Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146619
Jean-Marie C. Bouteiller, Zhuobo Feng, A. Onopa, Mike Huang, Eric Y. Hu, Endre T. Somogyi, M. Baudry, Serge Bischoff, T. Berger
Computational models are mathematical representations meant to replicate the biological system they represent, as well as provide insights and predict the system's dynamics in response to changing conditions. In a bottom-up modeling approach, a multitude of models may be compounded to represent more complex higher level biological systems. However, guaranteeing the validity and predictability of the compounded ensemble may become increasingly challenging as more components are integrated. We herein present a sequential and iterative method to maximize predictability of a complex multiscale model. We have successfully developed a multiscale modeling platform comprised of mechanisms ranging from the biomolecular level to multi-cellular networks. To maintain a high level of predictability of the global platform, we introduce a systematic approach to not only validate all models independently, but also verify the validity of compounded models as additional information becomes available at higher levels of complexity. Iterative and systematic application of these validation steps at increasing levels of complexity is intended to maximize the predictive power of the platform, making it a powerful tool to study the impacts of low-levels modifications (pathologies, drugs, etc.) on higher functional levels. The work presented lays down the rationale of the approach, the open design implementation and results.
{"title":"Maximizing predictability of a bottom-up complex multi-scale model through systematic validation and multi-objective multi-level optimization","authors":"Jean-Marie C. Bouteiller, Zhuobo Feng, A. Onopa, Mike Huang, Eric Y. Hu, Endre T. Somogyi, M. Baudry, Serge Bischoff, T. Berger","doi":"10.1109/NER.2015.7146619","DOIUrl":"https://doi.org/10.1109/NER.2015.7146619","url":null,"abstract":"Computational models are mathematical representations meant to replicate the biological system they represent, as well as provide insights and predict the system's dynamics in response to changing conditions. In a bottom-up modeling approach, a multitude of models may be compounded to represent more complex higher level biological systems. However, guaranteeing the validity and predictability of the compounded ensemble may become increasingly challenging as more components are integrated. We herein present a sequential and iterative method to maximize predictability of a complex multiscale model. We have successfully developed a multiscale modeling platform comprised of mechanisms ranging from the biomolecular level to multi-cellular networks. To maintain a high level of predictability of the global platform, we introduce a systematic approach to not only validate all models independently, but also verify the validity of compounded models as additional information becomes available at higher levels of complexity. Iterative and systematic application of these validation steps at increasing levels of complexity is intended to maximize the predictive power of the platform, making it a powerful tool to study the impacts of low-levels modifications (pathologies, drugs, etc.) on higher functional levels. The work presented lays down the rationale of the approach, the open design implementation and results.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127750462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146732
A. A. Fedorova, S. Shishkin, Yuri O. Nuzhdin, B. Velichkovsky
We propose a novel way of robotic device control with communicative eye movements that could possibly help to solve the problem of false activations during the gaze control, known as the Midas touch problem. The proposed approach can be considered as explicitly based on communication between a human operator and a robot. Specifically, we employed gaze patterns that are characteristic for “joint attention” type of communication between two persons. “Joint attention” gaze patterns are automatized and able to convey information about object location even under a high cognitive load. Therefore, we assumed that they may make robot control with gaze more stable. In a study with 28 healthy participants who were naive to this approach most of them easily acquired robot control with “joint attention” gaze patterns. The study did not reveal higher preference for communicative type of control, possibly because the participants did not practice before the tests. We discuss potential benefits of the new approach that can be tested in future studies.
