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The role of priors in Bayesian models of perception.

Frontiers in computational neuroscience

Teufel C, Subramaniam N, Fletcher PC.
PMID: 23565091
Front Comput Neurosci. 2013 Apr 03;7:25. doi: 10.3389/fncom.2013.00025. eCollection 2013.

No abstract available.

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Teufel C, Subramaniam N, Fletcher PC. The role of priors in Bayesian models of perception. Front Comput Neurosci. 2013;7:25doi: 10.3389/fncom.2013.00025.
Teufel, C., Subramaniam, N., & Fletcher, P. C. (2013). The role of priors in Bayesian models of perception. Frontiers in computational neuroscience, 725. https://doi.org/10.3389/fncom.2013.00025
Teufel, Christoph, et al. "The role of priors in Bayesian models of perception." Frontiers in computational neuroscience vol. 7 (2013): 25. doi: https://doi.org/10.3389/fncom.2013.00025
Teufel C, Subramaniam N, Fletcher PC. The role of priors in Bayesian models of perception. Front Comput Neurosci. 2013 Apr 03;7:25. doi: 10.3389/fncom.2013.00025. eCollection 2013. PMID: 23565091; PMCID: PMC3615220.
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Support vector machines for spike pattern classification with a leaky integrate-and-fire neuron.

Frontiers in computational neuroscience

Ambard M, Rotter S.
PMID: 23181017
Front Comput Neurosci. 2012 Nov 19;6:78. doi: 10.3389/fncom.2012.00078. eCollection 2012.

Spike pattern classification is a key topic in machine learning, computational neuroscience, and electronic device design. Here, we offer a new supervised learning rule based on Support Vector Machines (SVM) to determine the synaptic weights of a leaky integrate-and-fire...

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Ambard M, Rotter S. Support vector machines for spike pattern classification with a leaky integrate-and-fire neuron. Front Comput Neurosci. 2012;6:78doi: 10.3389/fncom.2012.00078.
Ambard, M., & Rotter, S. (2012). Support vector machines for spike pattern classification with a leaky integrate-and-fire neuron. Frontiers in computational neuroscience, 678. https://doi.org/10.3389/fncom.2012.00078
Ambard, Maxime, and Rotter, Stefan. "Support vector machines for spike pattern classification with a leaky integrate-and-fire neuron." Frontiers in computational neuroscience vol. 6 (2012): 78. doi: https://doi.org/10.3389/fncom.2012.00078
Ambard M, Rotter S. Support vector machines for spike pattern classification with a leaky integrate-and-fire neuron. Front Comput Neurosci. 2012 Nov 19;6:78. doi: 10.3389/fncom.2012.00078. eCollection 2012. PMID: 23181017; PMCID: PMC3501200.
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Synchrony analysis: application in early diagnosis, staging and prognosis of multiple sclerosis.

Frontiers in computational neuroscience

Ghanbari Z, Gharibzadeh S.
PMID: 25100985
Front Comput Neurosci. 2014 Jul 21;8:73. doi: 10.3389/fncom.2014.00073. eCollection 2014.

No abstract available.

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Ghanbari Z, Gharibzadeh S. Synchrony analysis: application in early diagnosis, staging and prognosis of multiple sclerosis. Front Comput Neurosci. 2014;8:73doi: 10.3389/fncom.2014.00073.
Ghanbari, Z., & Gharibzadeh, S. (2014). Synchrony analysis: application in early diagnosis, staging and prognosis of multiple sclerosis. Frontiers in computational neuroscience, 873. https://doi.org/10.3389/fncom.2014.00073
Ghanbari, Zahra, and Gharibzadeh, Shahriar. "Synchrony analysis: application in early diagnosis, staging and prognosis of multiple sclerosis." Frontiers in computational neuroscience vol. 8 (2014): 73. doi: https://doi.org/10.3389/fncom.2014.00073
Ghanbari Z, Gharibzadeh S. Synchrony analysis: application in early diagnosis, staging and prognosis of multiple sclerosis. Front Comput Neurosci. 2014 Jul 21;8:73. doi: 10.3389/fncom.2014.00073. eCollection 2014. PMID: 25100985; PMCID: PMC4104700.
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Statistical network analysis for functional MRI: summary networks and group comparisons.

Frontiers in computational neuroscience

Ginestet CE, Fournel AP, Simmons A.
PMID: 24834049
Front Comput Neurosci. 2014 May 06;8:51. doi: 10.3389/fncom.2014.00051. eCollection 2014.

Comparing networks in neuroscience is hard, because the topological properties of a given network are necessarily dependent on the number of edges in that network. This problem arises in the analysis of both weighted and unweighted networks. The term...

