Introduction Cerebral palsy (CP) is a neurodevelopmental condition that results from an injury to a developing brain. Children with CP fail to execute precise, well-coordinated movements, and excessive muscular co-contraction or co-activation is a prominent attribute of CP. The normal reciprocal relationship between agonists and antagonists during voluntary movements is altered in patients with CP. H-reflex, which is often regarded as the electrical equivalent of the spinal stretch reflex, can be used to examine the overall reflex arc, including the Ia sensory afferent strength and the spinal motoneuron excitability state. Furthermore, neuromodulatory influence of vibration on H-reflex has been found, which has been increasingly investigated to ascertain its potential use as an intervention in patients with increased spinal reflex excitability. Our goal was to identify the brain mechanism underlying the motor deficits by studying Soleus H-reflex changes during voluntary movement (dorsiflexion) and also to determine the role of vibration in H-reflex modulation in children with spastic CP. Methods Soleus H-reflex was recorded in 12 children with spastic CP (10-16 years) and 15 age-matched controls. Recordings were obtained at rest, during dorsiflexion, and during vibratory stimulation for each subject. H-responses (Hmax amplitudes and Hmax-to-Mmax ratio) were compared among the controls and the cases (CP), for the experiments performed, by the Wilcoxon signed-rank test. The recruitment curves depicting the distribution of mean H-response amplitudes with stimulus intensity increment, for dorsiflexion and vibration were compared among controls and cases by the two-sample Kolmogorov-Smirnov (KS) test. p-value <0.05 was considered as statistically significant. Results Hmax amplitudes and the Hmax-to-Mmax ratio increased (15 % and 12.2 % increment, respectively) from the resting values in the children with CP (p<0.05), while controls exhibited a decrease (reduction of 62% and 57 %, respectively) during dorsiflexion (p<0.05). Vibratory stimulation produced a decreasing trend in H-response measures in both the groups. There was about 15 % and 16 % reduction respectively among children with CP while that of 24 % and 21 % respectively among the controls. The differences in the recruitment curves (distribution of average H-response amplitudes with stimulation intensity) recorded during dorsiflexion and vibration experiments among controls compared with those with CP were found to be statistically significant by the two-sample KS test (p<0.0001). Conclusion The failure of H-reflex suppression during voluntary antagonist muscle activation suggests the presence of impaired reciprocal inhibition in spastic CP. The relatively modest H-response reduction caused by vibratory stimulation in children with CP provides limited evidence of vibratory regulation of the H-reflex in CP. More research into the mechanisms driving motor abnormalities in children with CP is needed, which could aid in therapy planning.

The field of computational cognitive neuroscience (CCN) builds and tests neurobiologically detailed computational models that account for both behavioral and neuroscience data. This article leverages a key advantage of CCN-namely, that it should be possible to interface different CCN models in a plug-and-play fashion-to produce a new and biologically detailed model of perceptual category learning. The new model was created from two existing CCN models: the HMAX model of visual object processing and the COVIS model of category learning. Using bitmap images as inputs and by adjusting only a couple of learning-rate parameters, the new HMAX/COVIS model provides impressively good fits to human category-learning data from two qualitatively different experiments that used different types of category structures and different types of visual stimuli. Overall, the model provides a comprehensive neural and behavioral account of basal ganglia-mediated learning.

In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX) is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT) layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 ms of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: (1) To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; (2) To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; (3) Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

Non-accidental properties (NAPs) correspond to image properties that are invariant to changes in viewpoint (e.g., straight vs. curved contours) and are distinguished from metric properties (MPs) that can change continuously with in-depth object rotation (e.g., aspect ratio, degree of curvature, etc.). Behavioral and electrophysiological studies of shape processing have demonstrated greater sensitivity to differences in NAPs than in MPs. However, previous work has shown that such sensitivity is lacking in multiple-views models of object recognition such as Hmax. These models typically assume that object processing is based on populations of view-tuned neurons with distributed symmetrical bell-shaped tuning that are modulated at least as much by differences in MPs as in NAPs. Here, we test the hypothesis that unsupervised learning of invariances to object transformations may increase the sensitivity to differences in NAPs vs. MPs in Hmax. We collected a database of video sequences with objects slowly rotating in-depth in an attempt to mimic sequences viewed during object manipulation by young children during early developmental stages. We show that unsupervised learning yields shape-tuning in higher stages with greater sensitivity to differences in NAPs vs. MPs in agreement with monkey IT data. Together, these results suggest that greater NAP sensitivity may arise from experiencing different in-depth rotations of objects.

The ability to recognize objects in clutter is crucial for human vision, yet the underlying neural computations remain poorly understood. Previous single-unit electrophysiology recordings in inferotemporal cortex in monkeys and fMRI studies of object-selective cortex in humans have shown that the responses to pairs of objects can sometimes be well described as a weighted average of the responses to the constituent objects. Yet, from a computational standpoint, it is not clear how the challenge of object recognition in clutter can be solved if downstream areas must disentangle the identity of an unknown number of individual objects from the confounded average neuronal responses. An alternative idea is that recognition is based on a subpopulation of neurons that are robust to clutter, i.e., that do not show response averaging, but rather robust object-selective responses in the presence of clutter. Here we show that simulations using the HMAX model of object recognition in cortex can fit the aforementioned single-unit and fMRI data, showing that the averaging-like responses can be understood as the result of responses of object-selective neurons to suboptimal stimuli. Moreover, the model shows how object recognition can be achieved by a sparse readout of neurons whose selectivity is robust to clutter. Finally, the model provides a novel prediction about human object recognition performance, namely, that target recognition ability should show a U-shaped dependency on the similarity of simultaneously presented clutter objects. This prediction is confirmed experimentally, supporting a simple, unifying model of how the brain performs object recognition in clutter.
UNASSIGNED: The neural mechanisms underlying object recognition in cluttered scenes (i.e., containing more than one object) remain poorly understood. Studies have suggested that neural responses to multiple objects correspond to an average of the responses to the constituent objects. Yet, it is unclear how the identities of an unknown number of objects could be disentangled from a confounded average response. Here, we use a popular computational biological vision model to show that averaging-like responses can result from responses of clutter-tolerant neurons to suboptimal stimuli. The model also provides a novel prediction, that human detection ability should show a U-shaped dependency on target-clutter similarity, which is confirmed experimentally, supporting a simple, unifying account of how the brain performs object recognition in clutter.

