Neuromodulation strongly impacts sensory processing by influencing neuronal excitability or synaptic processes in neuronal circuits (Hasselmo and Giocomo, 2006; Lucas-Meunier et al., 2003; Metherate, 2011; Muñoz and Rudy, 2014; Picciotto et al., 2012). Acetylcholine (ACh) is a widely distributed neuromodulator throughout the brain, including the AC, and modulates processes such as attention, learning, memory, arousal, sleep, and/or cognitive reinforcement (Batista-Brito et al., 2018; Dalley et al., 2004; Franklin and Frank, 2015). The main source of ACh to the AC is the basal forebrain (Chavez and Zaborszky, 2017; Mesulam, 2013; Zaborszky et al., 2008). In the auditory system, cholinergic modulation is known to alter frequency response areas generating changes across frequency tuning, decreasing the acoustic threshold at the characteristic frequency, and changing the encoding of the spectral representation of many auditory neurons (Ma and Suga, 2005; Metherate, 2011). Thus, ACh promotes neuronal and synaptic plasticity at different temporal scales (Kamke et al., 2005; Kilgard and Merzenich, 1998).

Here, we characterized the effect of ACh on stimulus-specific adaptation (SSA), a type of neuronal adaptation found in the AC. SSA is elicited by an oddball paradigm, which comprises a pattern of repeating sounds (standards), interrupted by a low-probability and unexpected sound (deviant). The deviant usually differs in frequency, but could differ on any other physical dimension from the standard sound, or otherwise violate a pattern of regularity established by the standard (Chen et al., 2015; Nieto-Diego and Malmierca, 2016; Parras et al., 2017; Ulanovsky et al., 2003; von der Behrens et al., 2009).

Thus, neurons that exhibit SSA adapt specifically to the standard stimulus but resume their firing when a deviant stimulus appears. SSA has been proposed to be a neuronal correlate of ‘mismatch negativity (MMN)’, an evoked potential obtained in human and animal electroencephalographic studies using the oddball paradigm (Nieto-Diego and Malmierca, 2016; Ulanovsky et al., 2003). Hence, SSA has been also referred to as nMM in animal models (Casado-Román et al., 2020; Lao-Rodríguez et al., 2023; Parras et al., 2020; Parras et al., 2017; Malmierca et al., 2019; Carbajal and Malmierca, 2020), a nomenclature that highlights deviance detection, as well as other underlying processes other than neuronal adaptation. Because nMM is considered a form of short-term plasticity (Ogawa and Oka, 2015) and ACh has been shown to play a role in this type of neural plasticity (Marshall et al., 2016; Moran et al., 2013; Parr and Friston, 2017; Vossel et al., 2012) it is plausible that ACh may be involved in the generation and/or modulation of nMM. Furthermore, it has been shown that ACh differentially modulates the neural response to the standard stimulus in neurons of the IC (Ayala and Malmierca, 2015).

Currently, MMN and nMM are best explained by the predictive coding theory (Friston and Sporns, 2008; Friston, 2005). According to the predictive coding framework, higher-level cortical areas generate predictions about sensory streams that are conveyed in a top-down manner to lower hierarchical levels, to suppress the ascending neuronal activity evoked by sensory events that can be anticipated. However, when current predictions do not match the sensory inputs, the lower levels then send forward bottom-up prediction errors to higher hierarchical levels (Friston and Kiebel, 2009).

In the setting of an oddball paradigm, every evoked response is considered to be a prediction error response. When the stimulus does not change, sensory learning enables better predictions to progressively attenuate the response to standards until an oddball or deviant stimulus is encountered — and a large prediction error is seen. The prediction error response to oddball stimuli may be further augmented by increasing the precision or gain of prediction errors: predictive coding models weigh sensory prediction errors by their precision, which is the inverse of the variance. Precision weighting of prediction errors, therefore, encodes the confidence in the accuracy of the information (i.e. prediction errors) in terms of their signal-to-noise ratio (Parr and Friston, 2017). In neurobiological terms, precision has been suggested to be mediated by synaptic gain modulation; namely, the sensitivity of postsynaptic responses to neural inputs (Adams et al., 2022), and is likely implemented by cholinergic neuromodulation (Moran et al., 2013; Yu and Dayan, 2005; Yu and Dayan, 2002). Increasing the gain of certain neuronal populations — e.g., superficial pyramidal cells reporting prediction errors – affords them privileged access to higher levels of processing and ensuing Bayesian belief updating. This can be likened to attentional gain in psychological terms – or increasing the Kalman gain in terms of Bayesian filtering.

According to the predictive coding model, there are, therefore, two mechanisms underlying the MMN/nMM (Auksztulewicz and Friston, 2016; Carbajal and Malmierca, 2018; Friston and Sporns, 2008; Harms et al., 2021; Parras et al., 2017). First, nMM could reflect the repetition suppression (RS) of the response to the predictable stimuli (standards) due to sensory learning – that resolves sensory prediction errors as predictions become more accurate. In addition, nMM could also reflect an increase in the precision or gain afforded prediction errors, when the (oddball) stimulus cannot be predicted; enabling the ascending prediction errors to revise higher-level representations about the auditory stream. In short, oddball stimuli elicit an enhanced neural response when an unexpected (deviant) stimulus is presented. Repetition suppression and prediction error modulation can be distinguished using control sequences (Ruhnau et al., 2012), and there is now evidence in both humans and rodents that MMN/nMM receives contributions from both prediction error (modulation) and repetition suppression (sensory learning) at various levels of the auditory system (Ishishita et al., 2019; Parras et al., 2017).

The main goal of the present study was to determine the role that ACh plays in the modulation of prediction errors and repetition suppression in the rat AC; utilizing control sequences to disambiguate these mechanisms (Nieto-Diego and Malmierca, 2016; Parras et al., 2017). We used microiontophoretic injections of ACh to determine how ACh neurotransmission affects the responses of AC neurons that exhibit nMM, and to establish the role of ACh on prediction error modulation and repetition suppression, in both primary and secondary AC areas.

To explore the influence of cholinergic modulation on nMM, we recorded a total of 113 units in the AC before, during and after the microiontophoretic injection of acetylcholine in the rat. Recording depths ranged from 120–1191 μm including neurons from all layers.

To allocate each recorded neuron to a specific field in the AC, we recorded the frequency response area (FRA) and analyzed the topographical distribution of CF (characteristic frequency) for each unit. Each recording was assigned to a dorsoventral and rostrocaudal coordinate system relative to bregma as in previous studies (Nieto-Diego and Malmierca, 2016; Parras et al., 2017; Polley et al., 2007). This analysis allowed us to pool the data from all animals (Figure 1a) and construct a synthetic map of the CF across the entire rat auditory cortex (Nieto-Diego and Malmierca, 2016; Parras et al., 2017). Similar to these previous studies, we found a high-frequency reversal zone between ventral auditory field (VAF, caudally) and anterior auditory field (AAF, rostrally), a low-frequency reversal zone between A1 and posterior auditory field (PAF, dorso-caudally), and a high-frequency reversal between VAF and supra-rhinal auditory field (SRAF, ventrally). Thus, we could reliably define the lemniscal (A1, AAF, and VAF) and non-lemniscal (SRAF, PAF) auditory cortical fields as shown in Figure 1b.

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ACh modulates the magnitude of neuronal responses and nMM

After the FRA was identified, we selected a pair of pure tones (10–30 dB above the minimum threshold) within the FRA at each recording site to test the adaptation of the neuronal response under the oddball paradigm. To do so we quantified nMM using the common SSA index (CSI). CSI values may range from –1 to +1, with negative values for units that respond more to standard stimuli and positive values for units that respond more to deviant stimuli (see Methods). In the following, we describe the effect of ACh on nMM (Figure 2; Figure 3; Figure 4; Figure 5; Figure 6), in terms of prediction error modulation and repetition suppression (Figure 7). Figure 2 illustrates representative responses of two units from the lemniscal auditory pathway — during and after the injection of ACh — that showed a change in the CSI during ACh release (Figure 2a and b, decreased and increased CSI, respectively). Similarly, the non-lemniscal, divisions (high-order regions) of the AC (Figure 3) also show decreased and increased CSI during ACh application (Figure 3a and b; respectively). In both Figures 2 and 3, peri-stimulus time histograms (PSTH) are also depicted for standard- and deviant stimuli (blue and red lines, respectively). On average, the firing rate decreased during the application of ACh (mean control: 5.8±7.0 spikes/s, mean ACh: 4.7±10.3 spikes/s; Wilcoxon signed-rank test, p<0.001). Most neurons recovered their basal firing rates after 60–90 min after the ACh injection (recovery mean: 6.5±8.1 spikes/s, Wilcoxon signed-rank test, p=0.4, control vs. recovery).

