Reducing variability in motor cortex activity by GABA (Hoshino et al. 2019)

 Download zip file 
Help downloading and running models
Accession:261078
Interaction between sensory and motor cortices is crucial for perceptual decision-making, in which intracortical inhibition might have an important role. We simulated a neural network model consisting of a sensory network (NS) and a motor network (NM) to elucidate the significance of their interaction in perceptual decision-making in association with the level of GABA in extracellular space: extracellular GABA concentration. Extracellular GABA molecules acted on extrasynaptic receptors embedded in membranes of pyramidal cells and suppressed them. A reduction in extracellular GABA concentration either in NS or NM increased the rate of errors in perceptual decision-making, for which an increase in ongoing-spontaneous fluctuations in subthreshold neuronal activity in NM prior to sensory stimulation was responsible. Feedback (NM-to-NS) signaling enhanced selective neuronal responses in NS, which in turn increased stimulus-evoked neuronal activity in NM. We suggest that GABA in extracellular space contributes to reducing variability in motor cortex activity at a resting state and thereby the motor cortex can respond correctly to a subsequent sensory stimulus. Feedback signaling from the motor cortex improves the selective responsiveness of the sensory cortex, which ensures the fidelity of information transmission to the motor cortex, leading to reliable perceptual decision-making.
Reference:
1 . Hoshino O, Kameno R, Watanabe K (2019) Reducing variability in motor cortex activity at a resting state by extracellular GABA for reliable perceptual decision-making. J Comput Neurosci [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex M1 interneuron basket PV GABA cell; Neocortex primary motor area pyramidal layer 5 corticospinal cell;
Channel(s):
Gap Junctions:
Receptor(s): AMPA; GabaA;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: C or C++ program;
Model Concept(s):
Implementer(s): Hoshino, Osamu [osamu.hoshino.507 at vc.ibaraki.ac.jp];
Search NeuronDB for information about:  Neocortex M1 interneuron basket PV GABA cell; GabaA; AMPA; Gaba; Glutamate;
/
Source
readme.html
ASMAfMD6.c
file_reader.m
screenshot.png
                            
% file_reader.m
% makes arrays available in matlab
% see name_array to see order that traces are stored in their respective arrays.
files=dir('*.dat');
data_array={};
data_index=1;
name_array={};
zero_valued={};
zero_index=1;
nonzero_indicies = {};
nonzero_index = 1;

ulb_array={};
upy_array={};
ulb_index=1;
upy_index=1;

for i=1:length(files)
    filename=files(i).name;
    if length(filename)>4
        if strcmp(filename(end-3:end),'.dat')
            cmd =['load ' filename ';'];
            eval(cmd)
            variable_name=filename(1:end-4);
            cmd=['data_array{' num2str(data_index) '}=' variable_name ';'];
            eval(cmd)
            name_array{data_index} = variable_name;
            cmd=['current_trace = ' variable_name ';'];
            eval(cmd)
            if sum(abs(current_trace))==0
                zero_valued{zero_index}=variable_name;
                zero_index = zero_index + 1;
            else
                nonzero_indicies{nonzero_index}=data_index;
                nonzero_index = nonzero_index + 1;
            end
            if strcmp(filename(1:3),'uLB')
                ulb_array{ulb_index}=current_trace;
                ulb_index=ulb_index + 1;
            end
            if strcmp(filename(1:3),'uPY')
                upy_array{upy_index}=current_trace;
                upy_index=upy_index + 1;
            end
            data_index = data_index+1;
        end
    end
end


figure
hold on
plot(ulb_array{7})
plot(ulb_array{9})
title('uLB3_01.dat, uLB4_01.dat','Interpreter','None')