Object-centered reference frames in depth as revealed by induced motion.

An object-centric reference frame is a spatial representation in which objects or their parts are coded relative to others. The existence of object-centric representations is supported by the phenomenon of induced motion, in which the motion of an inducer frame in a particular direction induces motion in the opposite direction in a target dot. We report on an experiment made with an induced motion display where a degree of slant is imparted to the inducer frame using either perspective or binocular disparity depth cues. Critically, the inducer frame oscillates perpendicularly to the line of sight, rather than moving in depth. Participants matched the perceived induced motion of the target dot in depth using a 3D rotatable rod. Although the frame did not move in depth, we found that subjects perceived the dot as moving in depth, either along the slanted frame or against it, when depth was given by perspective and disparity, respectively. The presence of induced motion is thus not only due to the competition among populations of planar motion filters, but rather incorporates 3D scene constraints. We also discuss this finding in the context of the uncertainty related to various depth cues, and to the locality of representation of reference frames.

Learning spatial invariance with the trace rule in nonuniform distributions.

Convolutional models of object recognition achieve invariance to spatial transformations largely because of the use of a suitably defined pooling operator. This operator typically takes the form of a max or average function defined across units tuned to the same feature. As a model of the brain's ventral pathway, where computations are carried out by weighted synaptic connections, such pooling can lead to spatial invariance only if the weights that connect similarly tuned units to a given pooling unit are of approximately equal strengths. How identical weights can be learned in the face of nonuniformly distributed data remains unclear. In this letter, we show how various versions of the trace learning rule can help solve this problem. This allows us in turn to explain previously published results and make recommendations as to the optimal rule for invariance learning.

Running as fast as it can: how spiking dynamics form object groupings in the laminar circuits of visual cortex.

How spiking neurons cooperate to control behavioral processes is a fundamental problem in computational neuroscience. Such cooperative dynamics are required during visual perception when spatially distributed image fragments are grouped into emergent boundary contours. Perceptual grouping is a challenge for spiking cells because its properties of collinear facilitation and analog sensitivity occur in response to binary spikes with irregular timing across many interacting cells. Some models have demonstrated spiking dynamics in recurrent laminar neocortical circuits, but not how perceptual grouping occurs. Other models have analyzed the fast speed of certain percepts in terms of a single feedforward sweep of activity, but cannot explain other percepts, such as illusory contours, wherein perceptual ambiguity can take hundreds of milliseconds to resolve by integrating multiple spikes over time. The current model reconciles fast feedforward with slower feedback processing, and binary spikes with analog network-level properties, in a laminar cortical network of spiking cells whose emergent properties quantitatively simulate parametric data from neurophysiological experiments, including the formation of illusory contours; the structure of non-classical visual receptive fields; and self-synchronizing gamma oscillations. These laminar dynamics shed new light on how the brain resolves local informational ambiguities through the use of properly designed nonlinear feedback spiking networks which run as fast as they can, given the amount of uncertainty in the data that they process.

Speed, more than depth, determines the strength of induced motion.

Motion of an inducer in a given direction can cause illusory motion in the opposite direction in a neighboring target object, a phenomenon called induced motion. Induced motion may be due to inducer-target interactions that are local in visual space. Accordingly, increasing the depth between inducer and target should weaken induced motion. Alternatively, separation in depth may not determine whether one object can affect the motion of another. Either viewpoint is supported by separate studies. We show that this contradiction is due to a methodological artifact related to target velocity. Our results support the suggestion that induced motion is not affected by depth separation. Participants rated the effect of an inducer on a target dot presented at different disparities. When target velocity varied with depth, induced motion decreased with depth separation. When target velocity was constant across depth, induced motion was also constant across depth. Thus, target velocity, more than depth, influence motion induction.

Extraction of Knowledge from the Topographic Attentive Mapping Network and its Application in Skill Analysis of Table Tennis.

The Topographic Attentive Mapping (TAM) network is a biologically-inspired classifier that bears similarities to the human visual system. In case of wrong classification during training, an attentional top-down signal modulates synaptic weights in intermediate layers to reduce the difference between the desired output and the classifier's output. When used in a TAM network, the proposed pruning algorithm improves classification accuracy and allows extracting knowledge as represented by the network structure. In this paper, sport technique evaluation of motion analysis modelled by the TAM network was discussed. The trajectory pattern of forehand strokes of table tennis players was analyzed with nine sensor markers attached to the right upper arm of players. With the TAM network, input attributes and technique rules were extracted in order to classify the skill level of players of table tennis from the sensor data. In addition, differences between the elite player, middle level player and beginner were clarified; furthermore, we discussed how to improve skills specific to table tennis from the view of data analysis.

The animat: new frontiers in whole brain modeling.

The researchers at Boston University (BU)'s Neuromorphics Laboratory, part of the National Science Foundation (NSF)-sponsored Center of Excellence for Learning in Education, Science, and Technology (CELEST), are working in collaboration with the engineers and scientists at Hewlett-Packard (HP) to implement neural models of intelligent processes for the next generation of dense, low-power, computer hardware that will use memristive technology to bring data closer to the processor where computation occurs. The HP and BU teams are jointly designing an optimal infrastructure, simulation, and software platform to build an artificial brain. The resulting Cog Ex Machina (Cog) software platform has been successfully used to implement a large-scale, multicomponent brain system that is able to simulate some key rat behavioral results in a virtual environment and has been applied to control robotic platforms as they learn to interact with their environment.

How do object reference frames and motion vector decomposition emerge in laminar cortical circuits?

How do spatially disjoint and ambiguous local motion signals in multiple directions generate coherent and unambiguous representations of object motion? Various motion percepts, starting with those of Duncker (Induced motion, 1929/1938) and Johansson (Configurations in event perception, 1950), obey a rule of vector decomposition, in which global motion appears to be subtracted from the true motion path of localized stimulus components, so that objects and their parts are seen as moving relative to a common reference frame. A neural model predicts how vector decomposition results from multiple-scale and multiple-depth interactions within and between the form- and motion-processing streams in V1-V2 and V1-MST, which include form grouping, form-to-motion capture, figure-ground separation, and object motion capture mechanisms. Particular advantages of the model are that these mechanisms solve the aperture problem, group spatially disjoint moving objects via illusory contours, capture object motion direction signals on real and illusory contours, and use interdepth directional inhibition to cause a vector decomposition, whereby the motion directions of a moving frame at a nearer depth suppress those directions at a farther depth, and thereby cause a peak shift in the perceived directions of object parts moving with respect to the frame.

KInNeSS: a modular framework for computational neuroscience.

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biologically-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalability, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multi-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions or ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further development of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effectively collaborate using a modern neural simulation platform.

Auditory ERP in extremely premature 5-year-old children.

The aim of this research was to test the hypothesis of a functional impairment in the automatic detection of deviant tones in 141 children born after 25 to 28 weeks of gestational age, as compared to 45 age-matched full-term control children. All of them were assessed at age 5 years 9 months and instructed to listen passively to two different pure tones (1000 vs 1200 Hz; 20 vs 80%) counterbalanced between ears. Rarity was thus defined by specific ear by tone combinations. The temporal N100 showed a clear contralateral functional organization of the central auditory pathway, especially for the left ear, but without group difference. By contrast, in full-term controls but not in premature children, the central N200 was specifically increased over frontal leads to rare stimuli as compared to frequent. Premature children demonstrated a lack of brain response when more complex processing integrating different informations was required.