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Ginestet CE, Fournel AP, Simmons A. Statistical network analysis for functional MRI: summary networks and group comparisons. Front Comput Neurosci. 2014;8:51doi: 10.3389/fncom.2014.00051.
Ginestet, C. E., Fournel, A. P., & Simmons, A. (2014). Statistical network analysis for functional MRI: summary networks and group comparisons. Frontiers in computational neuroscience, 851. https://doi.org/10.3389/fncom.2014.00051
Ginestet, Cedric E, et al. "Statistical network analysis for functional MRI: summary networks and group comparisons." Frontiers in computational neuroscience vol. 8 (2014): 51. doi: https://doi.org/10.3389/fncom.2014.00051
Ginestet CE, Fournel AP, Simmons A. Statistical network analysis for functional MRI: summary networks and group comparisons. Front Comput Neurosci. 2014 May 06;8:51. doi: 10.3389/fncom.2014.00051. eCollection 2014. PMID: 24834049; PMCID: PMC4018548.
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A Computational Analysis of the Function of Three Inhibitory Cell Types in Contextual Visual Processing.

Frontiers in computational neuroscience

Lee JH, Koch C, Mihalas S.
PMID: 28487644
Front Comput Neurosci. 2017 Apr 25;11:28. doi: 10.3389/fncom.2017.00028. eCollection 2017.

Most cortical inhibitory cell types exclusively express one of three genes, parvalbumin, somatostatin and 5HT3a. We conjecture that these three inhibitory neuron types possess distinct roles in visual contextual processing based on two observations. First, they have distinctive synaptic...

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Lee JH, Koch C, Mihalas S. A Computational Analysis of the Function of Three Inhibitory Cell Types in Contextual Visual Processing. Front Comput Neurosci. 2017;11:28doi: 10.3389/fncom.2017.00028.
Lee, J. H., Koch, C., & Mihalas, S. (2017). A Computational Analysis of the Function of Three Inhibitory Cell Types in Contextual Visual Processing. Frontiers in computational neuroscience, 1128. https://doi.org/10.3389/fncom.2017.00028
Lee, Jung H, et al. "A Computational Analysis of the Function of Three Inhibitory Cell Types in Contextual Visual Processing." Frontiers in computational neuroscience vol. 11 (2017): 28. doi: https://doi.org/10.3389/fncom.2017.00028
Lee JH, Koch C, Mihalas S. A Computational Analysis of the Function of Three Inhibitory Cell Types in Contextual Visual Processing. Front Comput Neurosci. 2017 Apr 25;11:28. doi: 10.3389/fncom.2017.00028. eCollection 2017. PMID: 28487644; PMCID: PMC5403882.
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Forearm Flexor Muscles in Children with Cerebral Palsy Are Weak, Thin and Stiff.

Frontiers in computational neuroscience

von Walden F, Jalaleddini K, Evertsson B, Friberg J, Valero-Cuevas FJ, Pontén E.
PMID: 28487645
Front Comput Neurosci. 2017 Apr 25;11:30. doi: 10.3389/fncom.2017.00030. eCollection 2017.

Children with cerebral palsy (CP) often develop reduced passive range of motion with age. The determining factor underlying this process is believed to be progressive development of contracture in skeletal muscle that likely changes the biomechanics of the joints....

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von Walden F, Jalaleddini K, Evertsson B, et al. Forearm Flexor Muscles in Children with Cerebral Palsy Are Weak, Thin and Stiff. Front Comput Neurosci. 2017;11:30doi: 10.3389/fncom.2017.00030.
von Walden, F., Jalaleddini, K., Evertsson, B., Friberg, J., Valero-Cuevas, F. J., & Pontén, E. (2017). Forearm Flexor Muscles in Children with Cerebral Palsy Are Weak, Thin and Stiff. Frontiers in computational neuroscience, 1130. https://doi.org/10.3389/fncom.2017.00030
von Walden, Ferdinand, et al. "Forearm Flexor Muscles in Children with Cerebral Palsy Are Weak, Thin and Stiff." Frontiers in computational neuroscience vol. 11 (2017): 30. doi: https://doi.org/10.3389/fncom.2017.00030
von Walden F, Jalaleddini K, Evertsson B, Friberg J, Valero-Cuevas FJ, Pontén E. Forearm Flexor Muscles in Children with Cerebral Palsy Are Weak, Thin and Stiff. Front Comput Neurosci. 2017 Apr 25;11:30. doi: 10.3389/fncom.2017.00030. eCollection 2017. PMID: 28487645; PMCID: PMC5403928.
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On the Complexity of Brain Disorders: A Symptom-Based Approach.

