Human tactile FA1 neurons (Hay and Pruszynski 2020)

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Accession:266798
"... we show that synaptic integration across the complex signals from the first-order neuronal population could underlie human ability to accurately (< 3°) and rapidly process the orientation of edges moving across the fingertip. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. We then use the model neurons in simulating the peripheral neuronal population that innervates a fingertip. We train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons can process edge orientations with high acuity and speed. ... our models suggest that integration of fast-decaying (AMPA-like) synaptic inputs within short timescales is critical for discriminating fine orientations, whereas integration of slow-decaying (NMDA-like) synaptic inputs supports discrimination of coarser orientations and maintains robustness over longer timescales"
Reference:
1 . Hay E, Pruszynski JA (2020) Orientation processing by synaptic integration across first-order tactile neurons. PLoS Comput Biol 16:e1008303 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Axon; Realistic Network;
Brain Region(s)/Organism: Human;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Sensory coding; Synaptic Integration; Receptive field;
Implementer(s):
Search NeuronDB for information about:  AMPA; NMDA;
% Author: Etay Hay
% Orientation processing by synaptic integration across first-order tactile neurons (Hay and Pruszynski 2020)

warning off
close all;
clear all;
rng(1234);

cellnames = {...
	'ap001FAI_Nerve____VideoCLW_DIR0__Drumpilot3-193.b00',...
	'sx0302FAI_Nerve____VideoCLW_DIR0__Drumpilot3-212.b00',...
	'ra0410FAI_Nerve____VideoCLW_DIR0__Drumpilot3-250.b00',...
	'ra0411FAI_Nerve____VideoCLW_DIR0__Drumpilot3-256.b00_c',...
	'lk0602FAI_Nerve____VideoCLW_DIR0__Drumpilot3-260.b00',...
	'fb1001FAI_Nerve____VideoCLW_DIR0__Drumpilot3-299.b00',...
	'pd1101FAI_Nerve____VideoCCLWDIR0__Drumpilot3-313.b00',... % opposite direction
	'pd1102FAI_Nerve____VideoCLW_DIR0__Drumpilot3-315.b00',...
	'pd1103FAI_Nerve____VideoCCLWDIR0__Drumpilot3-317.b00',... % opposite direction
	'sc1203FAI_Nerve____VideoCCLWDIR0__Drumpilot3-324.b00',... % opposite direction
	'JO1601FAI_Nerve____VideoCCLWDIR0__Drumpilot3-343.b00',... % opposite direction
	'SH1701FAI_Nerve____VideoCCLWDIR90_Drumpilot3-357.b00',... % opposite direction
	'SH1703FAI_Nerve____VideoCCLWDIR90_Drumpilot3-358.b00',... % opposite direction
	'ha28FAI_Nerve____VideoCCLWDIR0__Drumpilot3-444.b00',... % opposite direction , noisy response and between stims
	'ha28FAI_Nerve____VideoCLW_DIR0__Drumpilot3-445.b00',...
	'jk29FAI_Nerve____VideoCLW_DIR0__Drumpilot3-453.b00',...
	'jk29FAI_Nerve____VideoCLW_DIR0__Drumpilot3-456.b00',...
	'ms29FAI_Nerve____VideoCLW_DIR90_Drumpilot3-469.b00',...
	'is3201FAI_Nerve____VideoCLW_DIR0__Drumpilot3-423.b00',... % receptive field larger than dot spacing
};

modeled_cells = [1:6,8:12,15:18];
Nmrs2 = [10,20,30,40];
model_type = '1';
[cell_fit,cell_crossval,s_models] = select_model(modeled_cells,Nmrs2,model_type);
m_fit = mean(cell_fit);
s_fit = std(cell_fit);
m_crossval = mean(cell_crossval);
s_crossval = std(cell_crossval);
disp(['fit = ',num2str(round(m_fit)),' + ',num2str(round(s_fit))])
disp(['crossval = ',num2str(round(m_crossval)),' + ',num2str(round(s_crossval))])

save(['models/selected_models_',model_type],'s_models','cell_fit','cell_crossval','modeled_cells');