Publications

@article{SEM2024,

title = {Purkinje cell models: past, present and future},

author = {Fernandez SEM and Karim A and Warnaar P and De Zeeuw CI and Badura A and Negrello M },

doi = {10.3389/fncom.2024.1426653 },

year  = {2024},

date = {2024-07-11},

journal = {Frontiers in Computational Neuroscience},

volume = {18},

keywords = {Brain Dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Tricking AI chips into simulating the human brain: A detailed performance analysis},

author = {Landsmeer L and Engelen M and Miedema R and Strydis C},

doi = {10.1016/j.neucom.2024.127953},

year  = {2024},

date = {2024-06-07},

journal = {Neurocomputing},

keywords = {Brain Dynamics, BrainFrame},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {NeuroDots: From Single-Target to Brain-Network Modulation: Why and What Is Needed?},

author = {De Ridder D and Siddiqi MA and Dauwels J and Serdijn WA and Strydis C},

doi = {10.1016/j.neurom.2024.01.003},

year  = {2024},

date = {2024-04-16},

urldate = {2024-04-16},

journal = {Neuromodulation: Technology at the Neural Interface},

keywords = {SIMS},

pubstate = {published},

tppubtype = {article}

}

@article{R2024,

title = {ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations},

author = {Miedema R and Strydis C },

doi = {10.3389/fninf.2024.1330875},

year  = {2024},

date = {2024-04-12},

urldate = {2024-04-12},

journal = {Frontiers in Neuroinformatics},

volume = {18 - 2024},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {article}

}

@article{M2024,

title = {Four-dimensional computational ultrasound imaging of brain hemodynamics},

author = {Brown M and Generowicz BS and Dijkhuizen S and Koekkoek SKE and Strydis C and Bosch JG and Arvanitis P and Springeling G and Leus G and De Zeeuw CI and Kruizinga P},

doi = {10.1126/sciadv.adk7957},

year  = {2024},

date = {2024-01-17},

urldate = {2024-01-17},

journal = {Science Advances},

volume = {10},

keywords = {CUBE},

pubstate = {published},

tppubtype = {article}

}

@article{S2022b,

title = {Purkinje Cell Activity Resonation Generates Rhythmic Behaviors at the Preferred Frequency of 8 Hz},

author = {Bauer S and van Wingerden N and Jacobs T and van der Horst A and Zhai P and Betting JLF and Strydis C and White JJ and De Zeeuw CI and Romano V},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9404806/pdf/biomedicines-10-01831.pdf, Download Paper},

doi = {    10.3390/biomedicines10081831 },

year  = {2022},

date = {2022-08-01},

urldate = {2022-08-01},

journal = {Biomedicines},

volume = {10},

number = {8},

keywords = {Other},

pubstate = {published},

tppubtype = {article}

}

@article{MA2022,

title = {Improving the Security of the IEEE 802.15.6 Standard for Medical BANs},

author = {Siddiqi MA and Hahn G and Hamdioui S, Serdijn WA and Strydis C},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9792251, Download Paper},

doi = {10.1109/ACCESS.2022.3181630},

year  = {2022},

date = {2022-06-09},

journal = {IEEE Access },

number = {10},

pages = {62953 - 62975},

keywords = {SIMS},

pubstate = {published},

tppubtype = {article}

}

@article{S2022,

title = {EDEN: A High-Performance, General-Purpose, NeuroML-Based Neural Simulator },

author = {Panagiotou S and Sidiropoulos H and Soudris D and Negrello M and Strydis C },

url = {https://neurocomputinglab.com/wp-content/uploads/2022/05/eden-frontiers-neuroinformatics-1.pdf, Download Paper},

doi = {10.3389/fninf.2022.724336},

year  = {2022},

date = {2022-05-20},

journal = {Frontiers in Neuroinformatics},

keywords = {Brain Dynamics, BrainFrame},

pubstate = {published},

tppubtype = {article}

}

@article{MA2021b,

title = {Securing Implantable Medical Devices Using Ultrasound Waves},

author = {Siddiqi MA and Beurskens RHSH and Kruizinga P and De Zeeuw CI and Strydis C},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9440455, Download Paper},

doi = {10.1109/ACCESS.2021.3083576},

year  = {2021},

date = {2021-05-25},

journal = {IEEE Access},

volume = {9},

pages = { 80170 - 80182},

keywords = {SIMS},

pubstate = {published},

tppubtype = {article}

}

@article{F2020,

title = {WhiskEras: A New Algorithm for Accurate Whisker Tracking},

author = {Betting JLF and Romano V and Al-Ars Z and Bosman LWJ and Strydis C and De Zeeuw CI},

