Computational models of neurophysiological correlates of tinnitus

Abstract

The understanding of tinnitus has progressed considerably in the past decade, but the details of the mechanisms that give rise to this phantom perception of sound without a corresponding acoustic stimulus have not yet been pinpointed. It is now clear that tinnitus is generated in the brain, not in the ear, and that it is correlated with pathologically altered spontaneous activity of neurons in the central auditory system. Both increased spontaneous firing rates and increased neuronal synchrony have been identified as putative neuronal correlates of phantom sounds in animal models, and both phenomena can be triggered by damage to the cochlea. Various mechanisms could underlie the generation of such aberrant activity. At the cellular level, decreased synaptic inhibition and increased neuronal excitability, which may be related to homeostatic plasticity, could lead to an over-amplification of natural spontaneous activity. At the network level, lateral inhibition could amplify differences in spontaneous activity, and structural changes such as reorganization of tonotopic maps could lead to self-sustained activity in recurrently connected neurons. However, it is difficult to disentangle the contributions of different mechanisms in experiments, especially since not all changes observed in animal models of tinnitus are necessarily related to tinnitus. Computational modeling presents an opportunity of evaluating these mechanisms and their relation to tinnitus. Here we review the computational models for the generation of neurophysiological correlates of tinnitus that have been proposed so far, and evaluate predictions and compare them to available data. We also assess the limits of their explanatory power, thus demonstrating where an understanding is still lacking and where further research may be needed. Identifying appropriate models is important for finding therapies, and we therefore, also summarize the implications of the models for approaches to treat tinnitus.

Keywords: computational model; gain adaptation; hearing loss; homeostatic plasticity; lateral inhibition; tinnitus.

Figures

Figure 1

Schematic illustration of lateral-inhibition models. (A) Depiction of a layer of neurons with lateral inhibition. Neurons are represented by gray circles, lateral inhibitory connections by gray lines (only the inhibitory projections from the central neuron to its neighbors are shown), and excitatory afferents by black lines. (B) Hypothetical auditory activity pattern with a drop toward high frequencies, as it could for example occur in the spontaneous activity of the auditory nerve after noise-induced hearing loss. (C) Activity pattern in the lateral-inhibition network driven by the input shown in (B). An activity peak is generated at the edge of the input pattern but below the region of hearing loss.

Figure 2

Schematic illustration of homeostatic plasticity models. The “knobs” represent the effective response gain of neurons in the central auditory system, determined by the strength of excitatory and inhibitory synapses as well as intrinsic neuronal excitability. (A) Before homeostatic plasticity: noise-induced hearing loss (example audiogram in the top panel) has reduced mean and spontaneous activity in the central auditory system (bottom panels). (B) After homeostatic plasticity: the response gain has been increased to restore the mean activity of central auditory neurons back to its target level. However, spontaneous activity is amplified through the increased gain, giving rise to increased spontaneous firing rates in the region of hearing loss.