{"title":"Gaze based robot control: The communicative approach","authors":"A. A. Fedorova, S. Shishkin, Yuri O. Nuzhdin, B. Velichkovsky","doi":"10.1109/NER.2015.7146732","DOIUrl":"https://doi.org/10.1109/NER.2015.7146732","url":null,"abstract":"We propose a novel way of robotic device control with communicative eye movements that could possibly help to solve the problem of false activations during the gaze control, known as the Midas touch problem. The proposed approach can be considered as explicitly based on communication between a human operator and a robot. Specifically, we employed gaze patterns that are characteristic for “joint attention” type of communication between two persons. “Joint attention” gaze patterns are automatized and able to convey information about object location even under a high cognitive load. Therefore, we assumed that they may make robot control with gaze more stable. In a study with 28 healthy participants who were naive to this approach most of them easily acquired robot control with “joint attention” gaze patterns. The study did not reveal higher preference for communicative type of control, possibly because the participants did not practice before the tests. We discuss potential benefits of the new approach that can be tested in future studies.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126756164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146656
Aaron D. Gilmour, J. Goding, L. Poole-Warren, C. Thomson, R. Green
The development of the next generation electrode interfaces for neural prosthetic devices requires high-through-put multifaceted testing strategies to assess material interactions with both peripheral and central nervous system (CNS) immune cells. The utility of a primary astrocyte enriched glial cell culture was assessed as a potential in vitro tool for understanding the immune response to electrode materials. Conductive polymer consisting of electropolymerized poly(3,4-ethylenedioxythiophene) (PEDOT) doped with paratoluene sulfonate (pTS) was used as a novel electrode material and compared to the conventional electrode material, platinum (Pt). Morphology of astrocytes and microglia in contact with the materials was analyzed and compared to an immunoassay of TNFα release from human blood plasma. While all electrode materials failed to stimulate TNFα release from human leukocytes, the materials in contact with glial cells resulted in progressive reactive gliosis. This primary astrocyte in vitro assay provides insight into the degeneration of electrode performance in vivo as a result of scar tissue reactions in chronic implant devices. It also highlights the relevance of testing for immune reactions with an appropriate cell system.
{"title":"In vitro biological assessment of electrode materials for neural interfaces","authors":"Aaron D. Gilmour, J. Goding, L. Poole-Warren, C. Thomson, R. Green","doi":"10.1109/NER.2015.7146656","DOIUrl":"https://doi.org/10.1109/NER.2015.7146656","url":null,"abstract":"The development of the next generation electrode interfaces for neural prosthetic devices requires high-through-put multifaceted testing strategies to assess material interactions with both peripheral and central nervous system (CNS) immune cells. The utility of a primary astrocyte enriched glial cell culture was assessed as a potential in vitro tool for understanding the immune response to electrode materials. Conductive polymer consisting of electropolymerized poly(3,4-ethylenedioxythiophene) (PEDOT) doped with paratoluene sulfonate (pTS) was used as a novel electrode material and compared to the conventional electrode material, platinum (Pt). Morphology of astrocytes and microglia in contact with the materials was analyzed and compared to an immunoassay of TNFα release from human blood plasma. While all electrode materials failed to stimulate TNFα release from human leukocytes, the materials in contact with glial cells resulted in progressive reactive gliosis. This primary astrocyte in vitro assay provides insight into the degeneration of electrode performance in vivo as a result of scar tissue reactions in chronic implant devices. It also highlights the relevance of testing for immune reactions with an appropriate cell system.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121350280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146575
Tien Pham, Wanli Ma, D. Tran, Duc Tran, Dinh Q. Phung
Electroencephalography (EEG) has been recently used as a new type of biometrics in user authentication with the advantages of being difficult to fake, impossible to observe or intercept, unique, and requiring alive person to record. However, understanding of the stability of brain responses to EEG-based authentication system while EEG is known to be sensitive to emotions is still a challenge that is addressed in this paper. Our experimental results and the related neurophysiological evidences show that some emotions should be considered to mitigate the impact of EEG signal changes on the EEG-based user authentication system in real-world applications.
{"title":"A study on the stability of EEG signals for user authentication","authors":"Tien Pham, Wanli Ma, D. Tran, Duc Tran, Dinh Q. Phung","doi":"10.1109/NER.2015.7146575","DOIUrl":"https://doi.org/10.1109/NER.2015.7146575","url":null,"abstract":"Electroencephalography (EEG) has been recently used as a new type of biometrics in user authentication with the advantages of being difficult to fake, impossible to observe or intercept, unique, and requiring alive person to record. However, understanding of the stability of brain responses to EEG-based authentication system while EEG is known to be sensitive to emotions is still a challenge that is addressed in this paper. Our experimental results and the related neurophysiological evidences show that some emotions should be considered to mitigate the impact of EEG signal changes on the EEG-based user authentication system in real-world applications.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"356 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123108051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146624
B. Jarrahi, D. Mantini, S. Kollias
Most stimuli in the viscera do not reach conscious perception, although they may activate some cortical structures. However, recent evidences suggest that various forms of subliminal interoceptive inputs may influence brain function. In this study, we used spatial independent component analysis (ICA) as a multivariate method to investigate the effect of interoception on the intra- and inter-network connectivity of the human brain. 15 healthy participants were scanned during the resting-state and a visceral stimulation task. Following a recently suggested ICA framework, we applied a high model order ICA of 75 to the fMRI data, and identified 34 components as non-artifactual intrinsic connectivity networks (ICNs). Results demonstrate significant intra-network connectivity difference within the salience network (SN) and the default mode network (p <; 0.05, family-wise error corrected). Significant inter-network connectivity differences were also found for several ICN pairs, most notably between the SN and the frontoparietal central executive network, and between the SN and the limbic association network (p<;0.05, false discovery rate corrected for multiple comparisons). Taken together, these observations suggest significant effect of interoception on the network connectivity architecture of the human brain especially involving the SN when compared to the resting-state baseline.