url = {https://www.frontiersin.org/articles/10.3389/fncel.2020.588445/full, Download Paper},

doi = {10.3389/fncel.2020.588445},

year  = {2020},

date = {2020-11-20},

urldate = {2020-11-20},

journal = {Frontiers in Cellular Neuroscience},

volume = {14},

pages = {372},

keywords = {Other},

pubstate = {published},

tppubtype = {article}

}

@article{MA2020c,

title = {IMDfence: Architecting a Secure Protocol for Implantable Medical Devices},

author = {Siddiqi MA and Doerr C and Strydis C},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9165063},

doi = {10.1109/ACCESS.2020.3015686},

year  = {2020},

date = {2020-08-11},

journal = {IEEE Access},

volume = {8},

pages = {147948 - 147964},

keywords = {SIMS},

pubstate = {published},

tppubtype = {article}

}

@article{R2020,

title = {flexHH: A Flexible Hardware Library for Hodgkin-Huxley-Based Neural Simulations},

author = {Miedema R and Smaragdos G and Negrello M and Al-Ars Z and Möller M and Strydis C},

url = {https://ieeexplore.ieee.org/abstract/document/9133074/, Download Paper},

doi = {10.1109/ACCESS.2020.3007019},

year  = {2020},

date = {2020-07-03},

journal = {IEEE Access},

volume = {8},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {article}

}

@article{V2020,

title = {Functional Convergence of Autonomic and Sensorimotor Processing in the Lateral Cerebellum},

author = {Romano V and Reddington AL and Cazzanelli S and Mazza R and Ma Y and Strydis C and Negrello M and Bosman LWJ and De Zeeuw CI},

url = {https://www.cell.com/cell-reports/fulltext/S2211-1247(20)30848-2?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2211124720308482%3Fshowall%3Dtrue, Download Paper},

doi = {10.1016/j.celrep.2020.107867},

year  = {2020},

date = {2020-07-01},

journal = {Cell Reports},

volume = {32},

number = {1},

abstract = {The cerebellum is involved in the control of voluntary and autonomic rhythmic behaviors, yet it is unclear to what extent it coordinates these in concert. We studied Purkinje cell activity during unperturbed and perturbed respiration in lobules simplex, crus 1, and crus 2. During unperturbed (eupneic) respiration, complex spike and simple spike activity encode the phase of ongoing sensorimotor processing. In contrast, when the respiratory cycle is perturbed by whisker stimulation, mice concomitantly protract their whiskers and advance their inspiration in a phase-dependent manner, preceded by increased simple spike activity. This phase advancement of respiration in response to whisker stimulation can be mimicked by optogenetic stimulation of Purkinje cells and prevented by cell-specific genetic modification of their AMPA receptors, hampering increased simple spike firing. Thus, the impact of Purkinje cell activity on respiratory control is context and phase dependent, highlighting a coordinating role for the cerebellar hemispheres in aligning autonomic and sensorimotor behaviors.},

keywords = {Brain Dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{MA2020b,

title = {Zero-Power Defense Done Right: Shielding IMDs from Battery-Depletion Attacks},

author = {Siddiqi MA and Serdijn WA and Strydis C},

url = {https://link.springer.com/content/pdf/10.1007/s11265-020-01530-5.pdf, Download Paper},

year  = {2020},

date = {2020-04-08},

journal = {Journal of Signal Processing Systems},

keywords = {SIMS},

pubstate = {published},

tppubtype = {article}

}

@article{deA2020,

title = {NINscope, a versatile miniscope for multi-region circuit investigations},

author = {de Groot A and van den Boom B JG and van Genderen RM and Coppens J and van Veldhuijzen J and Bos J and Hoedemaker H and Negrello M and Willuhn I and De Zeeuw CI and Hoogland TM},

url = {https://elifesciences.org/articles/49987, Donwload Paper},

doi = {10.7554/eLife.49987},

year  = {2020},

date = {2020-01-14},

journal = {eLife},

keywords = {Brain Dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{S2020,

title = {Functional Ultrasound (fUS) During Awake Brain Surgery: The Clinical Potential of Intra-Operative Functional and Vascular Brain Mapping},

author = { Soloukey Tbalvandany S and Vincent AJPE and Satoer DD and Mastik F and Smits M and Dirven CMF and Strydis C and Bosch JG and van der Steen AFW and De Zeeuw CI and Koekkoek SKE and Kruizinga P},

