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 [err,m_spike_times,m_spike_rate,o_spike_times,o_spike_rate,m_t] = test_model(model,stim_list,sim_param)
	dx = sim_param.dx;
	dy = sim_param.dy;
	data = sim_param.data;
	drum_speed = sim_param.drum_speed;
	dot_xy = sim_param.dot_xy;	

	err = [];
	for stim_i = 1:length(stim_list)
		o_spike_times{stim_i} = get_spikes_times(stim_list{stim_i},sim_param);
		[m_spike_times{stim_i},m_t{stim_i}] = run_drum_stim(model,stim_list{stim_i},sim_param,0,0,[]);
		if length(m_spike_times{stim_i})>0 && length(o_spike_times{stim_i})>0
			[o_spike_times{stim_i},m_spike_times{stim_i}] = align_times(o_spike_times{stim_i},m_spike_times{stim_i},m_t{stim_i});
		end
		o_spike_rate{stim_i} = get_spike_rate(o_spike_times{stim_i},m_t{stim_i});
		m_spike_rate{stim_i} = get_spike_rate(m_spike_times{stim_i},m_t{stim_i});
		err(stim_i) = calc_err(o_spike_rate{stim_i},m_spike_rate{stim_i});
	end
end