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Given the changes in the FRAs observed in individual units during the application of ACh (Figures 2 and 3), we checked for bandwidth changes at the population level. For that, we measured the FRA bandwidth in octaves at 10 and 30 dB above the minimum threshold (BW10 and BW30, respectively). Numerous individual units showed either broadening or sharpening of their FRA bandwidth during ACh application, which were averaged out at the population level (Figure 4a, b). Indeed, no significant changes occur in neither BW10 (control: 0.76±0.57 oc taves; effect: 0.88±0.75 oc taves; Wilcoxon signed-rank test, p=0.248) nor BW30 (control: 1.33±0.73 oc taves; effect: 1.43±0.80 oc taves; Wilcoxon signed-rank test, p=0.664) (Figure 4c).

Next, we analyzed the effect of ACh on the CSI at the population level (n=85; comprising the neurons in our sample with significant responses to both f₁ and f₂ auditory stimuli). Figures 2 and 3 illustrate four examples of units from lemniscal and non-lemniscal subdivisions before, during and after the application of ACh. Modulation of CSI levels was observed in all individual examples. ACh produced a significant increase of the CSI value in 26% (Figure 5a, purple dots; 22/85, bootstrapping, 95% c.i.) and a significant decrease in 31% of the neurons recorded (Figure 5a, orange dots; 26/85, bootstrapping, 95% c.i.). Thirty-seven units (43%) were unaffected by ACh. The average CSI during ACh injection (CSI = 0.54±0.27) was similar to that of the control condition (CSI = 0.57±0.23; Wilcoxon signed-rank test, p=0.359; Figure 5b).

We analyzed the effect of ACh on the response to the deviant- or standard stimuli, separately. We observed that ACh produced a significant decrease in the mean firing rate in response to the deviant tones (Figure 5c; 11.75±17.46 Hz), as compared to the control condition (16.51±16.19 Hz; Wilcoxon signed-rank test, p<0.001), as well as in the mean firing rate in response to standard tones (Figure 5c; 4.58±6.38 vs. 3.97±9.90 Hz, control vs ACh; Wilcoxon signed-rank test, p=0.001).

In order to establish how ACh affects discriminability of deviant- and standard stimuli — i.e., how well the stimulus identity can be decoded from the neuronal firing rate – we calculated the area under the curve (AUC) of the receiver operating characteristic (ROC) for each tone (Figure 5d, left panel) and the Mutual Information to determine how much the response distributions for deviant- and standard stimuli overlap before and after ACh injections and how well the stimulus identity can be decoded from the neuronal firing rate. AUC indicates how different the neuronal responses to deviant- and standard stimuli are, and thus, how well a neuron is able to discriminate between both types of stimuli, considering that the neuron would respond differently to them (in terms of firing rate). Completely overlapping firing rate distributions (in response to deviants and standards) would yield an AUC = 0.5, while separate distributions would result in AUC = 1. On the other hand, Mutual Information is defined as the reduction in the uncertainty of one variable (in this case the neuronal response) due to the knowledge of another (in this case, the type of stimulus; Timme and Lapish, 2018). In other words, this measure reflects how much information a neural response (firing rate) contains about the type of stimulus that is played. In practical terms, high Mutual Information implies that a neuron is able to discriminate between stimuli, and thus respond differently to them. This analysis shows that the mean AUC decreased significantly during the application of ACh (0.74±0.13 vs. 0.67±0.15, control vs. effect; Wilcoxon signed-rank test, p<0.001). The same happened for Mutual Information (Figure 5d, right panel; 0.09±0.07 vs. 0.07±0.08, control vs. effect; Wilcoxon signed-rank test, p=0.003). These AUC and Mutual Information analyses indicate that under the influence of ACh, neurons are less able to discriminate deviant stimuli from standard stimuli.

ACh affects neuronal mismatch, modulating prediction error responses but not repetition suppression

According to the predictive coding framework, nMM can be split into two functionally distinct components: prediction error modulation (i.e. an increased response to an oddball caused by the violation of a regularity) and repetition suppression (i.e. reduction in the response to a standard caused by sensory learning of a repeated stimulus). These differential effects can be quantified by comparing the responses to the oddball paradigm with a suitable control sequence (Figure 7—figure supplement 1). Therefore, to test if ACh modulates prediction error signaling in AC, we also recorded the cascade control sequence (for the corresponding oddball sequences) in 66 neurons of the sample, using an approach previously published (Carbajal and Malmierca, 2018; Parras et al., 2017; Ruhnau et al., 2012; Valdés-Baizabal et al., 2019). First, we calculated the normalized spike counts in response to the cascade control, deviant, and standard stimuli (CAS, DEV, and STD, respectively; see Methods). ACh led to a significant decrease in the DEV responses in those units where SI decreased (Figure 7a; Wilcoxon Signed Rank test, p<0.001) and to a significant increase in those units where SI increased (Figure 7b; p=0.011). The opposite effect occurs on STD stimuli, increasing in units where SI decreased (Figure 7a; p<0.001) and decreasing in units where SI increased (Figure 7b; p<0.001). While this trend is very clear, there were no significant changes in CAS (p=0.091 and p=0.072, for SI decrease and increase, respectively).

Using the normalized CAS, DEV, and STD responses, we computed the indexes iMM, iPE, and iRS before and after the ACh injection in SI decrease units (Figure 7c, iMM: 0.63±0.19 vs. 0.13±42; iPE: 0.29±0.27 to -0.05±0.38; iRS: 0.34±0.20 vs. 0.17±0.49; control vs. ACh, respectively) and SI increase units (Figure 7d, iMM: 0.47±0.31 vs. 0.67±0.22; iPE: 0.15±0.36 vs. 0.33±43; iRS: 0.32±0.30 vs. 0.34±0.31). iMM is equivalent to the CSI, but using normalized response values, and since it includes information about the cascade control, it can be divided into prediction error and repetition suppression components (iPE and iRS, respectively; see Methods and Figure 7—figure supplement 1c). Significant effects of ACh administration were found for iMM and iPE, both in SI decrease units (iPE: p<0.001; iMM: p<0.001; Wilcoxon signed-rank test; Figure 7c) and SI increase units (iPE: p=0.023; iMM: p<0.001; Wilcoxon signed-rank test; Figure 7d), while iRS remained unchanged (SI decrease units iRS: p=0.121; SI increase units iRS: p=0.675; Wilcoxon signed-rank test).

Under the predictive coding framework, the precision of prediction errors encodes the confidence or reliability afforded such errors (Friston and Sporns, 2008), enabling precise prediction errors to access higher levels of the auditory hierarchy. To test whether neuronal responses show differential effects of precision weighting — depending on their level of deviance sensitivity – we split our sample into two similar-sized groups (of frequencies), using the median SI as cutoff (0.6143: SI before the application of ACh): low SI (n=65 frequencies with cascade control) and high SI (n=66 frequencies with cascade control) (Figure 8a–c). Given this grouping, we found that ACh decreased significantly iMM (0.72±0.13 vs. 0.52±0.36, control vs. effect; p<0.001) and iPE (0.33±0.33 vs. 0.145±0.48, control vs. effect; p=0.004) in high SI frequencies, while only decreasing iRS significantly in low SI frequencies (0.28±0.25 vs. 0.18±0.36, control vs. effect; p<0.037) (Figure 8a–c, top row).

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Given the divergent defects of ACh on putative prediction error modulation, we hypothesized that acetylcholine would increase the variance of deviance-sensitive responses over groups of cells. We calculated the variance of the above distributions of predictive coding indexes (iMM, iPE, and iRS) as an estimate of the (inverse of) their precision, as well as its 95% confidence interval using a 1000-sample bootstrap strategy, and then used an F-test to check for differences in the variances before and during ACh application (Figure 8a–c, bottom row). These results demonstrate that ACh application significantly increases the variance of iMM and iRS for both low SI and high SI groups, but interestingly ACh also significantly increases the variance of iPE only for the high SI group.

Then, we repeated the same variance analysis grouping the frequencies based on the anatomical layer location (depth) of the recording sites (Figure 8d–f). In this case, we split the frequencies into deep and superficial, with a depth cutoff of 650 µm, which is near the boundary between layers IV and V in the rat AC (Games and Winer, 1988; Malmierca, 2015). In this case, we found no significant effect of ACh on iMM, iPE, nor iRS for none of the depth groups (Figure 8d–f, top row). However, the general trend was an increase in the variance for all indexes and depth groups during the application of ACh (except for iPE in superficial units), which was significant for all indexes in the case of deep units only (Figure 8d–f, top row).

