DOI: 10.5061/dryad.p631f
title: Data from: Input-dependent frequency modulation of cortical gamma oscillations shapes spatial synchronization and enables phase coding

description: 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.
description: monkey_LFP_V1_stimulus_contrast_example_dataExample LFP data from one contact point of a laminar probe inserted in macaque V1 (superficial cortex)PING_HH_increasing_driveSimulaiton of a Hodgkin-Huxley pramidal-interneuron gamma (PING) network with different input drive conditions (corresponds to Figure 1).ring_PING_HH_dataSimulation 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-celltwo_interacting_PING_HH_dataIt 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_part1simulation data from lattice phase-oscillator model part 1phase_oscillator_data_part1.zipoverviewIf 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_dataread_HH_datacode_izhi_ring_networkMatlab Code of a ring-shaped pryramidal-interneuron gamma (PING) network with Izhikevich-type model neurons (Suppl. Fig.3).code_phase_oscillator_ring_networkMatlab Code of a ring-shaped phase-oscillator model (Fig.6)phase_oscillator_lattice_data_part2simulation data from lattice phase-oscillator model part 2phase_oscillator_data_part2.zipphase_oscillator_lattice_data_part3simulation data from lattice phase-oscillator model part 3phase_oscillator_data_part3.zipphase_oscillator_lattice_data_part4simulation data from lattice phase-oscillator model part 4phase_oscillator_data_part4.zipphase_oscillator_lattice_data_part5simulation data from lattice phase-oscillator model part 5phase_oscillator_data_part5.zipphase_oscillator_lattice_data_part6simulation data from lattice phase-oscillator model part 6phase_oscillator_data_part6.zipphase_oscillator_lattice_data_part7simulation data from lattice phase-oscillator model part 7phase_oscillator_data_part7.zipphase_oscillator_lattice_data_part8simulation data from lattice phase-oscillator model part 8phase_oscillator_data_part8.zipphase_oscillator_lattice_data_part9simulation data from lattice phase-oscillator model part 9phase_oscillator_data_part9.zipphase_oscillator_laatice_data_part10simulation data from lattice phase-oscillator model part 10phase_oscillator_data_part10.zip