1, MT, MST,  neural model

Browning, A., Grossberg, S., and Mingolla, M.
Cortical dynamics of navigation and steering in natural scenes: Motion- 
based object segmentation, heading, and obstacle avoidance.
Neural Networks, in press.

Visually guided navigation through a cluttered natural scene is a  
challenging problem that animals and humans accomplish with ease. The  
ViSTARS neural model proposes how primates use motion information to  
segment objects and determine heading for purposes of goal approach  
and obstacle avoidance in response to video inputs from real and  
virtual environments. The model produces trajectories similar to those  
of human navigators. It does so by predicting how computationally  
complementary processes in cortical areas MT-/MSTv and MT+/MSTd  
compute object motion for tracking and self-motion for navigation,  
respectively. The model retina responds to transients in the input  
stream. Model V1 generates a local speed and direction estimate. This  
local motion estimate is ambiguous due to the neural aperture problem.  
Model MT+ interacts with MSTd via an attentive feedback loop to  
compute accurate heading estimates in MSTd that quantitatively  
simulate properties of human heading estimation data. Model MT-  
interacts with MSTv via an attentive feedback loop to compute accurate  
estimates of speed, direction and position of moving objects. This  
object information is combined with
heading information to produce steering decisions wherein goals behave  
like attractors and obstacles behave like repellers. These steering  
decisions lead to navigational trajectories that closely match human  
Key Words: Optic flow, navigation, MT, MST, motion segmentation,  
object tracking

Grossberg, S.
Beta oscillations and hippocampal place cell learning during  
exploration of novel environments.
Hippocampus, in press.

The functional role of synchronous oscillations in various brain  
processes has attracted a lot of
experimental interest. Berke et al. (2008) reported beta oscillations  
during the learning of
hippocampal place cell receptive fields in novel environments. Such  
place cell selectivity can
develop within seconds to minutes, and can remain stable for months.  
Paradoxically, beta power
was very low during the first lap of exploration, grew to full  
strength as a mouse traversed a lap
for the second and third times, and became low again after the first  
two minutes of exploration.
Beta oscillation power also correlated with the rate at which place  
cells became spatially
selective, and not with theta oscillations. These beta oscillation  
properties are explained by a
neural model of how place cell receptive fields may be learned and  
stably remembered as
spatially selective categories due to feedback interactions between  
entorhinal cortex and
hippocampus. This explanation allows the learning of place cell  
receptive fields to be understood
as a variation of category learning processes that take place in many  
brain systems, and
challenges hippocampal models in which beta oscillations and place  
cell stability cannot be
Key Words: grid cells, category learning, spatial navigation, adaptive  
resonance theory, entorhinal cortex

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