Thus, our results indicate that ACh produces a general increase in the variance for iMM, iPE, and iRS. This general increase occurs regardless of the baseline SI value, but is more noticeable for neurons located in the infragranular layers.

ACh effect on nMM as a function of topographic distribution

A remaining question is whether the effect of ACh is uniformly distributed across AC neurons in different fields and layers. Figure 9 shows the CSI (first row) and spike count levels for the standard and deviant tones (middle and bottom rows, respectively) before and after ACh application, as well as the difference between the ACh and control conditions. The highest levels of CSI in the control condition (left column) were found in SRAF. ACh application (middle column) produced an overall increment of CSI in AAF and a decrement in the rest of the fields, however, such changes did not reach statistical significance (p>0.79 in all fields, Wilcoxon signed-rank test with Holm-Bonferroni correction). Regarding the responses to the standard stimuli (middle row), ACh application caused an increase of the firing rate in A1 and PAF, and a decrease in AAF, VAF, and SRAF, but such changes were only significant in AAF (p=0.001, Wilcoxon signed-rank test with Holm-Bonferroni correction). By contrast, the firing rates in response to the deviant sounds (bottom row) decreased in all fields during ACh application, with significant changes in AAF, VAF, and SRAF (p=0.011, p=0.026, and p=0.009, respectively; Wilcoxon signed-rank test with Holm-Bonferroni correction).

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In order to better understand the effect of ACh on the responses to the individual frequencies of the oddball paradigm (f₁ and f₂), we calculated the SSA index (SI) independently for each of the paradigm frequencies. We found that ACh was more likely to cause an SI decrease in all AC fields except for AAF, where ACh was more likely to cause an SI increase (Figure 10a; Figure 10b) shows the distribution of recording depths within the AC, for those units that showed a significant SI decrease (orange) or increase (purple) during the application of ACh. The shaded background shows the probability density function for those distributions. We found that frequencies that experienced an SI decrease during ACh application were most likely located in the granular layer (layer IV) or the upper infragranular regions (dorsal parts of layer V). In contrast, frequencies that experienced an SI decrease during ACh application were more likely located in deeper infragranular regions (layers V and VI).

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We recorded neuronal responses from the AC under an auditory oddball paradigm while performing microiontophoretic applications of ACh to investigate the role of the cholinergic system in the processing of unexpected or surprising events. Furthermore, we employed cascade sequences to control for repetition suppression and assess cholinergic effects on prediction error relative to repetition suppression. Our results show that (1) ACh modulates nMM, evidenced by decrements or increments of the neuronal mismatch indexes (iMM, iPE, and iRS). The decreased SI is due to a diminished response to the deviant tone while increased SI is the consequence of a diminished response to the standard sounds (Figure 6, and examples in Figures 2–3); (2) ACh exerts a global and distributed effect on AC neurons that is layer-specific but field unspecific (Figures 9 and 10); (3) The decreased- and increased SI are driven by the effects of ACh on iPE but not iRS (Figure 7), and (4) ACh increases the variance of iMM, iPE, and iRS. Together, these findings reveal how ACh modulates prediction error signals and repetition suppression differentially in the AC, likely gating these signals, as they ascend beyond the auditory cortex to frontal, higher cognitive regions.

ACh increases the amplitude of AC neuronal responses to sound stimulation, decreases the auditory threshold, and sharpens the receptive field in the AC. (Edeline, 2012; Irvine, 2018a; Irvine, 2018b; Kilgard and Merzenich, 1998; Ma and Suga, 2005; Metherate, 2011; Metherate et al., 1992; Puckett et al., 2007). These ACh-mediated effects are produced by a rapid disinhibition of neuronal responses, modifying synaptic strength, enhancing excitatory-inhibitory balance, and reorganizing cortical circuits promoting cortical plasticity (Froemke et al., 2007; Irvine, 2018a; Irvine, 2018b; Metherate, 2011; Picciotto et al., 2012). But ACh also produces direct inhibitory effects leading to a decrease of cortical responses (Colangelo et al., 2019; Dasgupta et al., 2018; Eggermann and Feldmeyer, 2009; Qi and Feldmeyer, 2022). Our results confirm these mixed effects (excitatory and inhibitory, during the application of ACh) at the neuronal level. However, when looking at the population level, the overall effect in our sample was a differential reduction of the responses to either deviant or standard stimuli. Apart from the direct hyperpolarizing effect of ACh on some cell types, it is also possible that some of the recorded neurons received direct inhibitory inputs, and those inhibitory neurons were the ones affected by ACh. By increasing the firing rates of such inhibitory interneurons, the observed overall effect of ACh could lead to a reduction of the activity of the recorded neurons. An important functional motif — for state modulation in the cortex — is through disinhibition consisting of parvalbumin-, somatostatin-, and vasoactive intestinal peptide- expressing interneurons. When the latter receives cholinergic projections from the basal forebrain, they remove the inhibition on layer 2/3 pyramidal neurons exerted by somatostatin interneurons (Fu et al., 2014). The connectivity relationships among inhibitory neurons seem to be critical. Moreover, it has been recently described how each of those cell types responds differently to deviants and standards in the auditory cortex (Yarden et al., 2022). ACh plays important roles in arousal, attention, and sensory learning (Hasselmo, 1999; Hasselmo and Sarter, 2011; Metherate, 2011; Picciotto et al., 2012; Sarter et al., 2001; Weinberger, 2004). Thus, it is not surprising that ACh may have a critical role in shaping nMM, which has many properties in common with behavioral habituation to a repeated stimulus and can be considered a simple form of learning (Irvine, 2018a; Nelken, 2014; Netser et al., 2011). Furthermore, a recent study in rats has shown that the administration of muscarinic ACh receptor agonists and antagonists affected MMN amplitude in A1 and PAF (Schöbi et al., 2021), suggesting that the cholinergic system modulates the connectivity between those areas during the occurrence of deviant sounds. But to the best of our knowledge, the present study demonstrates a specific effect of ACh on prediction error responses.

In the IC, ACh selectively increased the response to the repetitious standard stimulus (Ayala and Malmierca, 2015), while we show here that ACh in the AC not only increases the responses to the unexpected deviant sounds leading to an increase of SI (Figure 6d) but also decreases responses to the repeated standard that result in a diminished CSI index (Figure 6c). This divergent effect of ACh on subcortical and cortical nMM may be related to the different origin of ACh and/or the unique organization of neuronal circuitries in IC and AC (Figure 11). While the sources of ACh to IC emerge from the pedunculopontine and laterodorsal tegmental regions in the brainstem (Motts and Schofield, 2009), those to AC originate in the basal forebrain (Chavez and Zaborszky, 2017).

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According to the predictive coding formulations, cortical areas send predictions to lower hierarchical areas to inhibit neuronal populations encoding prediction errors. When top-down predictions match bottom-up sensory input, the prediction error response is attenuated. Conversely, those lower areas send error information to higher hierarchical centers when event predictions fail to drive higher-level representations and improve the top-down predictions. If a stimulus is not predicted, the difference between predicted and received inputs yields large prediction errors, while if a stimulus is constantly repeated, sensory learning enables more accurate predictions and a decrease in prediction error responses – a phenomenon referred to as repetition suppression (Auksztulewicz and Friston, 2016). Thus, nMM can be explained under the predictive coding framework (Carbajal and Malmierca, 2018; Carbajal and Malmierca, 2020; Parras et al., 2017; Malmierca et al., 2019), and recent studies that found LFP and spiking responses to omitted tones in the rat auditory cortex (Auksztulewicz et al., 2023; Lao-Rodríguez et al., 2023), among others, strongly support this interpretation. However, predictions and prediction errors may also be modulated in a context-dependent manner, meaning that responses can be modulated and given precedence depending on the context in which the stimulus is perceived (Keller and Mrsic-Flogel, 2018). The source of such a modulating or gating signal may arise from neuromodulatory inputs (Fu et al., 2014; Moran et al., 2013; Pinto et al., 2013; Polack et al., 2013; Thiele and Bellgrove, 2018) that can not only gate plasticity (Kilgard and Merzenich, 1998; Martins and Froemke, 2015; Weinberger, 2004), but also change the balance of top-down versus bottom-up influence (Yu and Dayan, 2002).

