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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.


Illustration of the gamma oscillation mechanism in the model. a Spike raster of 250 ms from a simulation of a model with the same parameters as that shown in Fig. 6. For clarity, spikes from only 5 % of the neurons are shown. A gamma oscillation is apparent in layers 2/3 and 5. b Zoomed spike raster showing only neurons in layer 2/3. Spikes from only 1 % of the neurons are shown. c LFP recording from the virtual electrode with the highest gamma power in the LFP. d Power spectrum of the LFP from this electrode, calculated for 1.5 s simulation time, showing a clear gamma peak. e–h same as a–d, but with synaptic weights from P2/3 cells to B2/3 cells reduced to 1 % of their original value. e–f show B2/3 cell firing is greatly reduced, as they are not receiving excitation from the P2/3 cells. No gamma oscillation emerges. i–l same as a–d, but with synaptic weights from B2/3 cells to P2/3 cells reduced to 1 % of their original value. B2/3 cells fire rapidly and randomly: they are driven by the P2/3 cells but they cannot synchronise them as their synapses are too weak. No gamma oscillation emerges. m–p same as a–d, but with the mean and standard deviation of the stochastic input current to the B2/3 cells increased by 50 %. P2/3 cell firing is suppressed by the increased B2/3 cell firing, so no gamma oscillation occurs
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Fig8: Illustration of the gamma oscillation mechanism in the model. a Spike raster of 250 ms from a simulation of a model with the same parameters as that shown in Fig. 6. For clarity, spikes from only 5 % of the neurons are shown. A gamma oscillation is apparent in layers 2/3 and 5. b Zoomed spike raster showing only neurons in layer 2/3. Spikes from only 1 % of the neurons are shown. c LFP recording from the virtual electrode with the highest gamma power in the LFP. d Power spectrum of the LFP from this electrode, calculated for 1.5 s simulation time, showing a clear gamma peak. e–h same as a–d, but with synaptic weights from P2/3 cells to B2/3 cells reduced to 1 % of their original value. e–f show B2/3 cell firing is greatly reduced, as they are not receiving excitation from the P2/3 cells. No gamma oscillation emerges. i–l same as a–d, but with synaptic weights from B2/3 cells to P2/3 cells reduced to 1 % of their original value. B2/3 cells fire rapidly and randomly: they are driven by the P2/3 cells but they cannot synchronise them as their synapses are too weak. No gamma oscillation emerges. m–p same as a–d, but with the mean and standard deviation of the stochastic input current to the B2/3 cells increased by 50 %. P2/3 cell firing is suppressed by the increased B2/3 cell firing, so no gamma oscillation occurs

Mentions: To demonstrate how the oscillation is generated by the interaction of the excitatory and inhibitory populations, we simulated activity in the model under four different conditions: firstly the original case described above (connection weights in Table ESM5), secondly with P2/3 to B2/3 synapses reduced to 1 % of their original weight, thirdly with B2/3 to P2/3 synapses reduced to 1 % of their original weight, and fourthly with the original synapse weights but increased input current to the B2/3 population (1.5 times the mean and standard deviation used in the original simulation values given in Table ESM9). Simulation results using these different configurations are plotted in Fig. 8, which shows that both P2/3 to B2/3 synapses and B2/3 to P2/3 synapses are necessary for the generation of a population gamma oscillation in the model. Without these connections—or with their strengths severely reduced—no oscillation emerges. This oscillation mechanism is the same as the “weak” pyramidal-interneuron network gamma (PING) model described by Börgers et al. (2005). Firing in a subset of P2/3 cells, which are densely connected with B2/3 neurons with strong synapses, causes a population spike from the B2/3 cells. This suppresses the network until the P2/3 neurons that receive the most input from the stochastic drive reach threshold. This subset of P2/3 neurons then fires, causing another B2/3 cell population spike, and so the oscillation continues (Börgers et al. 2005). Figure 8m–p shows that the oscillation is also suppressed in our model when the driving current to B2/3 cells is increased, allowing them to suppress P2/3 cell firing. This is in line with the gamma suppression mechanism described by Börgers and Kopell (2005). Figure 8 also demonstrates that the gamma oscillation in layer 5 is dependent on a gamma oscillation occurring in layer 2/3: layer 5 gamma is suppressed in each of the cases where layer 2/3 gamma is suppressed. Firing rates for each population in each case are given in Table ESM11.Fig. 8


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)

