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Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): comparing multi-electrode recordings from simulated and biological mammalian cortical tissue.

Tomsett RJ, Ainsworth M, Thiele A, Sanayei M, Chen X, Gieselmann MA, Whittington MA, Cunningham MO, Kaiser M - Brain Struct Funct (2014)

Bottom Line: We first identified a reduced neuron model that retained the spatial and frequency filtering characteristics of extracellular potentials from neocortical neurons.A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue.We envisage that VERTEX will stimulate experimentalists, clinicians, and computational neuroscientists to use models to understand the mechanisms underlying measured brain dynamics in health and disease.

View Article: PubMed Central - PubMed

Affiliation: School of Computing Science, Newcastle University, Claremont Tower, Newcastle upon Tyne, NE1 7RU, UK, indigentmartian@gmail.com.

ABSTRACT
Local field potentials (LFPs) sampled with extracellular electrodes are frequently used as a measure of population neuronal activity. However, relating such measurements to underlying neuronal behaviour and connectivity is non-trivial. To help study this link, we developed the Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX). We first identified a reduced neuron model that retained the spatial and frequency filtering characteristics of extracellular potentials from neocortical neurons. We then developed VERTEX as an easy-to-use Matlab tool for simulating LFPs from large populations (>100,000 neurons). A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue. Our model, with virtual electrodes placed anywhere in 3D, allows direct comparisons with the in vitro recording setup. We envisage that VERTEX will stimulate experimentalists, clinicians, and computational neuroscientists to use models to understand the mechanisms underlying measured brain dynamics in health and disease.

No MeSH data available.


Parallel simulation performance with increasing numbers of Matlab workers (i.e. parallel processes). Toprow model initialisation times for a the 9 881 neuron model and b the 123,517 neuron model. Bottom simulation times for 1 s of biological time for c the 9,881 neuron model and d the 123,517 neuron model. Thick black lines indicate linear speed scaling; legends indicate the number of electrodes used in each simulation run. The sub-linear speed-up in the small model is due to the decreasing relative performance influence of code vectorisation for smaller matrices (see “Results”)
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Fig4: Parallel simulation performance with increasing numbers of Matlab workers (i.e. parallel processes). Toprow model initialisation times for a the 9 881 neuron model and b the 123,517 neuron model. Bottom simulation times for 1 s of biological time for c the 9,881 neuron model and d the 123,517 neuron model. Thick black lines indicate linear speed scaling; legends indicate the number of electrodes used in each simulation run. The sub-linear speed-up in the small model is due to the decreasing relative performance influence of code vectorisation for smaller matrices (see “Results”)

Mentions: To show how performance improves in parallel mode, we compared the run times for two network models, one large (123,517 neurons with on average 1,835 synapses per neuron) and one small (9,881 neurons with on average 256 synapses per neuron), using VERTEX on a single multicore computer (Fig. 4). Each model contained two populations: layer 5 pyramidal (P5) neurons and layer 5 basket (B5) interneurons. Spike rates in each small model (large model) simulation were ~6 Hz (~7 Hz) and ~24 Hz (~31 Hz) for the P5 and B5 neurons, respectively. The large model shows linear speed-up with increasing number of cores for model initialisation and close-to-linear speed-up in simulation time. The speed-up for the small model is sub-linear: as the interpretation overhead for a vectorised operation on a small matrix is the same as on a large matrix, this overhead starts to dominate the calculation times below a certain number of neurons (Brette and Goodman 2011). Therefore, splitting already small neuron state matrices between more processes does not significantly improve performance. This limit is not reached in larger models.Fig. 4


Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): comparing multi-electrode recordings from simulated and biological mammalian cortical tissue.

Tomsett RJ, Ainsworth M, Thiele A, Sanayei M, Chen X, Gieselmann MA, Whittington MA, Cunningham MO, Kaiser M - Brain Struct Funct (2014)

Parallel simulation performance with increasing numbers of Matlab workers (i.e. parallel processes). Toprow model initialisation times for a the 9 881 neuron model and b the 123,517 neuron model. Bottom simulation times for 1 s of biological time for c the 9,881 neuron model and d the 123,517 neuron model. Thick black lines indicate linear speed scaling; legends indicate the number of electrodes used in each simulation run. The sub-linear speed-up in the small model is due to the decreasing relative performance influence of code vectorisation for smaller matrices (see “Results”)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4481302&req=5

Fig4: Parallel simulation performance with increasing numbers of Matlab workers (i.e. parallel processes). Toprow model initialisation times for a the 9 881 neuron model and b the 123,517 neuron model. Bottom simulation times for 1 s of biological time for c the 9,881 neuron model and d the 123,517 neuron model. Thick black lines indicate linear speed scaling; legends indicate the number of electrodes used in each simulation run. The sub-linear speed-up in the small model is due to the decreasing relative performance influence of code vectorisation for smaller matrices (see “Results”)
Mentions: To show how performance improves in parallel mode, we compared the run times for two network models, one large (123,517 neurons with on average 1,835 synapses per neuron) and one small (9,881 neurons with on average 256 synapses per neuron), using VERTEX on a single multicore computer (Fig. 4). Each model contained two populations: layer 5 pyramidal (P5) neurons and layer 5 basket (B5) interneurons. Spike rates in each small model (large model) simulation were ~6 Hz (~7 Hz) and ~24 Hz (~31 Hz) for the P5 and B5 neurons, respectively. The large model shows linear speed-up with increasing number of cores for model initialisation and close-to-linear speed-up in simulation time. The speed-up for the small model is sub-linear: as the interpretation overhead for a vectorised operation on a small matrix is the same as on a large matrix, this overhead starts to dominate the calculation times below a certain number of neurons (Brette and Goodman 2011). Therefore, splitting already small neuron state matrices between more processes does not significantly improve performance. This limit is not reached in larger models.Fig. 4

Bottom Line: We first identified a reduced neuron model that retained the spatial and frequency filtering characteristics of extracellular potentials from neocortical neurons.A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue.We envisage that VERTEX will stimulate experimentalists, clinicians, and computational neuroscientists to use models to understand the mechanisms underlying measured brain dynamics in health and disease.

View Article: PubMed Central - PubMed

Affiliation: School of Computing Science, Newcastle University, Claremont Tower, Newcastle upon Tyne, NE1 7RU, UK, indigentmartian@gmail.com.

ABSTRACT
Local field potentials (LFPs) sampled with extracellular electrodes are frequently used as a measure of population neuronal activity. However, relating such measurements to underlying neuronal behaviour and connectivity is non-trivial. To help study this link, we developed the Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX). We first identified a reduced neuron model that retained the spatial and frequency filtering characteristics of extracellular potentials from neocortical neurons. We then developed VERTEX as an easy-to-use Matlab tool for simulating LFPs from large populations (>100,000 neurons). A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue. Our model, with virtual electrodes placed anywhere in 3D, allows direct comparisons with the in vitro recording setup. We envisage that VERTEX will stimulate experimentalists, clinicians, and computational neuroscientists to use models to understand the mechanisms underlying measured brain dynamics in health and disease.

No MeSH data available.