Frontiers in computational neuroscience

Moustafa AA, Phillips J, Kéri S, Misiak B, Frydecka D.
PMID: 26941635
Front Comput Neurosci. 2016 Feb 23;10:16. doi: 10.3389/fncom.2016.00016. eCollection 2016.

Mounting evidence shows that brain disorders involve multiple and different neural dysfunctions, including regional brain damage, change to cell structure, chemical imbalance, and/or connectivity loss among different brain regions. Understanding the complexity of brain disorders can help us map...

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Moustafa AA, Phillips J, Kéri S, et al. On the Complexity of Brain Disorders: A Symptom-Based Approach. Front Comput Neurosci. 2016;10:16doi: 10.3389/fncom.2016.00016.
Moustafa, A. A., Phillips, J., Kéri, S., Misiak, B., & Frydecka, D. (2016). On the Complexity of Brain Disorders: A Symptom-Based Approach. Frontiers in computational neuroscience, 1016. https://doi.org/10.3389/fncom.2016.00016
Moustafa, Ahmed A, et al. "On the Complexity of Brain Disorders: A Symptom-Based Approach." Frontiers in computational neuroscience vol. 10 (2016): 16. doi: https://doi.org/10.3389/fncom.2016.00016
Moustafa AA, Phillips J, Kéri S, Misiak B, Frydecka D. On the Complexity of Brain Disorders: A Symptom-Based Approach. Front Comput Neurosci. 2016 Feb 23;10:16. doi: 10.3389/fncom.2016.00016. eCollection 2016. PMID: 26941635; PMCID: PMC4763073.
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Burst Firing in the Electrosensory System of Gymnotiform Weakly Electric Fish: Mechanisms and Functional Roles.

Frontiers in computational neuroscience

Metzen MG, Krahe R, Chacron MJ.
PMID: 27531978
Front Comput Neurosci. 2016 Aug 02;10:81. doi: 10.3389/fncom.2016.00081. eCollection 2016.

Neurons across sensory systems and organisms often display complex patterns of action potentials in response to sensory input. One example of such a pattern is the tendency of neurons to fire packets of action potentials (i.e., a burst) followed...

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Metzen MG, Krahe R, Chacron MJ. Burst Firing in the Electrosensory System of Gymnotiform Weakly Electric Fish: Mechanisms and Functional Roles. Front Comput Neurosci. 2016;10:81doi: 10.3389/fncom.2016.00081.
Metzen, M. G., Krahe, R., & Chacron, M. J. (2016). Burst Firing in the Electrosensory System of Gymnotiform Weakly Electric Fish: Mechanisms and Functional Roles. Frontiers in computational neuroscience, 1081. https://doi.org/10.3389/fncom.2016.00081
Metzen, Michael G, et al. "Burst Firing in the Electrosensory System of Gymnotiform Weakly Electric Fish: Mechanisms and Functional Roles." Frontiers in computational neuroscience vol. 10 (2016): 81. doi: https://doi.org/10.3389/fncom.2016.00081
Metzen MG, Krahe R, Chacron MJ. Burst Firing in the Electrosensory System of Gymnotiform Weakly Electric Fish: Mechanisms and Functional Roles. Front Comput Neurosci. 2016 Aug 02;10:81. doi: 10.3389/fncom.2016.00081. eCollection 2016. PMID: 27531978; PMCID: PMC4969294.
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Beta Band Corticomuscular Drive Reflects Muscle Coordination Strategies.

Frontiers in computational neuroscience

Reyes A, Laine CM, Kutch JJ, Valero-Cuevas FJ.
PMID: 28420975
Front Comput Neurosci. 2017 Apr 04;11:17. doi: 10.3389/fncom.2017.00017. eCollection 2017.

During force production, hand muscle activity is known to be coherent with activity in primary motor cortex, specifically in the beta-band (15-30 Hz) frequency range. It is not clear, however, if this coherence reflects the control strategy selected by...

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Reyes A, Laine CM, Kutch JJ, et al. Beta Band Corticomuscular Drive Reflects Muscle Coordination Strategies. Front Comput Neurosci. 2017;11:17doi: 10.3389/fncom.2017.00017.
Reyes, A., Laine, C. M., Kutch, J. J., & Valero-Cuevas, F. J. (2017). Beta Band Corticomuscular Drive Reflects Muscle Coordination Strategies. Frontiers in computational neuroscience, 1117. https://doi.org/10.3389/fncom.2017.00017
Reyes, Alexander, et al. "Beta Band Corticomuscular Drive Reflects Muscle Coordination Strategies." Frontiers in computational neuroscience vol. 11 (2017): 17. doi: https://doi.org/10.3389/fncom.2017.00017
Reyes A, Laine CM, Kutch JJ, Valero-Cuevas FJ. Beta Band Corticomuscular Drive Reflects Muscle Coordination Strategies. Front Comput Neurosci. 2017 Apr 04;11:17. doi: 10.3389/fncom.2017.00017. eCollection 2017. PMID: 28420975; PMCID: PMC5378725.
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Neurocomputational Model of EEG Complexity during Mind Wandering.

