Human tactile FA1 neurons (Hay and Pruszynski 2020)

"... 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"

Model Type: Neuron or other electrically excitable cell; Axon; Realistic Network

Region(s) or Organism(s): Human

Receptors: AMPA; NMDA

Model Concept(s): Sensory coding; Synaptic Integration; Receptive field

Simulation Environment: MATLAB


Hay E, Pruszynski JA. (2020). Orientation processing by synaptic integration across first-order tactile neurons. PLoS computational biology. 16 [PubMed]

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