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A central question in sensory neuroscience is how neurons represent complex natural stimuli. This process involves multiple steps of feature extraction to obtain a condensed, categorical representation useful for classification and behavior. It has previously been shown that central auditory neurons in the starling have composite receptive fields composed of multiple features when probed with conspecific songs. Whether this property is an idiosyncratic characteristic of songbirds, a group of highly specialized vocal learners, or a generic characteristic of central auditory systems in different animals is, however, unknown. To address this question, we have recorded responses from auditory cortical neurons in mice, and characterized their receptive fields using mouse ultrasonic vocalizations (USVs) as a natural and ethologically relevant stimulus and pitch-shifted starling songs as a natural but ethologically irrelevant control stimulus. We have found that auditory cortical neurons in the mouse display composite receptive fields with multiple excitatory and inhibitory subunits. Moreover, this was the case with either the conspecific or the heterospecific vocalizations. We then trained the sparse filtering algorithm on both classes of natural stimuli to obtain statistically optimal features, and compared the natural and artificial features using UMAP, a dimensionality-reduction algorithm previously used to analyze mouse USVs and birdsongs. We have found that the receptive-field features obtained with the mouse USVs and those obtained with the pitch-shifted starling songs clustered together, as did the sparse-filtering features. However, the natural and artificial receptive-field features clustered mostly separately. These results indicate that composite receptive fields are likely a generic property of central auditory systems in different classes of animals. They further suggest that the quadratic receptive-field features of the mouse auditory cortical neurons are natural-stimulus invariant.

To survive, animals have to quickly modify their behaviour when the reward changes. The internal representations responsible for this are updated through synaptic weight changes, mediated by certain neuromodulators conveying feedback from the environment. In previous experiments, we discovered a form of hippocampal Spike-Timing-Dependent-Plasticity (STDP) that is sequentially modulated by acetylcholine and dopamine. Acetylcholine facilitates synaptic depression, while dopamine retroactively converts the depression into potentiation. When these experimental findings were implemented as a learning rule in a computational model, our simulations showed that cholinergic-facilitated depression is important for reversal learning. In the present study, we tested the model’s prediction by optogenetically inactivating cholinergic neurons in mice during a hippocampus-dependent spatial learning task with changing rewards. We found that reversal learning, but not initial place learning, was impaired, verifying our computational prediction that acetylcholine-modulated plasticity promotes the unlearning of old reward locations. Further, differences in neuromodulator concentrations in the model captured mouse-by-mouse performance variability in the optogenetic experiments. Our line of work sheds light on how neuromodulators enable the learning of new contingencies., Author summary Reversal learning likely involves changes in synaptic connections, a neural mechanism known as synaptic plasticity, so old information can be updated. We previously discovered that acetylcholine, an important neuromodulator in the brain, changes synaptic connections in a way that favours reversal learning. Specifically, acetylcholine weakens active synapses in brain slices, but these synapses can later be strengthened by a reward signal. Based on this result in slices, we used a computational model to propose a behavioural function for the action of acetylcholine on synaptic connections. In the model, acetylcholine would weaken synaptic connections associated with an old reward, allowing an agent to quickly learn a new reward location. We tested this hypothesis here by silencing acetylcholine neurons in mice while they navigated a maze for food rewards. These animals were able to learn the location of the first food reward, but were impaired when the reward was shifted to a new location. The behavioural results of this study suggest that acetylcholine indeed facilitates reversal learning, which the computational model attributes to a weakening of synaptic connections that do not lead to reward. Taken together, our experimental and computational work show how synaptic strength changes, gated by neuromodulators, affect learning behaviour.