A computer model of amplitude-modulation sensitivity of single units in the inferior colliculus

Abstract

A computer model is presented of a neural circuit that replicates amplitude-modulation (AM) sensitivity of cells in the central nucleus of the inferior colliculus (ICC). The ICC cell is modeled as a point neuron whose input consists of spike trains from a number of simulated ventral cochlear nucleus (VCN) chopper cells. Input to the VCN chopper cells is provided by simulated spike trains from a model of the auditory periphery [Hewitt et al., J. Acoust. Soc. Am. 91, 2096-2109 (1992)]. The performance of the model at the output of the auditory nerve, the cochlear nucleus and ICC simulations in response to amplitude-modulated stimuli is described. The results are presented in terms of both temporal and rate modulation transfer functions (MTFs) and compared with data from physiological studies in the literature. Qualitative matches were obtained to the following main empirical findings: (a) Auditory nerve temporal-MTFs are low pass, (b) VCN chopper temporal-MTFs are low pass at low signal levels and bandpass at moderate and high signal levels, (c) ICC unit temporal-MTFs are low pass at low signal levels and broadly tuned bandpass at moderate and high signal levels, and (d) ICC unit rate-MTFs are sharply tuned bandpass at low and moderate signal levels and flat at high levels. VCN and ICC units preferentially sensitive to different rates of modulation are presented. The model supports the hypothesis that cells in the ICC decode temporal information into a rate code [Langner and Schreiner, J. Neurophysiol. 60, 1799-1822 (1988)], and provides a candidate wiring diagram of how this may be achieved.

A biophysical model for modulation frequency encoding in the cochlear nucleus

“…An approach that has been taken to calibrate the neural tMTFs to different BMFs was the adjustment of membrane and potassium conductance time constants (Hewitt and Meddis, 1994;Guérin et al, 2006). For example, using membrane time constants in the order of 1 ms and conductance time constants in the 0.1-10 ms range for point neuron models of VCN chopper neurons, results in BMFs in the 40-400 Hz range (Hewitt and Meddis, 1994).…”

“…An approach that has been taken to calibrate the neural tMTFs to different BMFs was the adjustment of membrane and potassium conductance time constants (Hewitt and Meddis, 1994;Guérin et al, 2006). For example, using membrane time constants in the order of 1 ms and conductance time constants in the 0.1-10 ms range for point neuron models of VCN chopper neurons, results in BMFs in the 40-400 Hz range (Hewitt and Meddis, 1994). Besides the fact that tuning neuronal BMFs through ad-hoc calibration of conductance time constants can be regarded as a biologically Demanding assumption (specially when one considers the lack of calibration freedom that these parameters present, since they are molecularly determined), using a 100-fold range of time constants for a unique and somewhat physiologically homogeneous population of neurons seems biologically unrealistic.…”

“…Several models for the processing and encoding of amplitude modulated sounds were proposed so far (Hewitt et al, 1991;Hewitt and Meddis, 1994;Eriksson and Robert, 1999;Nelson and Carney, 2004;Borst et al, 2004;Guérin et al, 2006;Dicke et al, 2006;. Some of these works were mainly centered in the replication of the temporal responses to AM stimuli, but others were also aimed at obtaining a sort of periodotopic organization in the system.…”

Intracellular Responses of Neurons in the Mouse Inferior Colliculus to Sinusoidal Amplitude-Modulated Tones

“…The few intracellular studies addressing this question have shown that the membrane potential of IC neurons can phase-lock to the modulation envelope in response to SAM tones (Casseday et al 1994;Leary et al 2008) and that the amplitude of this response decreases with higher modulation frequencies (Tan and Borst 2007). In addition, several models have been made for bandpass rate tuning to AM stimuli, but essential features of these models, such as coincidence detection (Borst et al 2004;Guérin et al 2006;Hewitt and Meddis 1994;Langner 1981), the presence of feedback (Friedel et al 2007), or rate-tuned inhibition (Dicke et al 2007;Carney 2004, 2007), have not yet been tested.…”

A Neural Correlate of Stochastic Echo Imaging

“…Hewitt and Meddis (1994) simulated the transformation of a temporal code for amplitude modulation in cochlear-nucleus sustained-chopper cells into a rate code of amplitude modulation through inferior-colliculus coincidence-detector cells. Ongoing experiments in our lab try to localize the neural substrate for the transformation of roughness into a rate code.…”