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R A Andersen

Publications and source records attributed to R A Andersen.

151 records · Page 9Linked to original sources
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The effect of transiency on perceived velocity of visual patterns: a case of "temporal capture".

We measured the points of subjective equality of velocity for dynamic unidirectionally moving random-dot patterns with different amounts of transiency. The transiency was changed by varying the time a dot would move before being randomly replotted within the stimulus. The perceived velocity of patterns moving at intermediate velocities (4 or 6 deg/sec) was increased by decreasing the point lifetime while no speedup was observed at high velocities (12 deg/sec). A speedup was also observed when a few stationary points of short lifetime were introduced into a stimulus. The non-directional transiency generated by these flickering points seems to be captured by the moving pattern and biases the velocity estimate. We term this phenomenon "temporal capture". The results are in agreement with models that determine velocity by comparing the activity in lower and higher temporal frequency channels. Our stimuli would selectively increase activity in high temporal frequency channels and thus lead to an increase in perceived velocity.

Humans↗
PubMedDate not supplied

Neural responses to velocity gradients in macaque cortical area MT.

Visual motion, i.e. the pattern of changes on the retinae caused by the motion of objects or the observer through the environment, contains important cues for the accurate perception of the three-dimensional layout of the visual scene. In this study, we investigate if neurons in the visual system, specifically in area MT of the macaque monkey, are able to differentiate between various velocity gradients. Our stimuli were random dot patterns designed to eliminate stimulus variables other than the orientation of a velocity gradient. We develop a stimulus space ("deformation space") that allows us to easily parameterize our stimuli. We demonstrate that a substantial proportion of MT cells show tuned responses to our various velocity gradients, often exceeding the response evoked by an optimized flat velocity profile. This suggests that MT cells are able to represent complex aspects of the visual environment and that their properties make them well suited as building blocks for the complex receptive field properties encountered in higher areas, such as area MST to which many cells in area MT project.

Animals↗
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A more biologically plausible learning rule than backpropagation applied to a network model of cortical area 7a.

Area 7a of the posterior parietal cortex of the primate brain is concerned with representing head-centered space by combining information about the retinal location of a visual stimulus and the position of the eyes in the orbits. An artificial neural network was previously trained to perform this coordinate transformation task using the backpropagation learning procedure, and units in its middle layer (the hidden units) developed properties very similar to those of area 7a neurons presumed to code for spatial location (Andersen and Zipser, 1988; Zipser and Andersen, 1988). We developed two neural networks with architecture similar to Zipser and Andersen's model and trained them to perform the same task using a more biologically plausible learning procedure than backpropagation. This procedure is a modification of the Associative Reward-Penalty (AR-P) algorithm (Barto and Anandan, 1985), which adjusts connection strengths using a global reinforcement signal and local synaptic information. Our networks learn to perform the task successfully to any degree of accuracy and almost as quickly as with backpropagation, and the hidden units develop response properties very similar to those of area 7a neurons. In particular, the probability of firing of the hidden units in our networks varies with eye position in a roughly planar fashion, and their visual receptive fields are large and have complex surfaces. The synaptic strengths computed by the AR-P algorithm are equivalent to and interchangeable with those computed by backpropagation. Our networks also perform the correct transformation on pairs of eye and retinal positions never encountered before. All of these findings are unaffected by the interposition of an extra layer of units between the hidden and output layers. These results show that the response properties of the hidden units of a layered network trained to perform coordinate transformations, and their similarity with those of area 7a neurons, are not a specific result of backpropagation training. The fact that they can be obtained by a more biologically plausible learning rule corroborates the validity of this neural network's computational algorithm as a plausible model of how area 7a may perform coordinate transformations.

Algorithms↗
PubMedDate not supplied

Encoding of intention and spatial location in the posterior parietal cortex.

The posterior parietal cortex is functionally situated between sensory cortex and motor cortex. The responses of cells in this area are difficult to classify as strictly sensory or motor, since many have both sensory- and movement-related activities, as well as activities related to higher cognitive functions such as attention and intention. In this review we will provide evidence that the posterior parietal cortex is an interface between sensory and motor structures and performs various functions important for sensory-motor integration. The review will focus on two specific sensory-motor tasks--the formation of motor plans and the abstract representation of space. Cells in the lateral intraparietal area, a subdivision of the parietal cortex, have activity related to eye movements the animal intends to make. This finding represents the lowest stage in the sensory-motor cortical pathway in which activity related to intention has been found and may represent the cortical stage in which sensory signals go "over the hump" to become intentions and plans to make movements. The second part of the review will discuss the representation of space in the posterior parietal cortex. Encoding spatial locations is an essential step in sensory-motor transformations. Since movements are made to locations in space, these locations should be coded invariant of eye and head position or the sensory modality signaling the target for a movement. Data will be reviewed demonstrating that there exists in the posterior parietal cortex an abstract representation of space that is constructed from the integration of visual, auditory, vestibular, eye position, and proprioceptive head position signals. This representation is in the form of a population code and the above signals are not combined in a haphazard fashion. Rather, they are brought together using a specific operation to form "planar gain fields" that are the common foundation of the population code for the neural construct of space.

Animals↗