Michaels, Jonathan A.

[["dc.bibliographiccitation.artnumber","e15719"],["dc.bibliographiccitation.journal","eLife"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Dann, Benjamin"],["dc.contributor.author","Michaels, Jonathan A."],["dc.contributor.author","Schaffelhofer, Stefan"],["dc.contributor.author","Scherberger, Hansjörg"],["dc.date.accessioned","2016-10-20T12:02:12Z"],["dc.date.accessioned","2021-10-27T13:12:53Z"],["dc.date.available","2016-10-20T12:02:12Z"],["dc.date.available","2021-10-27T13:12:53Z"],["dc.date.issued","2016-08-15"],["dc.description.abstract","The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks."],["dc.identifier.doi","10.7554/eLife.15719"],["dc.identifier.fs","622743"],["dc.identifier.gro","3151420"],["dc.identifier.pmid","27525488"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13789"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91731"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.euproject","NEBIAS"],["dc.relation.issn","2050-084X"],["dc.relation.orgunit","Deutsches Primatenzentrum"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","17985"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Intveld, Rijk W."],["dc.contributor.author","Dann, Benjamin"],["dc.contributor.author","Michaels, Jonathan A."],["dc.contributor.author","Scherberger, Hansjörg"],["dc.date.accessioned","2019-07-09T11:51:08Z"],["dc.date.available","2019-07-09T11:51:08Z"],["dc.date.issued","2018"],["dc.description.abstract","Considerable progress has been made over the last decades in characterizing the neural coding of hand shape, but grasp force has been largely ignored. We trained two macaque monkeys (Macaca mulatta) on a delayed grasping task where grip type and grip force were instructed. Neural population activity was recorded from areas relevant for grasp planning and execution: the anterior intraparietal area (AIP), F5 of the ventral premotor cortex, and the hand area of the primary motor cortex (M1). Grasp force was strongly encoded by neural populations of all three areas, thereby demonstrating for the first time the coding of grasp force in single- and multi-units of AIP. Neural coding of intended grasp force was most strongly represented in area F5. In addition to tuning analysis, a dimensionality reduction method revealed low-dimensional responses to grip type and grip force. Additionally, this method revealed a high correlation between latent variables of the neural population representing grasp force and the corresponding latent variables of electromyographic forearm muscle activity. Our results therefore suggest an important role of the cortical areas AIP, F5, and M1 in coding grasp force during movement execution as well as of F5 for coding intended grasp force."],["dc.identifier.doi","10.1038/s41598-018-35488-z"],["dc.identifier.pmid","30573765"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16056"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59882"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","570"],["dc.title","Neural coding of intended and executed grasp force in macaque areas AIP, F5, and M1"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Michaels, Jonathan A."],["dc.contributor.author","Scherberger, Hansjörg"],["dc.date.accessioned","2020-12-10T18:10:09Z"],["dc.date.available","2020-12-10T18:10:09Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1038/s41598-018-20051-7"],["dc.identifier.eissn","2045-2322"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15415"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73862"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Population coding of grasp and laterality-related information in the macaque fronto-parietal network"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1005175"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","PLOS Computational Biology"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Michaels, Jonathan A."],["dc.contributor.author","Dann, Benjamin"],["dc.contributor.author","Scherberger, Hansjörg"],["dc.date.accessioned","2017-09-07T11:54:32Z"],["dc.date.available","2017-09-07T11:54:32Z"],["dc.date.issued","2016"],["dc.description.abstract","Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity."],["dc.identifier.doi","10.1371/journal.pcbi.1005175"],["dc.identifier.gro","3151431"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13940"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8233"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","1553-7358"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]