doi = {10.3389/fnins.2019.01384},

year  = {2020},

date = {2020-01-09},

journal = {Frontiers in Neuroscience},

volume = {13},

abstract = {Background and Purpose: Oncological neurosurgery relies heavily on making continuous, intra-operative tumor-brain delineations based on image-guidance. Limitations of currently available imaging techniques call for the development of real-time image-guided resection tools, which allow for reliable functional and anatomical information in an intra-operative setting. Functional ultrasound (fUS), is a new mobile neuro-imaging tool with unprecedented spatiotemporal resolution, which allows for the detection of small changes in blood dynamics that reflect changes in metabolic activity of activated neurons through neurovascular coupling. We have applied fUS during conventional awake brain surgery to determine its clinical potential for both intra-operative functional and vascular brain mapping, with the ultimate aim of achieving maximum safe tumor resection.



Methods: During awake brain surgery, fUS was used to image tumor vasculature and task-evoked brain activation with electrocortical stimulation mapping (ESM) as a gold standard. For functional imaging, patients were presented with motor, language or visual tasks, while the probe was placed over (ESM-defined) functional brain areas. For tumor vascular imaging, tumor tissue (pre-resection) and tumor resection cavity (post-resection) were imaged by moving the hand-held probe along a continuous trajectory over the regions of interest.



Results: A total of 10 patients were included, with predominantly intra-parenchymal frontal and temporal lobe tumors of both low and higher histopathological grades. fUS was able to detect (ESM-defined) functional areas deep inside the brain for a range of functional tasks including language processing. Brain tissue could be imaged at a spatial and temporal resolution of 300 μm and 1.5–2.0 ms respectively, revealing real-time tumor-specific, and healthy vascular characteristics.



Conclusion: The current study presents the potential of applying fUS during awake brain surgery. We illustrate the relevance of fUS for awake brain surgery based on its ability to capture both task-evoked functional cortical responses as well as differences in vascular characteristics between tumor and healthy tissue. As current neurosurgical practice is still pre-dominantly leaning on inherently limited pre-operative imaging techniques for tumor resection-guidance, fUS enters the scene as a promising alternative that is both anatomically and physiologically informative.},

keywords = {CUBE},

pubstate = {published},

tppubtype = {article}

}

@article{vanderVlag2019,

title = {Exploring Complex Brain-Simulation Workloads on Multi-GPU Deployments},

author = { van der Vlag MA and Smaragdos G and Al-Ars Z and Strydis C },

doi = {10.1145/3371235},

year  = {2019},

date = {2019-12-01},

journal = {ACM Transactions on Architecture and Code Optimization (TACO)},

volume = {16},

number = {4},

abstract = {In-silico brain simulations are the de-facto tools computational neuroscientists use to understand large-scale and complex brain-function dynamics. Current brain simulators do not scale efficiently enough to large-scale problem sizes (e.g., >100,000 neurons) when simulating biophysically complex neuron models. The goal of this work is to explore the use of true multi-GPU acceleration through NVIDIA’s GPUDirect technology on computationally challenging brain models and to assess their scalability. The brain model used is a state-of-the-art, extended Hodgkin-Huxley, biophysically meaningful, three-compartmental model of the inferior-olivary nucleus. The Hodgkin-Huxley model is the most widely adopted conductance-based neuron representation, and thus the results from simulating this representative workload are relevant for many other brain experiments. Not only the actual network-simulation times but also the network-setup times were taken into account when designing and benchmarking the multi-GPU version, an aspect often ignored in similar previous work. Network sizes varying from 65K to 2M cells, with 10 and 1,000 synapses per neuron were executed on 8, 16, 24, and 32 GPUs. Without loss of generality, simulations were run for 100 ms of biological time. Findings indicate that communication overheads do not dominate overall execution while scaling the network size up is computationally tractable. This scalable design proves that large-network simulations of complex neural models are possible using a multi-GPU design with GPUDirect.},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {article}