Within the predictive coding framework, classical neuromodulators such as ACh are often thought to increase the precision of prediction error signaling (Auksztulewicz et al., 2018; Auksztulewicz and Friston, 2016; Feldman and Friston, 2010; Moran et al., 2013). Indeed, Yu and Dayan, 2005; Yu and Dayan, 2002 proposed that ACh levels reflect the ‘expected uncertainty’ (when there is less confidence in the prediction) associated with top-down information and modulate the interaction between top-down and bottom-up processing in determining the appropriate neural representations for inputs (Auksztulewicz et al., 2017; Yu and Dayan, 2005; Yu and Dayan, 2002). Accordingly, the precision of prediction errors would encode the confidence or reliability afforded by such errors (Friston, 2005), increasing the influence of precise prediction errors on higher-level processing. Thus, the canonical view – within the predictive coding framework — is that precision would affect postsynaptic gain through the action of neuromodulators such as ACh, enhancing the responses of units broadcasting prediction errors. Using computational neuronal modeling and EEG recordings under oddball paradigms in subjects manipulated pharmacologically with galantamine a competitive inhibitor of acetylcholinesterase, Moran et al., 2013 have shown that ACh enhances the precision of bottom-up synaptic transmission in cortical hierarchies by optimizing the gain of supragranular pyramidal neurons, amounting to an increase in the precision of prediction error signaling. Thus, our results and those from Moran and colleagues (2013) suggest that ACh mediates the representation of precision.

Our results are consistent with this predictive coding picture; particularly when noting that an increase in precision leads to a representational sharpening (Kok et al., 2012a), in which the majority of (prediction error) neurons are suppressed and a few neurons encoding precise prediction errors are enhanced (Friston, 2018; Kok et al., 2012a; Shipp, 2016). This is consistent with the effect of acetylcholine on average over neurons studied and the differential effect on standard and oddball responses; dependent upon their SI.

ACh increases the variance of the prediction error component of neuronal responses in those units with higher SI (Figure 8b, top), especially in units with strong deviance detection properties (Figure 8b, bottom). This finding suggests that ACh plays a key role in encoding precision or uncertainty. Interestingly, ACh not only increases the variance of prediction error responses, but also the variance of repetition suppression (Figure 8c).

A change in the precision of prediction errors may denote — or alert to — a variable environment, where it is difficult to extract rules and only generate weak (and less useful) predictions. Under this scenario, some fluctuations in the sensory input can be expected, assigning less precision to prediction errors (Yon and Frith, 2021). If so, ACh would adjust the predictions’ confidence, so we would not be surprised by small changes in the environment, since they are likely, expected noisy, fluctuations. Otherwise, an excessive confidence on predictions in a volatile environment may override the incoming evidence, leading to the false inference of expected by absent events (Yon and Frith, 2021). Attention is thought to boost the precision of prediction errors (Kok et al., 2012b), and our experiments carried out under anesthesia clearly lack attention, where baseline cholinergic activity is low, making it difficult to interpret how the results generalize to more ecologically valid situations (Minces et al., 2017). Interestingly, there is a wealth of data suggesting that minimal impairments to the encoding or defective signaling of precision or uncertainty are sufficient to explain psychopathic conditions such as schizophrenia or autism (Friston, 2023). Furthermore, while we are not aware of any known direct connection between auditory processing disorders and ACh, individuals with auditory processing disorders do have difficulties with auditory selective attention, so perhaps one could speculate that ACh, by modulating SSA/prediction error, could impact encoding of salient events, and if disrupted could lead to problems with selective attention. Moore, 2006; Moore, 2012 speculated that auditory processing disorders may arise from unbalanced processing in bottom-up and top-down contributions, i.e., there might be some aspects of predictive processing altered. A recent study (Felix et al., 2019) analyzed altered temporal processing at the level of the brainstem in α₇-subunit of the nicotinic acetylcholine receptor (α₇-nAChR)-deficient mice. After studying α₇-nAChR knockout mice of both sexes and wild-type colony controls, they concluded that the malfunction of the CHRNA7 gene that encodes the α₇-nAChR may contribute to degraded spike timing in the midbrain, which may underlie the observed timing delay in the ABR signals. These authors (Felix et al., 2019) proposed that their findings are consistent with a role for the α₇-nAChR in types of neurodevelopmental and auditory processing disorders. There is also evidence of cholinergic system disfunction being related to the pathophysiology of Alzheimer’s disease (Pérez-González et al., 2022). For instance, disfunction of the synapses of cholinergic neurons in the hippocampus and nucleus basalis of Meynert, as well as decreased choline acetyltransferase activity, is associated with memory disorders in Alzheimer’s disease (Hampel et al., 2018). Also, Alzheimer’s disease patients show reduced amounts of the vesicular ACh transporter in some brain areas (Aghourian et al., 2017). Finally, cholinesterase inhibitors seem to have some favorable effects in the treatment of Alzheimer’s disease patients (Sharma, 2019).

It is unclear to what degree the amount of agonist liberated used iontophoretic application of cholinergic agonists emulates a relevant physiological condition (Minces et al., 2017). The application of exogenous ACh, as we used here, could potentially surpass in physiological concentrations, making the results hard to extrapolate to a behaving animal. Furthermore, future studies should investigate the effects of different doses of ACh on auditory processing to determine if there is a dose-response relationship as well as the effect of muscarinic- and nicotinic receptors blockade to theoretically observe more akin, analogous physiological effects. Similarly, forthcoming studies using optogenetic techniques to activate only basal forebrain cholinergic neurons while also recording from auditory cortex neurons will allow a fine-grained investigation of the role of cortical acetylcholine on sensory representation. Moreover, because our study was carried out in anesthetized animals, it precludes generalization of the effects of ACh on behaving states (Quintela-Vega et al., 2023). Future studies using an awake preparation will confirm a definitive corroboration of the effect of ACh on precision and determine the behavioral relevance of these findings. The available neurobiological evidence suggests that precision is encoded by the synaptic gain of superficial pyramidal cells encoding prediction errors, which are controlled by neuromodulatory systems (e.g. dopaminergic, cholinergic) and/or synchronized neural activity (Feldman and Friston, 2010; Kok et al., 2012b). However, we do not find changes in prediction error precision in the superficial layers; our analysis suggests instead that the effects of ACh were more limited to the deep AC layers (Figure 8e). We observed a distinct and differential distribution of units throughout the cortical layers, contingent upon the influence of ACh on their selectivity index (SI). Units exhibiting an SI decrease during ACh application were predominantly located and biased in layers III-IV and its adjacent regions, while those demonstrating an SI increment during ACh application spanned from the deeper layer IV to layer VI (Figure 10b). In the initial conceptualizations of predictive coding, it was posited that prediction units reside in the infragranular layers (layers V and VI), with error units situated in supragranular layers (layers I – III) (Bastos et al., 2012; Friston, 2005). Alternatively, some models suggest that error units would be localized in the granular layer (layer IV), while prediction units would be detected in both supra- and infragranular layers biased competition model of predictive coding (Spratling, 2008). In our recordings, units displaying an SI reduction during ACh application were also those with a higher SI under baseline conditions (i.e. larger response to deviant than to standard stimuli), thereby potentially qualifying as error units. These units were identified in layer IV and neighboring areas, which appears to support the model proposed by Spratling, 2008. Furthermore, according to Eggermann and Feldmeyer, 2009, the application of ACh would filter weak sensory inputs to layer IV and amplify signals at superficial and deep layers, effectively increasing the signal-to-noise ratio. In contrast, units demonstrating an SI increase during ACh application could be considered potential prediction units; such units were detected in granular and deep layers but were absent in superficial layers. This observation aligns with both predictive coding models, as each postulates that deep layers contain prediction units. A very interesting study would have been to estimate whether the neurons we recorded were excitatory or inhibitory based on the spike shape. Indeed, we have previously shown that both types of responses show prediction error (Pérez-González et al., 2021) but ACh may affect them differentially and this potential divergence may explain some of the variance of the data. This would involve implementing a similar analysis as done in our previous study (Pérez-González et al., 2021), however, that study used single-unit recordings and here we used multi-unit recordings. In the present data, the recordings captured occasional spikes from other neurons which introduced alterations in the average spike shape, and thus precluded an accurate categorization. Therefore, we decided to discard this analysis for the sake of accuracy and appropriate scrutiny awaits future studies.

In conclusion, we have shown that ACh plays a multifold role in modulating nMM in AC, affecting the precision of prediction error signaling and gating prediction errors to hierarchically higher processing levels.