{"title":"Effect of interoception on intra- and inter-network connectivity of human brain — An independent component analysis of fMRI data","authors":"B. Jarrahi, D. Mantini, S. Kollias","doi":"10.1109/NER.2015.7146624","DOIUrl":"https://doi.org/10.1109/NER.2015.7146624","url":null,"abstract":"Most stimuli in the viscera do not reach conscious perception, although they may activate some cortical structures. However, recent evidences suggest that various forms of subliminal interoceptive inputs may influence brain function. In this study, we used spatial independent component analysis (ICA) as a multivariate method to investigate the effect of interoception on the intra- and inter-network connectivity of the human brain. 15 healthy participants were scanned during the resting-state and a visceral stimulation task. Following a recently suggested ICA framework, we applied a high model order ICA of 75 to the fMRI data, and identified 34 components as non-artifactual intrinsic connectivity networks (ICNs). Results demonstrate significant intra-network connectivity difference within the salience network (SN) and the default mode network (p <; 0.05, family-wise error corrected). Significant inter-network connectivity differences were also found for several ICN pairs, most notably between the SN and the frontoparietal central executive network, and between the SN and the limbic association network (p<;0.05, false discovery rate corrected for multiple comparisons). Taken together, these observations suggest significant effect of interoception on the network connectivity architecture of the human brain especially involving the SN when compared to the resting-state baseline.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126923121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146567
Lin Yao, Tao Xie, Xiaokang Shu, X. Sheng, Dingguo Zhang, Xiangyang Zhu
In this work, paired sensory stimulation training via pavlovian conditioning (PSSPC) was proposed to improve motor imagery BCI performance, especially targeted in those poor performing BCI users. Motor imagery task was paired with the sensory stimulus to establish the conditioned responses through the long-term classic conditioning training. Three poor performing subjects were recruited to participate the PSSPC experiment lasting for about one month in eight sessions. R2 contrast image have shown that the discriminative brain pattern was emerged out in the sensorimotor area of the brain after several sessions training. In addition, up to 80% BCI performance was achieve to some subjects, and it has also shown that learning was evolved in the PSSPC training, complementary to the feedback based training (also termed operant conditioning). The PSSPC methodology has the potential in improving those poor performing BCI subjects, and laid the potential to guide the cortical plastic changes for those with motor impairments.
