read_phaseoscillator_data

read_phaseoscillator_dat

code_phase_oscillator_ring_network

Matlab Code of a ring-shaped phase-oscillator model (Fig.6

code_izhi_ring_network

Matlab Code of a ring-shaped pryramidal-interneuron gamma (PING) network with Izhikevich-type model neurons (Suppl. Fig.3)

Cloning and characterization of the bovine somatotropin and somatostatin genes to detect selection markers

The objective of this study was to identify alleles of the somatotropin (ST) and somatostatin (SRIF) genes in a group of 100 dairy sires with restriction fragment length polymorphism (RFLP) analysis, and to evaluate the relationship of these alleles to measures of the genetic merit, Milk Fat Protein Dollars (MFPD) and Predictive Transmitting Ability for Milk (PTAM). Three polymerase chain reaction (PCR) cloned fragments of the ST gene were digested with MspI, PvuII, AluI, CfoI and HinfI in separate reactions. The resulting fragments were separated with electrophoresis. No mutations were detected in the promoter (from nt 2 to nt 849) region with any of the enzymes. MspI digestion of the second fragment (from nt 398 to nt 1597) detected three genotypes. The 15% heterozygous [MspI(+ –)], 1% homozygous mutated [Msp(+ +)], 83% wild type [MspI(− −)] distribution frequency agrees with most previous reports. There were no differences (p \u3e .05) between MFPD or PTA M of the groups. Group 3 was excluded from analysis because only one individual had this genotype. Even though the MspI site in intron 3 is located near a transcription factor binding site and the [MspI (+ −)], is associated with a .9 kb insertion/deletion in the 3′ flanking region, potentially carrying transcription regulator sites, neither this, nor the [AluI(+ −)] or the presence of both [MspI(+ −)/AluI(+ −)] in the current study resulted in differences in indicators of the genetic merit. ^ The objective of the second part was to determine whether bSRIF alleles with different growth hormone release inhibiting potentials existed, bearing in mind that RFLPs in human somatostatin (hSRIF) gene have been reported. The gene (Genbank U97077), including the promoter region, exons 1 and 2, the intron and 3′ flanking region, was isolated using sequential PCR. Results of the GenBank Blast analysis revealed that the bSRIF promoter is 91% homologous to the hSRIF promoter and 90% identical to the rat and mouse SRIF promoters. Compared with hSRIF coding region, there is 95% homology among the 348 nt that code for 116 amino-acid pre-prosomatostatin. The bSRIF intron has a consensus CRE, as well as a reverse Ganuna Activated Sequence (GAS), suggesting intron-based influence of gene transcription. The lack of detection of mutations in the SRIF gene may indicate involvement in the control of transcription of most of gene sequences. Based on the results of this investigation, neither the ST nor the SRIF genes contain markers for selection of dairy sires. (Abstract shortened by UMI.)

PING_HH_increasing_drive

Simulaiton of a Hodgkin-Huxley pramidal-interneuron gamma (PING) network with different input drive conditions (corresponds to Figure 1)

monkey_LFP_V1_stimulus_contrast_example_data

Example LFP data from one contact point of a laminar probe inserted in macaque V1 (superficial cortex

Reconstruction of stimulus input based on phase and frequency coding.

<p>See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004072#sec002" target="_blank">Methods</a> for derivations of the coding schemes. A) The stimulus input S_orig to be reconstructed B) Reconstruction based on frequency S_est(ω) (here E-ell rate) alone C) based phase-differences among E-cells S_est(θ) D) based on a combined frequency and phase code S_est(ω,θ). E) The reconstruction performance, measured by mutual information (MI), was from lowest to highest MI = 0.18 for S_est(θ), MI = 0.65 for frequency code S_est(ω) and MI = 0.92 for combined code S_est(ω,θ).</p

overview

If any questions arise, please contact [email protected] 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

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

read_HH_data

read_HH_dat