Source data in the manuscript contains the numerical data used to generate the figures.

1. 1. Adams RA
   2. Pinotsis D
   3. Tsirlis K
   4. Unruh L
   5. Mahajan A
   6. Horas AM
   7. Convertino L
   8. Summerfelt A
   9. Sampath H
   10. Du XM
   11. Kochunov P
   12. Ji JL
   13. Repovs G
   14. Murray JD
   15. Friston KJ
   16. Hong LE
   17. Anticevic A

   (2022) Computational Modeling of Electroencephalography and Functional Magnetic Resonance Imaging Paradigms Indicates a Consistent Loss of Pyramidal Cell Synaptic Gain in Schizophrenia

   Biol Psychiatry 91:202–215.

   https://doi.org/10.1016/j.biopsych.2021.07.024

   • Google Scholar
2. 1. Aghourian M
   2. Legault-Denis C
   3. Soucy JP
   4. Rosa-Neto P
   5. Gauthier S
   6. Kostikov A
   7. Gravel P
   8. Bédard MA

   (2017) Quantification of brain cholinergic denervation in Alzheimer’s disease using PET imaging with [¹⁸F]-FEOBV

   Molecular Psychiatry 22:1531–1538.

   https://doi.org/10.1038/mp.2017.183

   • PubMed
   • Google Scholar
3. 1. Auksztulewicz R
   2. Friston K

   (2016) Repetition suppression and its contextual determinants in predictive coding

   Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 80:125–140.

   https://doi.org/10.1016/j.cortex.2015.11.024

   • PubMed
   • Google Scholar
4. 1. Auksztulewicz R
   2. Friston KJ
   3. Nobre AC

   (2017) Task relevance modulates the behavioural and neural effects of sensory predictions

   PLOS Biology 15:e2003143.

   https://doi.org/10.1371/journal.pbio.2003143

   • PubMed
   • Google Scholar
5. 1. Auksztulewicz R
   2. Schwiedrzik CM
   3. Thesen T
   4. Doyle W
   5. Devinsky O
   6. Nobre AC
   7. Schroeder CE
   8. Friston KJ
   9. Melloni L

   (2018) Not all predictions are equal: “What” and “when” predictions modulate activity in auditory cortex through different mechanisms

   The Journal of Neuroscience 38:8680–8693.

   https://doi.org/10.1523/JNEUROSCI.0369-18.2018

   • PubMed
   • Google Scholar
6. 1. Auksztulewicz R
   2. Rajendran VG
   3. Peng F
   4. Schnupp JWH
   5. Harper NS

   (2023) Omission responses in local field potentials in rat auditory cortex

   BMC Biology 21:130.

   https://doi.org/10.1186/s12915-023-01592-4

   • PubMed
   • Google Scholar
7. 1. Ayala YA
   2. Malmierca MS

   (2015) Cholinergic modulation of stimulus-specific adaptation in the inferior colliculus

   The Journal of Neuroscience 35:12261–12272.

   https://doi.org/10.1523/JNEUROSCI.0909-15.2015

   • PubMed
   • Google Scholar
8. 1. Ayala YA
   2. Udeh A
   3. Dutta K
   4. Bishop D
   5. Malmierca MS
   6. Oliver DL

   (2015) Differences in the strength of cortical and brainstem inputs to SSA and non-SSA neurons in the inferior colliculus

   Scientific Reports 5:10383.

   https://doi.org/10.1038/srep10383

   • PubMed
   • Google Scholar
9. 1. Bastos AM
   2. Usrey WM
   3. Adams RA
   4. Mangun GR
   5. Fries P
   6. Friston KJ

   (2012) Canonical microcircuits for predictive coding

   Neuron 76:695–711.

   https://doi.org/10.1016/j.neuron.2012.10.038

   • Google Scholar
10. 1. Batista-Brito R
    2. Zagha E
    3. Ratliff JM
    4. Vinck M

    (2018) Modulation of cortical circuits by top-down processing and arousal state in health and disease

    Current Opinion in Neurobiology 52:172–181.

    https://doi.org/10.1016/j.conb.2018.06.008

    • PubMed
    • Google Scholar
11. 1. Carbajal GV
    2. Malmierca MS

    (2018) The neuronal basis of predictive coding along the auditory pathway: From the subcortical roots to cortical deviance detection

    Trends in Hearing 22:2331216518784822.

    https://doi.org/10.1177/2331216518784822

    • PubMed
    • Google Scholar
12. 1. Carbajal GV
    2. Malmierca MS

    (2020)

    The Senses: A Comprehensive Reference

    Novelty processing in the auditory system: detection, adaptation or expectation, The Senses: A Comprehensive Reference, Elsevier, 10.1016/B978-0-12-809324-5.24154-0.

    • Google Scholar
13. Preprint

    1. Casado-Román L
    2. Carbajal GV
    3. Pérez-González D
    4. Malmierca MS

    (2019) Prediction Error Signaling Explains Neuronal Mismatch Responses in the Medial Prefrontal Cortex

    bioRxiv.

    https://doi.org/10.1101/778928

    • Google Scholar
14. 1. Casado-Román L
    2. Carbajal GV
    3. Pérez-González D
    4. Malmierca MS

    (2020) Prediction error signaling explains neuronal mismatch responses in the medial prefrontal cortex

    PLOS Biology 18:e3001019.

    https://doi.org/10.1371/journal.pbio.3001019

    • PubMed
    • Google Scholar
15. 1. Chavez C
    2. Zaborszky L

    (2017) Basal Forebrain Cholinergic-Auditory Cortical Network: Primary Versus Nonprimary Auditory Cortical Areas

    Cerebral Cortex 27:2335–2347.

    https://doi.org/10.1093/cercor/bhw091

    • PubMed
    • Google Scholar
16. 1. Chen I-W
    2. Helmchen F
    3. Lutcke H

    (2015) Specific early and late oddball-evoked responses in excitatory and inhibitory neurons of mouse auditory cortex

    Journal of Neuroscience 35:12560–12573.

    https://doi.org/10.1523/JNEUROSCI.2240-15.2015

    • Google Scholar
17. 1. Colangelo C
    2. Shichkova P
    3. Keller D
    4. Markram H
    5. Ramaswamy S

    (2019) Cellular, synaptic and network effects of acetylcholine in the neocortex

    Frontiers in Neural Circuits 13:24.

    https://doi.org/10.3389/fncir.2019.00024

    • PubMed
    • Google Scholar
18. 1. Dalley JW
    2. Cardinal RN
    3. Robbins TW

    (2004) Prefrontal executive and cognitive functions in rodents: neural and neurochemical substrates

    Neuroscience and Biobehavioral Reviews 28:771–784.

    https://doi.org/10.1016/j.neubiorev.2004.09.006

    • PubMed
    • Google Scholar
19. 1. Dasgupta R
    2. Seibt F
    3. Beierlein M

    (2018) Synaptic release of acetylcholine rapidly suppresses cortical activity by recruiting muscarinic receptors in layer 4

    The Journal of Neuroscience 38:5338–5350.

    https://doi.org/10.1523/JNEUROSCI.0566-18.2018

    • PubMed
    • Google Scholar
20. 1. Duque D
    2. Wang X
    3. Nieto-Diego J
    4. Krumbholz K
    5. Malmierca MS

    (2016) Neurons in the inferior colliculus of the rat show stimulus-specific adaptation for frequency, but not for intensity

    Scientific Reports 6:24114.

    https://doi.org/10.1038/srep24114

    • PubMed
    • Google Scholar
21. 1. Edeline JM

    (2012) Beyond traditional approaches to understanding the functional role of neuromodulators in sensory cortices

    Frontiers in Behavioral Neuroscience 6:45.

    https://doi.org/10.3389/fnbeh.2012.00045

    • PubMed
    • Google Scholar
22. 1. Eggermann E
    2. Feldmeyer D

    (2009) Cholinergic filtering in the recurrent excitatory microcircuit of cortical layer 4

    PNAS 106:11753–11758.

    https://doi.org/10.1073/pnas.0810062106

    • PubMed
    • Google Scholar
23. 1. Feldman H
    2. Friston KJ

    (2010) Attention, uncertainty, and free-energy

    Frontiers in Human Neuroscience 4:215.

    https://doi.org/10.3389/fnhum.2010.00215

    • PubMed
    • Google Scholar
24. 1. Felix RA
    2. Chavez VA
    3. Novicio DM
    4. Morley BJ
    5. Portfors CV

    (2019) Nicotinic acetylcholine receptor subunit α₇-knockout mice exhibit degraded auditory temporal processing

    Journal of Neurophysiology 122:451–465.

    https://doi.org/10.1152/jn.00170.2019

    • PubMed
    • Google Scholar
25. 1. Franklin NT
    2. Frank MJ

    (2015) A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning

    eLife 4:e12029.

    https://doi.org/10.7554/eLife.12029

    • PubMed
    • Google Scholar
26. 1. Friston KJ

    (2005) A theory of cortical responses

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 360:815–836.

    https://doi.org/10.1098/rstb.2005.1622

    • PubMed
    • Google Scholar
27. 1. Friston K
    2. Sporns O

    (2008) Hierarchical models in the brain

    PLOS Computational Biology 4:e1000211.

    https://doi.org/10.1371/journal.pcbi.1000211

    • PubMed
    • Google Scholar
28. 1. Friston K
    2. Kiebel S

    (2009) Cortical circuits for perceptual inference

    Neural Networks 22:1093–1104.

    https://doi.org/10.1016/j.neunet.2009.07.023

    • PubMed
    • Google Scholar
29. 1. Friston K

    (2018) Does predictive coding have a future?

