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We provide explicit exponential upper bounds for the probabilities of under- and overestimating the interaction graph restricted to the observed set and obtain the strong consistency of the estimator. Our result does not require stationarity nor uniqueness of the invariant measure of the process.

Source Bernoulli , Volume 25, Number 1 , Zentralblatt MATH identifier Keywords biological neural nets graph of interactions interacting chains of variable memory length statistical model selection. Estimating the interaction graph of stochastic neural dynamics. Bernoulli 25 , no. Read more about accessing full-text Buy article.

Abstract Article info and citation First page References Abstract In this paper, we address the question of statistical model selection for a class of stochastic models of biological neural nets. Article information Source Bernoulli , Volume 25, Number 1 , Export citation.

Neural Networks in R: Example with Categorical Response at Two Levels

Export Cancel. References [1] Adrian, E. The discharge of impulses in motor nerve fibres. Part II. The frequency of discharge in reflex and voluntary contractions. London: Christophers. Efficiently learning Ising models on arbitrary graphs. Vertical and horizontal electrooculograms EOGs were recorded simultaneously. Electromagnetic calibration of the coil positions was conducted before each MEG recording session. In this study, the stimuli were made from the same materials as those in the present study. The resulting activations showed similar patterns to the original study, including the clusters of bilateral posterior visual areas the activation foci in the V5, FG, and STS , the right inferior parietal lobule, and the right IFG.

A Basic Introduction To Neural Networks

We adopted these cortical activities as the prior. Threshold-based artifact rejection was also conducted. Trials including artifacts were also excluded on the basis of visual inspection. Before the source reconstruction analysis, we conducted a sensor level analysis to check for data quality by computing the mean-square field strength from the MEG sensors.

The mean-squared responses were then averaged across all participants to create the grand-mean waveforms for each condition and contour maps at representative peaks Supplementary Fig.

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The inverse of this normalization transformation was then used to warp a canonical cortical mesh in the MNI space to the individual cortical mesh The cortical mesh described the source locations with 20, vertices i. Next, the MEG sensors were coregistered to the anatomical MRI by matching the positions of three fiducials nasion and R- and L-preauricular points and head shape.

Following inversion of the forward model, we conducted cortical source reconstruction using a parametric empirical Bayesian framework A standard minimum norm inversion was used to compute the cortical source activities on the cortical mesh based on the aforementioned fMRI data as spatial priors on the source localization The use of priors in the current framework imposed only soft not hard constraints The parameters of the inversion were based on SPM default settings, with the exception of not using a Hanning taper for the time series.

The intensity was normalized to the mean over voxels and conditions to reduce inter-participant variance. A non-sphericity correction was used to correct for uneven variance between the factor levels. The observations dependent on the factor levels were also corrected. The ensuing covariance components were estimated using a restricted maximum likelihood procedure and used to adjust the statistics. The low-variance regions, which can cause artificially high statistical values and localization bias, were also adjusted Planned contrasts were performed for each time window.

We tested the main effect of stimulus type dynamic facial expression versus dynamic mosaic and also analyzed the main effect of emotion and the interactions between stimulus type and emotion for descriptive purposes. We used DCM for ERP modeling of electrophysiological data 67 to explore how effective connectivity between brain regions was modulated by dynamic facial expression.

DCM allows us to make inferences about the influence that one neural system exerts over another and how this is affected by experimental contexts We focused on modulation of the cortical network by the presentation of dynamic facial expressions; thus, individual averaged responses were collapsed across the frightened and happy conditions, and the factor of emotion was excluded from the DCM input.

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Anatomical identification was conducted using the cytoarchitectonic map with the Anatomy Toolbox version 1. The time window was determined because it was the first to show a large deflection during visual inspections of source estimates in this region The V1 search region was derived from the Anatomy Toolbox. The ROIs were restricted to the right hemisphere because this was the only one that showed significant activation in all ROIs. The hypothesized models of neural networks were constructed with the driving input of the visual stimulus into V1.

The modulatory effect of dynamic facial expressions was modeled to modulate each of these bidirectional connections. Based on these criteria, we constructed a total of seven models by changing the locations of the modulatory effects Fig. The first model included no modulatory effect on any connections. The next three models included modulatory effects on forward connections, but differed in terms of the included stages. The last three models included modulation on backward connections, in addition to modulatory effects on all forward connections, and also differed gradually in terms of the included stages.

To select the fittest model, we used random-effects BMS We used the exceedance probability to evaluate the belief that a particular model was more likely than any other given the group data. To clarify the involvement of feedback modulation, we grouped the models into three families: no modulation family, only including the null modulation model; forward modulation only family, including modulatory effects on forward connections alone; and forward and backward modulation family, containing modulatory effects on both forward and backward connections.

We then compared the families using BMS To specify the effect of timing of backward modulation, we further compared the models with and without backward modulation models 4 and 7, respectively, in Fig. How to cite this article : Sato, W. Spatiotemporal neural network dynamics for the processing of dynamic facial expressions. Darwin, C. John Murray, London, Yoshikawa, S. Dynamic facial expressions of emotion induce representational momentum. Anttonen, J. Ballistocardiographic responses to dynamic facial displays of emotion while sitting on the EMFi chair.

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Media Psychol. Sato, W. Spontaneous facial mimicry in response to dynamic facial expressions. Cognition , 1—18 Kilts, C. Dissociable neural pathways are involved in the recognition of emotion in static and dynamic facial expressions. Neuroimage 18 , — LaBar, K.

