Fine-scale temporal organization of cortical activity in the gamma range (~25–80Hz) may play a significant role in information processing, for example by neural grouping (‘binding’) and phase coding. Recent experimental studies have shown that the precise frequency of gamma oscillations varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common frequency. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent frequency modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different oscillation frequencies at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and frequency differences, and quantified the stimulus-related information represented by gamma phase and frequency. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled oscillators, where input drive determines the intrinsic (natural) frequency of oscillators. The gamma phase-locking, the precise phase relation and the emergent (measurable) frequencies were determined by two principal factors: the detuning (intrinsic frequency difference, i.e. local input difference) and the coupling strength. In addition to frequency coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower oscillation phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma frequencies could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity.
monkey_LFP_V1_stimulus_contrast_example_data
Example LFP data from one contact point of a laminar probe inserted in macaque V1 (superficial cortex)
PING_HH_increasing_drive
Simulaiton of a Hodgkin-Huxley pramidal-interneuron gamma (PING) network with different input drive conditions (corresponds to Figure 1).
ring_PING_HH_data
Simulation of a ring-shaped PING network with spatial-defined connectivity and input drive to E-cells Fig3 = the network used for figure 3 in the main manuscript
highEE= high interconnecitons strength between E-cells
highII = high interconnection strength between I-cells
noII = no interconnection strength between I-cells
noEE= no interconnecitons strength between E-cells
noiselevel1 = low noise level in the input AMPA train to E-cell
noiselevel2 = higher noise level in the input AMPA train to E-cell
two_interacting_PING_HH_data
It includes the simulation of two interacting Hodgkin-Huxley pryramidal-interneuron gamma (PING) networks with different coupling conditions and input drive conditions. The coupling value is indicated in the folder name and the mean excitatory drive to each network is saved as input variables in the folder.
phase_oscillator_lattice_data_part1
simulation data from lattice phase-oscillator model part 1
phase_oscillator_data_part1.zip
overview
If any questions arise, please contact e.lowet@fcdonders.ru.nl
The sharing folder consists of:
1. monkey_LFP_V1_stimulus_contrast_example_data (Fig.1A-B)
Example monkey data (one contact of a laminar probe inserted in parafoveal V1 , see Roberts et al.,2013 in Neuron)
with 8 different contrast conditons. A square-wave grating is shown with different constrasts that stimulated the V1 receptive field.
The monkey is engaged in a passive fixation task.
2. PING_HH_increasing_drive
Here a single PING- network receive different level of excitatory input. This corresponds to Fig.1 C-F. This simulation to show that a
PING network react with increasing gamma frequency with increasing input drive. The relates to the experimental observation in the
monkey experiment where it is known that visual contrast increase the input drive to V1.
8 input level conditions are inlcuded here. The neuronal spiking data (spikes) as well as different network signals (signals) are
included.
3. two_interacting_PING_HH_data
This relates to Fig.2. Here two interacting PING networks are simulated. The coupling strength as well as the input level difference is manipulated systematically
to be able to reconstruct the Arnold tongue. In each folder the spikes, network signals as well the inputs to the both PING networks are included.
The coupling values are in the folder names.
4. ring_PING_HH_data
Here different simulaiton with the ring-PING network is included. Simulations realted to Fig.3 as well as Suppl.Fig 1-2 are included. Only the relevant spiking data are included.
5. phase_oscillator_lattice_data
It includes the simulation output data from the lattice phase-oscillator model for each of 80 input natural contrast images used. The natural contrast images
were used to set the intrinisc freuqency of the phase-osicllators. This relates to Fig.7-8.
6. code_phase_oscillator_ring_network.m (MATLAB code)
This simulation code corresponds to Fig.6. The simulation code reproduces the output data of the ring-phase-oscillator model.
7. code_izhi_ring_network.m(MATLAB code)
This simulation code corresponds to Suppl.Fig.3. The simulation code reproduces the output data of the ring-PING network with Izhikevih-type neurons.
read_phaseoscillator_data
code_izhi_ring_network
Matlab Code of a ring-shaped pryramidal-interneuron gamma (PING) network with Izhikevich-type model neurons (Suppl. Fig.3).
code_phase_oscillator_ring_network
Matlab Code of a ring-shaped phase-oscillator model (Fig.6)
phase_oscillator_lattice_data_part2
simulation data from lattice phase-oscillator model part 2
phase_oscillator_data_part2.zip
phase_oscillator_lattice_data_part3
simulation data from lattice phase-oscillator model part 3
phase_oscillator_data_part3.zip
phase_oscillator_lattice_data_part4
simulation data from lattice phase-oscillator model part 4
phase_oscillator_data_part4.zip
phase_oscillator_lattice_data_part5
simulation data from lattice phase-oscillator model part 5
phase_oscillator_data_part5.zip
phase_oscillator_lattice_data_part6
simulation data from lattice phase-oscillator model part 6
phase_oscillator_data_part6.zip
phase_oscillator_lattice_data_part7
simulation data from lattice phase-oscillator model part 7
phase_oscillator_data_part7.zip
phase_oscillator_lattice_data_part8
simulation data from lattice phase-oscillator model part 8
phase_oscillator_data_part8.zip
phase_oscillator_lattice_data_part9
simulation data from lattice phase-oscillator model part 9
phase_oscillator_data_part9.zip
phase_oscillator_laatice_data_part10
simulation data from lattice phase-oscillator model part 10
phase_oscillator_data_part10.zip