    Nature Neuroscience 21:1019–1021.

    https://doi.org/10.1038/s41593-018-0200-7

    • PubMed
    • Google Scholar
30. 1. Friston K

    (2023) Computational psychiatry: from synapses to sentience

    Molecular Psychiatry 28:256–268.

    https://doi.org/10.1038/s41380-022-01743-z

    • PubMed
    • Google Scholar
31. 1. Froemke RC
    2. Merzenich MM
    3. Schreiner CE

    (2007) A synaptic memory trace for cortical receptive field plasticity

    Nature 450:425–429.

    https://doi.org/10.1038/nature06289

    • PubMed
    • Google Scholar
32. 1. Fu Y
    2. Tucciarone JM
    3. Espinosa JS
    4. Sheng N
    5. Darcy DP
    6. Nicoll RA
    7. Huang ZJ
    8. Stryker MP

    (2014) A cortical circuit for gain control by behavioral state

    Cell 156:1139–1152.

    https://doi.org/10.1016/j.cell.2014.01.050

    • PubMed
    • Google Scholar
33. 1. Games KD
    2. Winer JA

    (1988) Layer V in rat auditory cortex: projections to the inferior colliculus and contralateral cortex

    Hearing Research 34:1–25.

    https://doi.org/10.1016/0378-5955(88)90047-0

    • PubMed
    • Google Scholar
34. 1. Hampel H
    2. Mesulam MM
    3. Cuello AC
    4. Khachaturian AS
    5. Vergallo A
    6. Farlow MR
    7. Snyder PJ
    8. Giacobini E
    9. Khachaturian ZS

    (2018) Revisiting the cholinergic hypothesis in alzheimer’s disease: Emerging evidence from translational and clinical research

    The Journal Of Prevention of Alzheimer’s Disease 6:1–14.

    https://doi.org/10.14283/jpad.2018.43

    • Google Scholar
35. 1. Harms L
    2. Parras GG
    3. Michie PT
    4. Malmierca MS

    (2021) The role of Glutamate Neurotransmission in mismatch negativity (MMN), a measure of auditory synaptic plasticity and change-detection

    Neuroscience 456:106–113.

    https://doi.org/10.1016/j.neuroscience.2020.01.046

    • PubMed
    • Google Scholar
36. 1. Hasselmo ME

    (1999) Neuromodulation: acetylcholine and memory consolidation

    Trends in Cognitive Sciences 3:351–359.

    https://doi.org/10.1016/s1364-6613(99)01365-0

    • PubMed
    • Google Scholar
37. 1. Hasselmo ME
    2. Giocomo LM

    (2006) Cholinergic modulation of cortical function

    Journal of Molecular Neuroscience 30:133–135.

    https://doi.org/10.1385/JMN:30:1:133

    • PubMed
    • Google Scholar
38. 1. Hasselmo ME
    2. Sarter M

    (2011) Modes and models of forebrain cholinergic neuromodulation of cognition

    Neuropsychopharmacology 36:52–73.

    https://doi.org/10.1038/npp.2010.104

    • PubMed
    • Google Scholar
39. 1. Irvine DRF

    (2018a) Auditory perceptual learning and changes in the conceptualization of auditory cortex

    Hearing Research 366:3–16.

    https://doi.org/10.1016/j.heares.2018.03.011

    • PubMed
    • Google Scholar
40. 1. Irvine DRF

    (2018b) Plasticity in the auditory system

    Hearing Research 362:61–73.

    https://doi.org/10.1016/j.heares.2017.10.011

    • PubMed
    • Google Scholar
41. 1. Ishishita Y
    2. Kunii N
    3. Shimada S
    4. Ibayashi K
    5. Tada M
    6. Kirihara K
    7. Kawai K
    8. Uka T
    9. Kasai K
    10. Saito N

    (2019) Deviance detection is the dominant component of auditory contextual processing in the lateral superior temporal gyrus: A human ECoG study

    Human Brain Mapping 40:1184–1194.

    https://doi.org/10.1002/hbm.24438

    • PubMed
    • Google Scholar
42. 1. Kamke MR
    2. Brown M
    3. Irvine DRF

    (2005) Basal forebrain cholinergic input is not essential for lesion-induced plasticity in mature auditory cortex

    Neuron 48:675–686.

    https://doi.org/10.1016/j.neuron.2005.09.014

    • PubMed
    • Google Scholar
43. 1. Keller GB
    2. Mrsic-Flogel TD

    (2018) Predictive processing: A canonical cortical computation

    Neuron 100:424–435.

    https://doi.org/10.1016/j.neuron.2018.10.003

    • PubMed
    • Google Scholar
44. 1. Kilgard MP
    2. Merzenich MM

    (1998) Cortical map reorganization enabled by nucleus basalis activity

    Science 279:1714–1718.

    https://doi.org/10.1126/science.279.5357.1714

    • PubMed
    • Google Scholar
45. 1. Kok P
    2. Jehee JFM
    3. de Lange FP

    (2012a) Less is more: expectation sharpens representations in the primary visual cortex

    Neuron 75:265–270.

    https://doi.org/10.1016/j.neuron.2012.04.034

    • PubMed
    • Google Scholar
46. 1. Kok P
    2. Rahnev D
    3. Jehee JFM
    4. Lau HC
    5. de Lange FP

    (2012b) Attention reverses the effect of prediction in silencing sensory signals

    Cerebral Cortex 22:2197–2206.

    https://doi.org/10.1093/cercor/bhr310

    • PubMed
    • Google Scholar
47. 1. Lao-Rodríguez AB
    2. Przewrocki K
    3. Pérez-González D
    4. Alishbayli A
    5. Yilmaz E
    6. Malmierca MS
    7. Englitz B

    (2023) Neuronal responses to omitted tones in the auditory brain: A neuronal correlate for predictive coding

    Science Advances 9:eabq8657.

    https://doi.org/10.1126/sciadv.abq8657

    • PubMed
    • Google Scholar
48. 1. Lucas-Meunier E
    2. Fossier P
    3. Baux G
    4. Amar M

    (2003) Cholinergic modulation of the cortical neuronal network

    Pflugers Archiv 446:17–29.

    https://doi.org/10.1007/s00424-002-0999-2

    • PubMed
    • Google Scholar
49. 1. Ma X
    2. Suga N

    (2005) Long-term cortical plasticity evoked by electric stimulation and acetylcholine applied to the auditory cortex

    PNAS 102:9335–9340.

    https://doi.org/10.1073/pnas.0503851102

    • PubMed
    • Google Scholar
50. 1. Magri C
    2. Whittingstall K
    3. Singh V
    4. Logothetis NK
    5. Panzeri S

    (2009) A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings

    BMC Neuroscience 10:81.

    https://doi.org/10.1186/1471-2202-10-81

    • PubMed
    • Google Scholar
51. 1. Malmierca MS
    2. Cristaudo S
    3. Pérez-González D
    4. Covey E

    (2009) Stimulus-specific adaptation in the inferior colliculus of the anesthetized rat

    The Journal of Neuroscience 29:5483–5493.

    https://doi.org/10.1523/JNEUROSCI.4153-08.2009

    • PubMed
    • Google Scholar
52. Book

    1. Malmierca MS

    (2015)

    Auditory system

    In: Paxinos G, editors. The Rat Nervous System. Elsevier - Academic Press. pp. 865–946.