Dynamic perception of facial affect and identity in the human brain. Cortex 13 , — Enhanced neural activity in response to dynamic facial expressions of emotion: An fMRI study. Schultz, J. Natural facial motion enhances cortical responses to faces. Trautmann, S. Emotions in motion: Dynamic compared to static facial expressions of disgust and happiness reveal more widespread emotion-specific activations.

Arsalidou, M. Converging evidence for the advantage of dynamic facial expressions. Brain Topogr. Mulaik, S. Toward a synthesis of deterministic and probabilistic formulations of causal relations by the functional relation concept. Puce, A. ERPs evoked by viewing facial movements. Watanabe, S. Occipitotemporal activity elicited by viewing eye movements: A magnetoencephalographic study. Neuroimage 13 , — Neuroimage 19 , — Tsuchiya, N. Decoding face information in time, frequency and space from direct intracranial recordings of the human brain.

PLoS One 3 , e Furl, N. Modulation of perception and brain activity by predictable trajectories of facial expressions. Cortex 20 , — Recio, G. Electrophysiological correlates of perceiving and evaluating static and dynamic facial emotional expressions. Brain Res. Trautmann-Lengsfeld, S.

The perception of dynamic and static facial expressions of happiness and disgust investigated by ERPs and fMRI constrained source analysis. PLoS One 8 , e Dale, A. Spatiotemporal mapping of brain activity by integration of multiple imaging modalities. Oram, M. Integration of form and motion in the anterior superior temporal polysensory area STPa of the macaque monkey.

De Antonia, F. Emulation and mimicry for social interaction: a theoretical approach to imitation in autism. Wicker, B. Abnormal cerebral effective connectivity during explicit emotional processing in adults with autism spectrum disorder.

Duarte , Galves , Löcherbach , Ost : Estimating the interaction graph of stochastic neural dynamics

Garrido, M. Evoked brain responses are generated by feedback loops. Henson, R. The dynamic aspects of emotional facial expressions. Friston, K. Dynamic causal modelling. Eickhoff, S. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data.

Neuroimage 25 , — Hubel, D. Segregation of form, color, and stereopsis in primate area Morel, A. Anatomical segregation of two cortical visual pathways in the macaque monkey. Catani, M. Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage 17 , 77—94 Petrides, M. Comparative cytoarchitectonic analysis of the human and the macaque ventrolateral prefrontal cortex and corticocortical connection patterns in the monkey.

Iacoboni, M. Understanding others: Imitation, language, empathy. Hurley, S. Kilner, J. The mirror-neuron system: A Bayesian perspective. Neuroreport 18 , — Okada, Y. Comparison of MEG and EEG on the basis of somatic evoked responses elicited by stimulation of the snout in the juvenile swine. George, N. Contrast polarity and face recognition in the human fusiform gyrus. Pelphrey, K.

Brain activation evoked by perception of gaze shifts: The influence of context. Neuropsychologia 41 , — Bould, E. Recognising subtle emotional expressions: The role of facial movements. Ortigue, S. PLoS One 4 , e Leslie, K. Functional imaging of face and hand imitation: Towards a motor theory of empathy. Neuroimage 21 , — Rizzolatti, G. Neurophysiological mechanisms underlying the understanding and imitation of action. Atkinson, A. Visual emotion perception: Mechanisms and processes. In: Emotion and Consciousness.

Feldman-Barrett, L. Bate, S. First report of generalized face processing difficulties in mobius sequence. Williams, J. Imitation, mirror neurons and autism.

Niedenthal, P. When did her smile drop? Facial mimicry and the influences of emotional state on the detection of change in emotional expression. Koivisto, M. Event-related brain potential correlates of visual awareness. Lamme, V. Towards a true neural stance on consciousness. Trends Cogn.

Hobson, R. Autism and the Development of Mind.

Hove Publishers, Hove, Perception of dynamic changes in facial affect and identity in autism. Narumoto, J. Attention to emotion modulates fMRI activity in human right superior temporal sulcus. Ekman, P. Prentice-Hall, Englewood Cliffs, Wallbott, H. Stop looking angry and smile, please: start and stop of the very same facial expression differentially activate threat- and reward-related brain networks Soc.

Amygdala activity in response to forward versus backward dynamic facial expressions Brain Res. Portin, K. Stronger occipital cortical activation to lower than upper visual field stimuli. Neuromagnetic recordings. Shigihara, Y. Parallel processing of face and house stimuli by V1 and specialized visual areas: A magnetoencephalographic MEG study. Rapid, high-frequency, and theta-coupled gamma oscillations in the inferior occipital gyrus during face processing. Cortex 60 , 52—68 Pitcher, D. TMS evidence for the involvement of the right occipital face area in early face processing.

Sel, A. The emotional homunculus: ERP evidence for independent somatosensory responses during facial emotional processing. Pictures of Facial Affect. Consulting Psychologists Press, Palo Alto, Enhanced experience of emotional arousal in response to dynamic facial expressions. Nonverbal Behav. Academic Press, Salt Lake, Mattout, J. Canonical source reconstruction for MEG. Nolte, G. The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors.

Baillet, S. IEEE Trans. Ridgway, G. Neuroimage 59 , — Worsley, K. A unified statistical approach for determining significant signals in images of cerebral activation. Brain Mapp. David, O. Neuroimage 30 , — Litvak, V. Stephan, K. Bayesian model selection for group studies. Neuroimage 46 , — Penny, W. Comparing families of dynamic causal models. PLoS Comput. Download references. We thank Professor S. This study was supported by funds from the Benesse Corporation W.

LZ W. The authors declare no competing financial or other interests. Conceived and designed the experiments: W. Performed the experiments: W.