Auditory cortex modelled as a dynamical network of oscillators: understanding event-related fields and their adaptation

Abstract

Adaptation, the reduction of neuronal responses by repetitive stimulation, is a ubiquitous feature of auditory cortex (AC). It is not clear what causes adaptation, but short-term synaptic depression (STSD) is a potential candidate for the underlying mechanism. In such a case, adaptation can be directly linked with the way AC produces context-sensitive responses such as mismatch negativity and stimulus-specific adaptation observed on the single-unit level. We examined this hypothesis via a computational model based on AC anatomy, which includes serially connected core, belt, and parabelt areas. The model replicates the event-related field (ERF) of the magnetoencephalogram as well as ERF adaptation. The model dynamics are described by excitatory and inhibitory state variables of cell populations, with the excitatory connections modulated by STSD. We analysed the system dynamics by linearising the firing rates and solving the STSD equation using time-scale separation. This allows for characterisation of AC dynamics as a superposition of damped harmonic oscillators, so-called normal modes. We show that repetition suppression of the N1m is due to a mixture of causes, with stimulus repetition modifying both the amplitudes and the frequencies of the normal modes. In this view, adaptation results from a complete reorganisation of AC dynamics rather than a reduction of activity in discrete sources. Further, both the network structure and the balance between excitation and inhibition contribute significantly to the rate with which AC recovers from adaptation. This lifetime of adaptation is longer in the belt and parabelt than in the core area, despite the time constants of STSD being spatially homogeneous. Finally, we critically evaluate the use of a single exponential function to describe recovery from adaptation.

Laminar Neural Dynamics of Auditory Evoked Responses: Computational Modeling of Local Field Potentials in Auditory Cortex of Non-Human Primates

“…Studies based on the Human Neocortical Neurosolver (Kohl et al, 2022; Neymotin et al, 2020) suggest that the P1 and P2 components are partly generated by upward currents within the dendrites of cortical pyramidal neurons due to bottom-up inputs, while the N1 component is partly generated by downward currents associated with top-down inputs. Studies using a multi-column model of the auditory cortex (Hajizadeh et al, 2019, 2021, 2022) further emphasize the dependence of current orientation on the cellular location of the active synapses (e.g., apical/somatic), synaptic type (i.e., excitatory/inhibitory), inter-column connection type (e.g., feedforward, feedback, within-field), and folding of the cortex (i.e., the neuroanatomical topography of the cortical surface). At the level of a cortical column, our analyses of the cell-specific contributions to ECDs (Figure 7A) suggests that initial thalamic input primarily contributes to P1, subsequent early activity of E and PV neurons (both BF and non-BF columns) primarily contributes to N1, and late SOM activity (especially in non-BF columns) joins the contribution to P2.…”

“…This single-column model relates the ERP/ERF to intracellular currents in pyramidal long dendrites but leaves the origins of the sequences of inputs unexplained. This issue was addressed by a rate-based core-belt-parabelt model that includes an entire network of brain regions comprising auditory cortex (208 cortical columns, each column consisting of one excitatory and one inhibitory population), where ERFs are considered as the weighted sum of spatially distributed damped harmonic oscillators emerging out of coupled excitation and inhibition (Hajizadeh et al, 2019(Hajizadeh et al, , 2021(Hajizadeh et al, , 2022. This model provides a holistic perspective on the generation of ERFs.…”

“…Invasive studies in animal models can contribute substantially to this aim by allowing the recording/imaging of layer-specific local field potentials (LFP) and cell-type specific neural firing (see Sections 1.1 and 1.2). Such animal model studies have already provided considerable information about lamina-specific neural activity (Bruyns-Haylett et al, 2017;Hajizadeh et al, 2019Hajizadeh et al, , 2021Hajizadeh et al, , 2022Kohl et al, 2022;Neymotin et al, 2020;Sumner et al, 2021) and enhanced our understanding of the functional roles of various types of inhibitory interneurons in cortical processing (Aponte et al, 2021;Blackwell & Geffen, 2017;Liu et al, 2019;Liu & Kanold, 2021;Studer & Barkat, 2022). However, to more thoroughly elucidate the underlying neural generators of evoked responses and their relationship with information processing in the brain, it is necessary to mechanistically link together the available information about cell types and local neuronal circuits in the brain, intracraniallyrecorded LFPs, and extracranially-measured EEG and MEG signals.…”

Hemispheric difference of adaptation lifetime in human auditory cortex measured with MEG

“…Further, Hajizadeh et al (2022) found that, in their model, the factors that govern the superposition of network activity into ERFs-the MEG efficiency and the input efficiency introduced above-are SOI-specific. This could mean that the jitter of the SOI-specific N1m-peak amplitudes around the fitted curve, as depicted in Figure 2, is not just due to noise but also reflects this SOI-specific effect.…”

“…The SOI-dependence of the N1m-peak amplitudes of ERFs, as shown in Figures 1a and b and the right hemisphere (open symbols). This relationship can be well approximated by a single saturating exponential function (Hajizadeh et al, 2022;Lu et al, 1992):…”