    • Google Scholar
53. Book

    1. Malmierca MS
    2. Carbajal GV
    3. Escera C

    (2019) Deviance Detection and Encoding Acoustic Regularity in the Auditory Midbrain

    In: Kandler Karl, editors. The Oxford Handbook of the Auditory Brainstem. Oxford Academic. pp. 707–740.

    https://doi.org/10.1093/oxfordhb/9780190849061.001.0001

    • Google Scholar
54. 1. Marshall L
    2. Mathys C
    3. Ruge D
    4. de Berker AO
    5. Dayan P
    6. Stephan KE
    7. Bestmann S

    (2016) Pharmacological fingerprints of contextual uncertainty

    PLOS Biology 14:e1002575.

    https://doi.org/10.1371/journal.pbio.1002575

    • PubMed
    • Google Scholar
55. 1. Martins ARO
    2. Froemke RC

    (2015) Coordinated forms of noradrenergic plasticity in the locus coeruleus and primary auditory cortex

    Nature Neuroscience 18:1483–1492.

    https://doi.org/10.1038/nn.4090

    • PubMed
    • Google Scholar
56. 1. Mesulam MM

    (2013) Cholinergic circuitry of the human nucleus basalis and its fate in Alzheimer’s disease

    The Journal of Comparative Neurology 521:4124–4144.

    https://doi.org/10.1002/cne.23415

    • PubMed
    • Google Scholar
57. 1. Metherate R
    2. Cox CL
    3. Ashe JH

    (1992) Cellular bases of neocortical activation: modulation of neural oscillations by the nucleus basalis and endogenous acetylcholine

    The Journal of Neuroscience 12:4701–4711.

    https://doi.org/10.1523/JNEUROSCI.12-12-04701.1992

    • PubMed
    • Google Scholar
58. 1. Metherate R

    (2011) Functional connectivity and cholinergic modulation in auditory cortex

    Neuroscience and Biobehavioral Reviews 35:2058–2063.

    https://doi.org/10.1016/j.neubiorev.2010.11.010

    • PubMed
    • Google Scholar
59. 1. Minces V
    2. Pinto L
    3. Dan Y
    4. Chiba AA

    (2017) Cholinergic shaping of neural correlations

    PNAS 114:5725–5730.

    https://doi.org/10.1073/pnas.1621493114

    • PubMed
    • Google Scholar
60. 1. Moore DR

    (2006) Auditory processing disorder (APD)-potential contribution of mouse research

    Brain Research 1091:200–206.

    https://doi.org/10.1016/j.brainres.2006.01.108

    • PubMed
    • Google Scholar
61. 1. Moore DR

    (2012) Listening difficulties in children: bottom-up and top-down contributions

    Journal of Communication Disorders 45:411–418.

    https://doi.org/10.1016/j.jcomdis.2012.06.006

    • PubMed
    • Google Scholar
62. 1. Moran RJ
    2. Campo P
    3. Symmonds M
    4. Stephan KE
    5. Dolan RJ
    6. Friston KJ

    (2013) Free energy, precision and learning: the role of cholinergic neuromodulation

    The Journal of Neuroscience 33:8227–8236.

    https://doi.org/10.1523/JNEUROSCI.4255-12.2013

    • PubMed
    • Google Scholar
63. 1. Motts SD
    2. Schofield BR

    (2009) Sources of cholinergic input to the inferior colliculus

    Neuroscience 160:103–114.

    https://doi.org/10.1016/j.neuroscience.2009.02.036

    • PubMed
    • Google Scholar
64. 1. Muñoz W
    2. Rudy B

    (2014) Spatiotemporal specificity in cholinergic control of neocortical function

    Current Opinion in Neurobiology 26:149–160.

    https://doi.org/10.1016/j.conb.2014.02.015

    • PubMed
    • Google Scholar
65. 1. Murone C
    2. Paxinos G
    3. McKinley MJ
    4. Oldfield BJ
    5. Muller-Esterl W
    6. Mendelsohn FA
    7. Chai SY

    (1997) Distribution of bradykinin B2 receptors in sheep brain and spinal cord visualized by in vitro autoradiography

    The Journal of Comparative Neurology 381:203–218.

    https://doi.org/10.1002/(sici)1096-9861(19970505)381:2<203::aid-cne7>3.0.co;2-7

    • PubMed
    • Google Scholar
66. 1. Nelken I

    (2014) Stimulus-specific adaptation and deviance detection in the auditory system: experiments and models

    Biological Cybernetics 108:655–663.

    https://doi.org/10.1007/s00422-014-0585-7

    • PubMed
    • Google Scholar
67. 1. Netser S
    2. Zahar Y
    3. Gutfreund Y

    (2011) Stimulus-specific adaptation: can it be a neural correlate of behavioral habituation?

    The Journal of Neuroscience 31:17811–17820.

    https://doi.org/10.1523/JNEUROSCI.4790-11.2011

    • PubMed
    • Google Scholar
68. 1. Nieto-Diego J
    2. Malmierca MS

    (2016) Topographic distribution of stimulus-specific adaptation across auditory cortical fields in the anesthetized rat

    PLOS Biology 14:e1002397.

    https://doi.org/10.1371/journal.pbio.1002397

    • PubMed
    • Google Scholar
69. 1. Ogawa H
    2. Oka K

    (2015) Direction-specific adaptation in neuronal and behavioral responses of an insect mechanosensory system

    The Journal of Neuroscience 35:11644–11655.

    https://doi.org/10.1523/JNEUROSCI.1378-15.2015

    • PubMed
    • Google Scholar
70. 1. Parr T
    2. Friston KJ

    (2017) Uncertainty, epistemics and active inference

    Journal of the Royal Society, Interface 14:20170376.

    https://doi.org/10.1098/rsif.2017.0376

    • PubMed
    • Google Scholar
71. 1. Parras GG
    2. Nieto-Diego J
    3. Carbajal GV
    4. Valdés-Baizabal C
    5. Escera C
    6. Malmierca MS

    (2017) Neurons along the auditory pathway exhibit a hierarchical organization of prediction error

    Nature Communications 8:2148.

    https://doi.org/10.1038/s41467-017-02038-6

    • PubMed
    • Google Scholar
72. 1. Parras GG
    2. Valdés-Baizabal C
    3. Harms L
    4. Michie PT
    5. Malmierca MS

    (2020) The effect of NMDA-R antagonist, MK-801, on neuronal mismatch along the rat auditory thalamocortical pathway

    Scientific Reports 10:12391.

    https://doi.org/10.1038/s41598-020-68837-y

    • PubMed
    • Google Scholar
73. 1. Pérez-González D
    2. Parras GG
    3. Morado-Díaz CJ
    4. Aedo-Sánchez C
    5. Carbajal GV
    6. Malmierca MS

    (2021) Deviance detection in physiologically identified cell types in the rat auditory cortex

    Hearing Research 399:107997.

    https://doi.org/10.1016/j.heares.2020.107997

    • PubMed
    • Google Scholar
74. 1. Pérez-González D
    2. Schreiner TG
    3. Llano DA
    4. Malmierca MS

    (2022) Alzheimer’s disease, hearing loss, and deviance detection

    Frontiers in Neuroscience 16:879480.

    https://doi.org/10.3389/fnins.2022.879480

    • PubMed
    • Google Scholar
75. 1. Picciotto MR
    2. Higley MJ
    3. Mineur YS

    (2012) Acetylcholine as a neuromodulator: cholinergic signaling shapes nervous system function and behavior

    Neuron 76:116–129.

    https://doi.org/10.1016/j.neuron.2012.08.036

    • PubMed
    • Google Scholar
76. 1. Pinto L
    2. Goard MJ
    3. Estandian D
    4. Xu M
    5. Kwan AC
    6. Lee SH
    7. Harrison TC
    8. Feng G
    9. Dan Y

    (2013) Fast modulation of visual perception by basal forebrain cholinergic neurons

    Nature Neuroscience 16:1857–1863.

    https://doi.org/10.1038/nn.3552

    • PubMed
    • Google Scholar
77. 1. Polack PO
    2. Friedman J
    3. Golshani P

    (2013) Cellular mechanisms of brain state-dependent gain modulation in visual cortex

    Nature Neuroscience 16:1331–1339.

    https://doi.org/10.1038/nn.3464

    • PubMed
    • Google Scholar
78. 1. Polley DB
    2. Read HL
    3. Storace DA
    4. Merzenich MM

    (2007) Multiparametric auditory receptive field organization across five cortical fields in the albino rat

    Journal of Neurophysiology 97:3621–3638.