}

@article{N.A.2019,

title = {Glissades Are Altered by Lesions to the Oculomotor Vermis but Not by Saccadic Adaptation},

author = { Flierman NA and Ignashchenkova A and Negrello M and Their P and De Zeeuw CI and Badura A},

doi = {10.3389/fnbeh.2019.00194},

year  = {2019},

date = {2019-08-23},

journal = {Frontiers in Behavioral Neuroscience },

volume = {13},

keywords = {Brain Dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{Tbalvandany2019,

title = {Functional ultrasound (FUS) during awake craniotomy tumor removal: Revolutionizing intra-operative functional brain and tumor mapping},

author = { Soloukey Tbalvandany S and Vincent AJPE and Satoer DD and Mastik F and Smits M and Dirven CMF and Strydis C and van der Steen AFW and Bosch JG and De Zeeuw CI and Koekkoek SKE and Kruizinga P },

doi = {10.1159/000501568},

year  = {2019},

date = {2019-06-01},

journal = { Stereotactic and Functional Neurosurgery},

volume = {97},

keywords = {CUBE},

pubstate = {published},

tppubtype = {article}

}

@article{M2019,

title = {Quasiperiodic rhythms of the inferior olive.},

author = { Negrello M and Warnaar P and Romano V and Owens CB and Lindeman S and Iavarone E and Spanke JK and Bosman LWJ and De Zeeuw CI	},

doi = {10.1371/journal.pcbi.1006475},

year  = {2019},

date = {2019-05-06},

journal = {PLoS computational biology 2019 },

abstract = {Inferior olivary activity causes both short-term and long-term changes in cerebellar output underlying motor performance and motor learning. Many of its neurons engage in coherent subthreshold oscillations and are extensively coupled via gap junctions. Studies in reduced preparations suggest that these properties promote rhythmic, synchronized output. However, the interaction of these properties with torrential synaptic inputs in awake behaving animals is not well understood. Here we combine electrophysiological recordings in awake mice with a realistic tissue-scale computational model of the inferior olive to study the relative impact of intrinsic and extrinsic mechanisms governing its activity. Our data and model suggest that if subthreshold oscillations are present in the awake state, the period of these oscillations will be transient and variable. Accordingly, by using different temporal patterns of sensory stimulation, we found that complex spike rhythmicity was readily evoked but limited to short intervals of no more than a few hundred milliseconds and that the periodicity of this rhythmic activity was not fixed but dynamically related to the synaptic input to the inferior olive as well as to motor output. In contrast, in the long-term, the average olivary spiking activity was not affected by the strength and duration of the sensory stimulation, while the level of gap junctional coupling determined the stiffness of the rhythmic activity in the olivary network during its dynamic response to sensory modulation. Thus, interactions between intrinsic properties and extrinsic inputs can explain the variations of spiking activity of olivary neurons, providing a temporal framework for the creation of both the short-term and long-term changes in cerebellar output.},

keywords = {Brain Dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{N2019,

title = {Variability and directionality of inferior olive neuron dendrites revealed by detailed 3D characterization of an extensive morphological library},

author = { Vrieler N and Loyola S and Yarden-Rabinowitz Y and Hoogendorp J and Medvedev N and Hoogland TM and De Zeeuw CI and De Schutter E and Yarom Y and Negrello M and Torben-Nielsen B and Uusisaari MY },

doi = {10.1007/s00429-019-01859-z},

year  = {2019},

date = {2019-03-30},

journal = {Brain structure & function},

volume = {224},

abstract = {The inferior olive (IO) is an evolutionarily conserved brain stem structure and its output activity plays a major role in the cerebellar computation necessary for controlling the temporal accuracy of motor behavior. The precise timing and synchronization of IO network activity has been attributed to the dendro-dendritic gap junctions mediating electrical coupling within the IO nucleus. Thus, the dendritic morphology and spatial arrangement of IO neurons governs how synchronized activity emerges in this nucleus. To date, IO neuron structural properties have been characterized in few studies and with small numbers of neurons; these investigations have described IO neurons as belonging to two morphologically distinct types, “curly” and “straight”. In this work we collect a large number of individual IO neuron morphologies visualized using different labeling techniques and present a thorough examination of their morphological properties and spatial arrangement within the olivary neuropil. Our results show that the extensive heterogeneity in IO neuron dendritic morphologies occupies a continuous range between the classically described “curly” and “straight” types, and that this continuum is well represented by a relatively simple measure of “straightness”. Furthermore, we find that IO neuron dendritic trees are often directionally oriented. Combined with an examination of cell body density distributions and dendritic orientation of adjacent IO neurons, our results suggest that the IO network may be organized into groups of densely coupled neurons interspersed with areas of weaker coupling.},

keywords = {Brain Dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{C2019,

title = {Neurons of the inferior olive respond to broad classes of sensory input while subject to homeostatic control},

author = {Ju C and Bosman LWJ and Hoogland TM and Velauthapillai A and Murugesan P and Warnaar P and van Genderen RM and Negrello M and De Zeeuw CI},

