Advertisement

< Back to Article

Spike-Timing-Based Computation in Sound Localization

Figure 3

Overview of the model.

(A) The source signal arrives at the two ears after acoustical filtering by HRTFs. The two monaural signals are transformed along the auditory pathway (decomposition into multiple frequency bands by the cochlea and further neural transformations) and transformed into spike trains by monaural neurons. These spike trains converge on neurons which fire preferentially when their inputs are coincident. Location-specific synchrony patterns are thus mapped to the activation of neural assemblies (shown here as (azimuth, elevation) pairs). (B) Detailed model architecture. Acoustical filtering (R,L) is simulated using measured HRTFs. The resulting signals are filtered by a set of gammatone filters γi with central frequencies between 150 Hz and 5 kHz, followed by additional transformations (“neural filtering” FjL/R). Spiking neuron models transform these filtered signals into spike trains, which converge from each side on a coincidence detector neuron (same neuron model). The neural assembly corresponding to a particular location is the set of coincidence detector neurons for which the synchrony field of their inputs contains that location (one pair for each frequency channel). (C) Model response to a sound played at a particular location. Colors represent the firing rate of postsynaptic neurons, vertically ordered by preferred frequency (the horizontal axis represents a dimension orthonogal to the tonotopical axis). The neural assembly that encodes the presented location is represented by white circles. (D) Same as in (C), but neurons are ordered by preferred interaural delay. (E) Total response of all neural assemblies to the same sound presentation, as a function of their assigned location. The most activated assembly encodes for the presented source location.

Figure 3

doi: https://doi.org/10.1371/journal.pcbi.1000993.g003