{"title":"Long-term paired sensory stimulation training for improved motor imagery BCI performance via pavlovian conditioning theory","authors":"Lin Yao, Tao Xie, Xiaokang Shu, X. Sheng, Dingguo Zhang, Xiangyang Zhu","doi":"10.1109/NER.2015.7146567","DOIUrl":"https://doi.org/10.1109/NER.2015.7146567","url":null,"abstract":"In this work, paired sensory stimulation training via pavlovian conditioning (PSSPC) was proposed to improve motor imagery BCI performance, especially targeted in those poor performing BCI users. Motor imagery task was paired with the sensory stimulus to establish the conditioned responses through the long-term classic conditioning training. Three poor performing subjects were recruited to participate the PSSPC experiment lasting for about one month in eight sessions. R2 contrast image have shown that the discriminative brain pattern was emerged out in the sensorimotor area of the brain after several sessions training. In addition, up to 80% BCI performance was achieve to some subjects, and it has also shown that learning was evolved in the PSSPC training, complementary to the feedback based training (also termed operant conditioning). The PSSPC methodology has the potential in improving those poor performing BCI subjects, and laid the potential to guide the cortical plastic changes for those with motor impairments.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"64 2-3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126972404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146782
A. Ferrante, Constantinos Gavriel, A. Faisal
EEG-based Brain Computer Interfaces (BCIs) are quite noisy brain signals recorded from the scalp (electroencephalography, EEG) to translate the user's intent into action. This is usually achieved by looking at the pattern of brain activity across many trials while the subject is imagining the performance of an instructed action - the process known as motor imagery. Nevertheless, existing motor imagery classification algorithms do not always achieve good performances because of the noisy and non-stationary nature of the EEG signal and inter-subject variability. Thus, current EEG BCI takes a considerable upfront toll on patients, who have to submit to lengthy training sessions before even being able to use the BCI. In this study, we developed a data-efficient classifier for left/right hand motor imagery by combining in our pattern recognition both the oscillation frequency range and the scalp location. We achieve this by using a combination of Morlet wavelet and Common Spatial Pattern theory to deal with nonstationarity and noise. The system achieves an average accuracy of 88% across subjects and was trained by about a dozen training (10-15) examples per class reducing the size of the training pool by up to a 100-fold, making it very data-efficient way for EEG BCI.
{"title":"Data-efficient hand motor imagery decoding in EEG-BCI by using Morlet wavelets & Common Spatial Pattern algorithms","authors":"A. Ferrante, Constantinos Gavriel, A. Faisal","doi":"10.1109/NER.2015.7146782","DOIUrl":"https://doi.org/10.1109/NER.2015.7146782","url":null,"abstract":"EEG-based Brain Computer Interfaces (BCIs) are quite noisy brain signals recorded from the scalp (electroencephalography, EEG) to translate the user's intent into action. This is usually achieved by looking at the pattern of brain activity across many trials while the subject is imagining the performance of an instructed action - the process known as motor imagery. Nevertheless, existing motor imagery classification algorithms do not always achieve good performances because of the noisy and non-stationary nature of the EEG signal and inter-subject variability. Thus, current EEG BCI takes a considerable upfront toll on patients, who have to submit to lengthy training sessions before even being able to use the BCI. In this study, we developed a data-efficient classifier for left/right hand motor imagery by combining in our pattern recognition both the oscillation frequency range and the scalp location. We achieve this by using a combination of Morlet wavelet and Common Spatial Pattern theory to deal with nonstationarity and noise. The system achieves an average accuracy of 88% across subjects and was trained by about a dozen training (10-15) examples per class reducing the size of the training pool by up to a 100-fold, making it very data-efficient way for EEG BCI.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"740 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126497273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146639
Narsis Salafzoon, D. Strauss, N. Hatsopoulos, Kazutaka Takahashi
The neocortex, as a great majority of the cerebral cortex, conceals multiple bands of oscillations recorded in local field potentials (LFPs), which are associated with different neural circuits and their corresponding brain functions. Not only are the complex neural connections in this area significant factors in the emergence of the cortical oscillations, but the inputs from the thalamus to the cortex along with cortical projections to dorsal thalamic nuclei and to the thalamic reticular nucleus as a part of the ventral nuclei as well. These cortical oscillations are related to sensory processing, memory, cognition, and motor control. Here, we developed a functional simulation model of the basic thalamocortical - corticothalamic loop architecture with detailed cortical laminar structure and a diverse set of neuron types. Our model generates prominent β oscillations, and demonstrates the role of the time-varying bottom up inputs in the dynamics of β oscillation in different layers of the cortex. Through excitatory top-down β signals, which were strongly modulated based on frontal attentional inputs, two states were also modeled: Attended and Non-Attended.