doi = {10.1101/379149},

year  = {2019},

date = {2019-03-25},

journal = {The Journal of Physiology},

volume = {597},

number = {9},

abstract = {Cerebellar Purkinje cells integrate sensory information with motor efference copies to adapt movements to behavioural and environmental requirements. They produce complex spikes that are triggered by the activity of climbing fibres originating in neurons of the inferior olive. These complex spikes can shape the onset, amplitude and direction of movements and the adaptation of such movements to sensory feedback. Clusters of nearby inferior olive neurons project to parasagittally aligned stripes of Purkinje cells, referred to as ‘microzones’. It is currently unclear to what extent individual Purkinje cells within a single microzone integrate climbing fibre inputs from multiple sources of different sensory origins, and to what extent sensory‐evoked climbing fibre responses depend on the strength and recent history of activation. Here we imaged complex spike responses in cerebellar lobule crus 1 to various types of sensory stimulation in awake mice. We find that different sensory modalities and receptive fields have a mild, but consistent, tendency to converge on individual Purkinje cells, with climbing fibres showing some degree of input‐specificity. Purkinje cells encoding the same stimulus show increased events with coherent complex spike firing and tend to lie close together. Moreover, whereas complex spike firing is only mildly affected by variations in stimulus strength, it depends strongly on the recent history of climbing fibre activity. Our data point towards a mechanism in the olivo‐cerebellar system that regulates complex spike firing during mono‐ or multi‐sensory stimulation around a relatively low set‐point, highlighting an integrative coding scheme of complex spike firing under homeostatic control.},

keywords = {Brain Dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{Chatzikonstantis2019,

title = {Multinode implementation of an extended Hodgkin–Huxley simulator},

author = {Chatzikonstantis G and Sidiropoulos H and Strydis C and Negrello M and Smaragdos G and De Zeeuw CI and Soudris D},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/06/1-s2.0-S0925231218312906-main-1.pdf, Download Paper},

year  = {2019},

date = {2019-02-15},

journal = {Elsevier Neuroscomputing},

keywords = {Brain Dynamics, BrainFrame},

pubstate = {published},

tppubtype = {article}

}

@article{Strydis2018,

title = {A Real-Time Reconfigurable Multichip Architecture for Large-Scale Biophysically Accurate Neuron Simulation},

author = {Zjajo A and Hofmann J and Christiaanse GJ and van Eijk M and Smaragdos G and Strydis C and de Graaf A and Galuzzi C and van Leuken R},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/06/NeuroNet_TBioCAS_Revised2.pdf, Download Paper},

doi = {10.1109/TBCAS.2017.2780287},

year  = {2018},

date = {2018-01-26},

journal = {IEEE transactions on biomedical circuits and systems},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {article}

}

@article{G2017,

title = {BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations},

author = {Smaragdos G and Chatzikonstantis G and Kukreja R and Sidiropoulos H and Rodopoulos D and Sourdis I and Al-Ars Z and Kachris C and Soudris D and De Zeeuw CI and Strydis C},

url = {http://iopscience.iop.org/article/10.1088/1741-2552/aa7fc5/meta},

year  = {2017},

date = {2017-11-10},

journal = {IOP Journal of Neural Engineering},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {article}

}

@article{SK2017,

title = {Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer},

author = {Sudhakar SK and Hong S and Raikov I and Publio R and Lang C and Close T and Guo D and Negrello M and De Schutter E},

url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005754},

year  = {2017},

date = {2017-09-21},

journal = {PLOS Computational Biology},

keywords = {Brain Dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{RM2017,

title = {Attacks on Heartbeat-Based Security Using Remote Photoplethysmography},

author = {Seepers RM and Wang W and de Haan G and Sourdis I and Strydis C},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/06/HR_attacks.pdf, Download Paper},

year  = {2017},

date = {2017-04-05},

journal = {IEEE Journal of Biomedical and Health Informatics},

keywords = {SIMS},

pubstate = {published},

tppubtype = {article}

}

@article{G2017b,

title = {Optimizing Extended Hodgkin-Huxley Neuron Model Simulations for a Xeon/Xeon Phi Node},

author = {Chatzikonstantis G and Rodopoulos D and Strydis C and De Zeeuw CI and Soudris D},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/06/tpds2016_170918113614-1.pdf, Download Paper},

year  = {2017},

date = {2017-03-22},

journal = {IEEE Transactions on Parallel and Distributed Systems},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {article}

}