Key properties of inferior temporal cortex neurons are described, and then, the biological plausibility of two leading approaches to invariant visual object recognition in the ventral visual system is assessed to investigate whether they account for these properties. Experiment 1 shows that VisNet performs object classification with random exemplars comparably to HMAX, except that the final layer C neurons of HMAX have a very non-sparse representation (unlike that in the brain) that provides little information in the single-neuron responses about the object class. Experiment 2 shows that VisNet forms invariant representations when trained with different views of each object, whereas HMAX performs poorly when assessed with a biologically plausible pattern association network, as HMAX has no mechanism to learn view invariance. Experiment 3 shows that VisNet neurons do not respond to scrambled images of faces, and thus encode shape information. HMAX neurons responded with similarly high rates to the unscrambled and scrambled faces, indicating that low-level features including texture may be relevant to HMAX performance. Experiment 4 shows that VisNet can learn to recognize objects even when the view provided by the object changes catastrophically as it transforms, whereas HMAX has no learning mechanism in its S-C hierarchy that provides for view-invariant learning. This highlights some requirements for the neurobiological mechanisms of high-level vision, and how some different approaches perform, in order to help understand the fundamental underlying principles of invariant visual object recognition in the ventral visual stream.

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The human visual system is assumed to transform low level visual features to object and scene representations via features of intermediate complexity. How the brain computationally represents intermediate features is still unclear. To further elucidate this, we compared the biologically plausible HMAX model and Bag of Words (BoW) model from computer vision. Both these computational models use visual dictionaries, candidate features of intermediate complexity, to represent visual scenes, and the models have been proven effective in automatic object and scene recognition. These models however differ in the computation of visual dictionaries and pooling techniques. We investigated where in the brain and to what extent human fMRI responses to short video can be accounted for by multiple hierarchical levels of the HMAX and BoW models. Brain activity of 20 subjects obtained while viewing a short video clip was analyzed voxel-wise using a distance-based variation partitioning method. Results revealed that both HMAX and BoW explain a significant amount of brain activity in early visual regions V1, V2, and V3. However, BoW exhibits more consistency across subjects in accounting for brain activity compared to HMAX. Furthermore, visual dictionary representations by HMAX and BoW explain significantly some brain activity in higher areas which are believed to process intermediate features. Overall our results indicate that, although both HMAX and BoW account for activity in the human visual system, the BoW seems to more faithfully represent neural responses in low and intermediate level visual areas of the brain.

To improve robustness in object recognition, many artificial visual systems imitate the way in which the human visual cortex encodes object information as a hierarchical set of features. These systems are usually evaluated in terms of their ability to accurately categorize well-defined, unambiguous objects and scenes. In the real world, however, not all objects and scenes are presented clearly, with well-defined labels and interpretations. Visual illusions demonstrate a disparity between perception and objective reality, allowing psychophysicists to methodically manipulate stimuli and study our interpretation of the environment. One prominent effect, the Müller-Lyer illusion, is demonstrated when the perceived length of a line is contracted (or expanded) by the addition of arrowheads (or arrow-tails) to its ends. HMAX, a benchmark object recognition system, consistently produces a bias when classifying Müller-Lyer images. HMAX is a hierarchical, artificial neural network that imitates the \"simple\" and \"complex\" cell layers found in the visual ventral stream. In this study, we perform two experiments to explore the Müller-Lyer illusion in HMAX, asking: (1) How do simple vs. complex cell operations within HMAX affect illusory bias and precision? (2) How does varying the position of the figures in the input image affect classification using HMAX? In our first experiment, we assessed classification after traversing each layer of HMAX and found that in general, kernel operations performed by simple cells increase bias and uncertainty while max-pooling operations executed by complex cells decrease bias and uncertainty. In our second experiment, we increased variation in the positions of figures in the input images that reduced bias and uncertainty in HMAX. Our findings suggest that the Müller-Lyer illusion is exacerbated by the vulnerability of simple cell operations to positional fluctuations, but ameliorated by the robustness of complex cell responses to such variance.

To respond appropriately to objects, we must process visual inputs rapidly and assign them meaning. This involves highly dynamic, interactive neural processes through which information accumulates and cognitive operations are resolved across multiple time scales. However, there is currently no model of object recognition which provides an integrated account of how visual and semantic information emerge over time; therefore, it remains unknown how and when semantic representations are evoked from visual inputs. Here, we test whether a model of individual objects--based on combining the HMax computational model of vision with semantic-feature information--can account for and predict time-varying neural activity recorded with magnetoencephalography. We show that combining HMax and semantic properties provides a better account of neural object representations compared with the HMax alone, both through model fit and classification performance. Our results show that modeling and classifying individual objects is significantly improved by adding semantic-feature information beyond ∼200 ms. These results provide important insights into the functional properties of visual processing across time.