    https://doi.org/10.1152/jn.01298.2006

    • PubMed
    • Google Scholar
79. 1. Puckett AC
    2. Pandya PK
    3. Moucha R
    4. Dai W
    5. Kilgard MP

    (2007) Plasticity in the rat posterior auditory field following nucleus basalis stimulation

    Journal of Neurophysiology 98:253–265.

    https://doi.org/10.1152/jn.01309.2006

    • PubMed
    • Google Scholar
80. 1. Qi G
    2. Feldmeyer D

    (2022) Cell-type specific neuromodulation of excitatory and inhibitory neurons via muscarinic acetylcholine receptors in layer 4 of rat barrel cortex

    Frontiers in Neural Circuits 16:843025.

    https://doi.org/10.3389/fncir.2022.843025

    • PubMed
    • Google Scholar
81. 1. Quintela-Vega L
    2. Morado-Díaz CJ
    3. Terreros G
    4. Sánchez JS
    5. Pérez-González D
    6. Malmierca MS

    (2023) Novelty detection in an auditory oddball task on freely moving rats

    Communications Biology 6:1063.

    https://doi.org/10.1038/s42003-023-05403-y

    • PubMed
    • Google Scholar
82. 1. Ruhnau P
    2. Herrmann B
    3. Schröger E

    (2012) Finding the right control: the mismatch negativity under investigation

    Clinical Neurophysiology 123:507–512.

    https://doi.org/10.1016/j.clinph.2011.07.035

    • PubMed
    • Google Scholar
83. 1. Sarter M
    2. Givens B
    3. Bruno JP

    (2001) The cognitive neuroscience of sustained attention: where top-down meets bottom-up

    Brain Research. Brain Research Reviews 35:146–160.

    https://doi.org/10.1016/s0165-0173(01)00044-3

    • PubMed
    • Google Scholar
84. 1. Schöbi D
    2. Homberg F
    3. Frässle S
    4. Endepols H
    5. Moran RJ
    6. Friston KJ
    7. Tittgemeyer M
    8. Heinzle J
    9. Stephan KE

    (2021) Model-based prediction of muscarinic receptor function from auditory mismatch negativity responses

    NeuroImage 237:118096.

    https://doi.org/10.1016/j.neuroimage.2021.118096

    • PubMed
    • Google Scholar
85. 1. Schröger E
    2. Wolff C

    (1996) Mismatch response of the human brain to changes in sound location

    Neuroreport 7:3005–3008.

    https://doi.org/10.1097/00001756-199611250-00041

    • PubMed
    • Google Scholar
86. 1. Sharma K

    (2019) Cholinesterase inhibitors as Alzheimer’s therapeutics (Review)

    Molecular Medicine Reports 20:1479–1487.

    https://doi.org/10.3892/mmr.2019.10374

    • PubMed
    • Google Scholar
87. 1. Shipp S

    (2016) Neural elements for predictive coding

    Frontiers in Psychology 7:1792.

    https://doi.org/10.3389/fpsyg.2016.01792

    • PubMed
    • Google Scholar
88. 1. Spratling MW

    (2008) Reconciling predictive coding and biased competition models of cortical function

    Frontiers in Computational Neuroscience 2:4.

    https://doi.org/10.3389/neuro.10.004.2008

    • PubMed
    • Google Scholar
89. 1. Thiele A
    2. Bellgrove MA

    (2018) Neuromodulation of attention

    Neuron 97:769–785.

    https://doi.org/10.1016/j.neuron.2018.01.008

    • PubMed
    • Google Scholar
90. 1. Timme NM
    2. Lapish C

    (2018) A tutorial for information theory in neuroscience

    eNeuro 5:ENEURO.0052-18.2018.

    https://doi.org/10.1523/ENEURO.0052-18.2018

    • PubMed
    • Google Scholar
91. 1. Ulanovsky N
    2. Las L
    3. Nelken I

    (2003) Processing of low-probability sounds by cortical neurons

    Nature Neuroscience 6:391–398.

    https://doi.org/10.1038/nn1032

    • PubMed
    • Google Scholar
92. 1. Ulanovsky N
    2. Las L
    3. Farkas D
    4. Nelken I

    (2004) Multiple time scales of adaptation in auditory cortex neurons

    The Journal of Neuroscience 24:10440–10453.

    https://doi.org/10.1523/JNEUROSCI.1905-04.2004

    • PubMed
    • Google Scholar
93. Preprint

    1. Valdés-Baizabal C
    2. Pérez-González D
    3. Malmierca MS

    (2019) Dopamine Gates Prediction Error Forwarding in the Cortices of the Inferior Colliculus

    bioRxiv.

    https://doi.org/10.1101/824656

    • Google Scholar
94. 1. von der Behrens W
    2. Bäuerle P
    3. Kössl M
    4. Gaese BH

    (2009) Correlating stimulus-specific adaptation of cortical neurons and local field potentials in the awake rat

    The Journal of Neuroscience 29:13837–13849.

    https://doi.org/10.1523/JNEUROSCI.3475-09.2009

    • PubMed
    • Google Scholar
95. 1. Vossel S
    2. Weidner R
    3. Driver J
    4. Friston KJ
    5. Fink GR

    (2012) Deconstructing the architecture of dorsal and ventral attention systems with dynamic causal modeling

    The Journal of Neuroscience 32:10637–10648.

    https://doi.org/10.1523/JNEUROSCI.0414-12.2012

    • PubMed
    • Google Scholar
96. 1. Weinberger NM

    (2004) Specific long-term memory traces in primary auditory cortex

    Nature Reviews. Neuroscience 5:279–290.

    https://doi.org/10.1038/nrn1366

    • PubMed
    • Google Scholar
97. 1. Yarden TS
    2. Mizrahi A
    3. Nelken I

    (2022) Context-dependent inhibitory control of stimulus-specific adaptation

    The Journal of Neuroscience 42:4629–4651.

    https://doi.org/10.1523/JNEUROSCI.0988-21.2022

    • PubMed
    • Google Scholar
98. 1. Yon D
    2. Frith CD

    (2021) Precision and the Bayesian brain

    Current Biology 31:R1026–R1032.

    https://doi.org/10.1016/j.cub.2021.07.044

    • PubMed
    • Google Scholar
99. 1. Yu AJ
    2. Dayan P

    (2002) Acetylcholine in cortical inference

    Neural Networks 15:719–730.

    https://doi.org/10.1016/s0893-6080(02)00058-8

    • PubMed
    • Google Scholar
100. 1. Yu AJ
     2. Dayan P

     (2005) Uncertainty, neuromodulation, and attention

     Neuron 46:681–692.

     https://doi.org/10.1016/j.neuron.2005.04.026

     • PubMed
     • Google Scholar
101. 1. Zaborszky L
     2. Hoemke L
     3. Mohlberg H
     4. Schleicher A
     5. Amunts K
     6. Zilles K

     (2008) Stereotaxic probabilistic maps of the magnocellular cell groups in human basal forebrain

     NeuroImage 42:1127–1141.

     https://doi.org/10.1016/j.neuroimage.2008.05.055

     • PubMed
     • Google Scholar

Author details

1. David Pérez-González

   1. Cognitive and Auditory Neuroscience Laboratory, Institute of Neuroscience of Castilla y León, Calle Pintor Fernando Gallego, Salamanca, Spain
   2. Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
   3. Department of Basic Psychology, Psychobiology and Behavioural Science Methodology, Faculty of Psychology, Campus Ciudad Jardín, University of Salamanca, Salamanca, Spain

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Ana Belén Lao-Rodríguez

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6288-2692

2. Ana Belén Lao-Rodríguez

   1. Cognitive and Auditory Neuroscience Laboratory, Institute of Neuroscience of Castilla y León, Calle Pintor Fernando Gallego, Salamanca, Spain
   2. Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – review and editing

   Contributed equally with

   David Pérez-González

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4093-0160

3. Cristian Aedo-Sánchez

   1. Cognitive and Auditory Neuroscience Laboratory, Institute of Neuroscience of Castilla y León, Calle Pintor Fernando Gallego, Salamanca, Spain
   2. Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain

   Contribution

   Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0670-7843

4. Manuel S Malmierca

   1. Cognitive and Auditory Neuroscience Laboratory, Institute of Neuroscience of Castilla y León, Calle Pintor Fernando Gallego, Salamanca, Spain
   2. Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
   3. Department of Biology and Pathology, Faculty of Medicine, Campus Miguel de Unamuno, University of Salamanca, Salamanca, Spain

   Contribution

   Conceptualization, Supervision, Funding acquisition, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   msm@usal.es

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0168-7572

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