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)

function stim_n = get_noise_stim(stim,noise_mean,d_ratio)
	if noise_mean > 0
		stim_n = zeros(size(stim));
		noise_level = max(max(stim))*noise_mean/100;
		s1 = ceil(size(stim,1)/d_ratio);
		s2 = ceil(size(stim,2)/d_ratio);
		noise_mat = (-1 + 2*rand(s1,s2))*noise_level;
		for i = 1:s1
			for j = 1:s2
				i1 = 1 + round((i-1)*d_ratio);
				i2 = min(size(stim,1),i1 + round(d_ratio));
				j1 = 1 + round((j-1)*d_ratio);
				j2 = min(size(stim,2),j1 + round(d_ratio));
				stim_n(i1:i2,j1:j2) = noise_mat(i,j);
			end
		end
	else
		stim_n = [];
	end
end