{"title":"Dynamic interlaminar and thalamocortical interaction supported by top-down beta rhythms","authors":"Narsis Salafzoon, D. Strauss, N. Hatsopoulos, Kazutaka Takahashi","doi":"10.1109/NER.2015.7146639","DOIUrl":"https://doi.org/10.1109/NER.2015.7146639","url":null,"abstract":"The neocortex, as a great majority of the cerebral cortex, conceals multiple bands of oscillations recorded in local field potentials (LFPs), which are associated with different neural circuits and their corresponding brain functions. Not only are the complex neural connections in this area significant factors in the emergence of the cortical oscillations, but the inputs from the thalamus to the cortex along with cortical projections to dorsal thalamic nuclei and to the thalamic reticular nucleus as a part of the ventral nuclei as well. These cortical oscillations are related to sensory processing, memory, cognition, and motor control. Here, we developed a functional simulation model of the basic thalamocortical - corticothalamic loop architecture with detailed cortical laminar structure and a diverse set of neuron types. Our model generates prominent β oscillations, and demonstrates the role of the time-varying bottom up inputs in the dynamics of β oscillation in different layers of the cortex. Through excitatory top-down β signals, which were strongly modulated based on frontal attentional inputs, two states were also modeled: Attended and Non-Attended.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131295263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146607
Tianruo Guo, N. Lovell, D. Tsai, Perry Twyford, S. Fried, J. Morley, G. Suaning, S. Dokos
A retinal ganglion cell (RGC) model based on accurate biophysics and detailed representations of cell morphologies was used to understand how these cells respond to electrical stimulation over a wide range of frequencies, spanning 50-2000 pulses per second (PPS). Our modeling results and associated in vitro data both suggest the usefulness of high stimulation frequency in effectively modulating the activity of RGCs. This model can be used for optimizing varied extracellular stimulus profiles, and to assist in the design of sophisticated stimulation strategies for clinical visual neuroprostheses.
{"title":"Optimizing retinal ganglion cell responses to high-frequency electrical stimulation strategies for preferential neuronal excitation","authors":"Tianruo Guo, N. Lovell, D. Tsai, Perry Twyford, S. Fried, J. Morley, G. Suaning, S. Dokos","doi":"10.1109/NER.2015.7146607","DOIUrl":"https://doi.org/10.1109/NER.2015.7146607","url":null,"abstract":"A retinal ganglion cell (RGC) model based on accurate biophysics and detailed representations of cell morphologies was used to understand how these cells respond to electrical stimulation over a wide range of frequencies, spanning 50-2000 pulses per second (PPS). Our modeling results and associated in vitro data both suggest the usefulness of high stimulation frequency in effectively modulating the activity of RGCs. This model can be used for optimizing varied extracellular stimulus profiles, and to assist in the design of sophisticated stimulation strategies for clinical visual neuroprostheses.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130170164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146677
M. Bisio, Valentina Pasquale, A. Bosca, L. Berdondini, M. Chiappalone
The development of in vitro neuronal models constituted by patterned networks is of significant interest in the neuroscientific community and requires the convergence of electrophysiological studies with micro/nano-fabrication techniques. In this paper we make use of a methodology to induce self-organization of networks into two connected sub-populations grown onto commercially available Micro Electrode Arrays (MEAs) in order to understand the role of `burst leaders' in generating the collective synchronous events (i.e. network bursts) spontaneously arising in dissociated cultures. Considering the multitude of connections shown by uniform neuronal cultures, the restraint of neurite outgrowth along specific pathways ensures a considerable control over network complexity. Here we exploit this topological configuration to investigate whether and how network burst generation is affected by the development of the network. Our results constitute important evidence that engineered neuronal networks are a powerful platform to systematically approach questions related to the dynamics of neuronal assemblies.
{"title":"Role of major burst leaders in modular hippocampal networks","authors":"M. Bisio, Valentina Pasquale, A. Bosca, L. Berdondini, M. Chiappalone","doi":"10.1109/NER.2015.7146677","DOIUrl":"https://doi.org/10.1109/NER.2015.7146677","url":null,"abstract":"The development of in vitro neuronal models constituted by patterned networks is of significant interest in the neuroscientific community and requires the convergence of electrophysiological studies with micro/nano-fabrication techniques. In this paper we make use of a methodology to induce self-organization of networks into two connected sub-populations grown onto commercially available Micro Electrode Arrays (MEAs) in order to understand the role of `burst leaders' in generating the collective synchronous events (i.e. network bursts) spontaneously arising in dissociated cultures. Considering the multitude of connections shown by uniform neuronal cultures, the restraint of neurite outgrowth along specific pathways ensures a considerable control over network complexity. Here we exploit this topological configuration to investigate whether and how network burst generation is affected by the development of the network. Our results constitute important evidence that engineered neuronal networks are a powerful platform to systematically approach questions related to the dynamics of neuronal assemblies.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124045918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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