Illustration of the gamma oscillation mechanism in the model. a Spike raster of 250 ms from a simulation of a model with the same parameters as that shown in Fig. 6. For clarity, spikes from only 5 % of the neurons are shown. A gamma oscillation is apparent in layers 2/3 and 5. b Zoomed spike raster showing only neurons in layer 2/3. Spikes from only 1 % of the neurons are shown. c LFP recording from the virtual electrode with the highest gamma power in the LFP. d Power spectrum of the LFP from this electrode, calculated for 1.5 s simulation time, showing a clear gamma peak. e–h same as a–d, but with synaptic weights from P2/3 cells to B2/3 cells reduced to 1 % of their original value. e–f show B2/3 cell firing is greatly reduced, as they are not receiving excitation from the P2/3 cells. No gamma oscillation emerges. i–l same as a–d, but with synaptic weights from B2/3 cells to P2/3 cells reduced to 1 % of their original value. B2/3 cells fire rapidly and randomly: they are driven by the P2/3 cells but they cannot synchronise them as their synapses are too weak. No gamma oscillation emerges. m–p same as a–d, but with the mean and standard deviation of the stochastic input current to the B2/3 cells increased by 50 %. P2/3 cell firing is suppressed by the increased B2/3 cell firing, so no gamma oscillation occurs
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Related In: Results  -  Collection

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Fig8: Illustration of the gamma oscillation mechanism in the model. a Spike raster of 250 ms from a simulation of a model with the same parameters as that shown in Fig. 6. For clarity, spikes from only 5 % of the neurons are shown. A gamma oscillation is apparent in layers 2/3 and 5. b Zoomed spike raster showing only neurons in layer 2/3. Spikes from only 1 % of the neurons are shown. c LFP recording from the virtual electrode with the highest gamma power in the LFP. d Power spectrum of the LFP from this electrode, calculated for 1.5 s simulation time, showing a clear gamma peak. e–h same as a–d, but with synaptic weights from P2/3 cells to B2/3 cells reduced to 1 % of their original value. e–f show B2/3 cell firing is greatly reduced, as they are not receiving excitation from the P2/3 cells. No gamma oscillation emerges. i–l same as a–d, but with synaptic weights from B2/3 cells to P2/3 cells reduced to 1 % of their original value. B2/3 cells fire rapidly and randomly: they are driven by the P2/3 cells but they cannot synchronise them as their synapses are too weak. No gamma oscillation emerges. m–p same as a–d, but with the mean and standard deviation of the stochastic input current to the B2/3 cells increased by 50 %. P2/3 cell firing is suppressed by the increased B2/3 cell firing, so no gamma oscillation occurs
Mentions: To demonstrate how the oscillation is generated by the interaction of the excitatory and inhibitory populations, we simulated activity in the model under four different conditions: firstly the original case described above (connection weights in Table ESM5), secondly with P2/3 to B2/3 synapses reduced to 1 % of their original weight, thirdly with B2/3 to P2/3 synapses reduced to 1 % of their original weight, and fourthly with the original synapse weights but increased input current to the B2/3 population (1.5 times the mean and standard deviation used in the original simulation values given in Table ESM9). Simulation results using these different configurations are plotted in Fig. 8, which shows that both P2/3 to B2/3 synapses and B2/3 to P2/3 synapses are necessary for the generation of a population gamma oscillation in the model. Without these connections—or with their strengths severely reduced—no oscillation emerges. This oscillation mechanism is the same as the “weak” pyramidal-interneuron network gamma (PING) model described by Börgers et al. (2005). Firing in a subset of P2/3 cells, which are densely connected with B2/3 neurons with strong synapses, causes a population spike from the B2/3 cells. This suppresses the network until the P2/3 neurons that receive the most input from the stochastic drive reach threshold. This subset of P2/3 neurons then fires, causing another B2/3 cell population spike, and so the oscillation continues (Börgers et al. 2005). Figure 8m–p shows that the oscillation is also suppressed in our model when the driving current to B2/3 cells is increased, allowing them to suppress P2/3 cell firing. This is in line with the gamma suppression mechanism described by Börgers and Kopell (2005). Figure 8 also demonstrates that the gamma oscillation in layer 5 is dependent on a gamma oscillation occurring in layer 2/3: layer 5 gamma is suppressed in each of the cases where layer 2/3 gamma is suppressed. Firing rates for each population in each case are given in Table ESM11.Fig. 8

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.