Frontiers in computational neuroscience

Ibáñez-Molina AJ, Iglesias-Parro S.
PMID: 26973505
Front Comput Neurosci. 2016 Mar 04;10:20. doi: 10.3389/fncom.2016.00020. eCollection 2016.

Mind wandering (MW) can be understood as a transient state in which attention drifts from an external task to internal self-generated thoughts. MW has been associated with the activation of the Default Mode Network (DMN). In addition, it has...

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Ibáñez-Molina AJ, Iglesias-Parro S. Neurocomputational Model of EEG Complexity during Mind Wandering. Front Comput Neurosci. 2016;10:20doi: 10.3389/fncom.2016.00020.
Ibáñez-Molina, A. J., & Iglesias-Parro, S. (2016). Neurocomputational Model of EEG Complexity during Mind Wandering. Frontiers in computational neuroscience, 1020. https://doi.org/10.3389/fncom.2016.00020
Ibáñez-Molina, Antonio J, and Iglesias-Parro, Sergio. "Neurocomputational Model of EEG Complexity during Mind Wandering." Frontiers in computational neuroscience vol. 10 (2016): 20. doi: https://doi.org/10.3389/fncom.2016.00020
Ibáñez-Molina AJ, Iglesias-Parro S. Neurocomputational Model of EEG Complexity during Mind Wandering. Front Comput Neurosci. 2016 Mar 04;10:20. doi: 10.3389/fncom.2016.00020. eCollection 2016. PMID: 26973505; PMCID: PMC4777878.
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A Computational Model for Aperture Control in Reach-to-Grasp Movement Based on Predictive Variability.

Frontiers in computational neuroscience

Takemura N, Fukui T, Inui T.
PMID: 26696874
Front Comput Neurosci. 2015 Dec 10;9:143. doi: 10.3389/fncom.2015.00143. eCollection 2015.

In human reach-to-grasp movement, visual occlusion of a target object leads to a larger peak grip aperture compared to conditions where online vision is available. However, no previous computational and neural network models for reach-to-grasp movement explain the mechanism...

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Takemura N, Fukui T, Inui T. A Computational Model for Aperture Control in Reach-to-Grasp Movement Based on Predictive Variability. Front Comput Neurosci. 2015;9:143doi: 10.3389/fncom.2015.00143.
Takemura, N., Fukui, T., & Inui, T. (2015). A Computational Model for Aperture Control in Reach-to-Grasp Movement Based on Predictive Variability. Frontiers in computational neuroscience, 9143. https://doi.org/10.3389/fncom.2015.00143
Takemura, Naohiro, et al. "A Computational Model for Aperture Control in Reach-to-Grasp Movement Based on Predictive Variability." Frontiers in computational neuroscience vol. 9 (2015): 143. doi: https://doi.org/10.3389/fncom.2015.00143
Takemura N, Fukui T, Inui T. A Computational Model for Aperture Control in Reach-to-Grasp Movement Based on Predictive Variability. Front Comput Neurosci. 2015 Dec 10;9:143. doi: 10.3389/fncom.2015.00143. eCollection 2015. PMID: 26696874; PMCID: PMC4675317.
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Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI.

Frontiers in computational neuroscience

Manor R, Geva AB.
PMID: 26696875
Front Comput Neurosci. 2015 Dec 02;9:146. doi: 10.3389/fncom.2015.00146. eCollection 2015.

Brain computer interfaces rely on machine learning (ML) algorithms to decode the brain's electrical activity into decisions. For example, in rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target...

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Manor R, Geva AB. Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI. Front Comput Neurosci. 2015;9:146doi: 10.3389/fncom.2015.00146.
Manor, R., & Geva, A. B. (2015). Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI. Frontiers in computational neuroscience, 9146. https://doi.org/10.3389/fncom.2015.00146
Manor, Ran, and Geva, Amir B. "Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI." Frontiers in computational neuroscience vol. 9 (2015): 146. doi: https://doi.org/10.3389/fncom.2015.00146
Manor R, Geva AB. Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI. Front Comput Neurosci. 2015 Dec 02;9:146. doi: 10.3389/fncom.2015.00146. eCollection 2015. PMID: 26696875; PMCID: PMC4667102.
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Showing 1 to 12 of 1501 entries