Stimulus-specific prediction error neurons in mouse auditory cortex

Abstract

Comparing expectation with experience is an important neural computation performed throughout the brain and is a hallmark of predictive processing. Experiments that alter the sensory outcome of an animal's behavior reveal enhanced neural responses to unexpected self-generated stimuli, indicating that populations of neurons in sensory cortex may reflect prediction errors - mismatches between expectation and experience. However, enhanced neural responses to self-generated stimuli could also arise through non-predictive mechanisms, such as the movement-based facilitation of a neuron's inherent sound responses. If sensory prediction error neurons exist in sensory cortex, it is unknown whether they manifest as general error responses, or respond with specificity to errors in distinct stimulus dimensions. To answer these questions, we trained mice to expect the outcome of a simple sound-generating behavior and recorded auditory cortex activity as mice heard either the expected sound or sounds that deviated from expectation in one of multiple distinct dimensions. Our data reveal that the auditory cortex learns to suppress responses to self-generated sounds along multiple acoustic dimensions simultaneously. We identify a distinct population of auditory cortex neurons that are not responsive to passive sounds or to the expected sound but that explicitly encode prediction errors. These prediction error neurons are abundant only in animals with a learned motor-sensory expectation, and encode one or two specific violations rather than a generic error signal.

Figure 1:. Specific suppression of expected sounds across multiple acoustic dimensions.

(A) Schematic of head-fixed lever press training paradigm (Top) and stimulus and reward timing for lever movements (Bottom). Grey area indicates home position. (B) Schematic of multi-array recording sessions in trained mice (Left) and aggregate neural responses to expected and multiple unexpected sounds in the passive (Darker) and movement-evoked (Lighter) context. Of the 1016 regular-spiking neurons we recorded (N = 5 Animals), a subset of neurons are analyzed for each sound type if they respond to that sound in either context (p < 0.01, 0 – 60ms post sound onset). Values are listed below each PSTH. Color differences represent sound frequency, and the likelihood of each lever press producing a given sound type during the recording session is displayed in black bar.

Figure 2:. Precise suppression of expected sound responses in individual neurons.

(A) Average responses across trials of three individual neurons to each tone type, showing suppression that is specific for the expected sound at the individual neuron level. (B) Modulation (See Methods) of individual neurons comparing responses to sounds heard in the active and passive condition to each tone type. Negative values indicate weaker responses in the active condition, i.e. suppression. A one-way ANOVA detected differences amongst the groups (F-statistic p = 2*10⁻³²), with Exp and Freq being significantly different from all other groups (Exp, p < 1*10⁻⁵; Freq, p < 0.01). Neuron values and inclusion are the same as (Fig 1B). (C) Confusion matrix showing how delivered stimuli were classified from auditory cortex neural responses on individual trials. For each animal (N = 4), we measured the response of each neuron to a given sound on 20 individual trials for each sound type, omission trials, and a set of randomly selected time points during behavior that were < 0.3s away from other sounds (‘Null’). Values represent the fraction of each ground-truth trial type that were classified as a given stimulus based on neural data, averaged across 4 animals.

Figure 3:. Abundant prediction error neurons in mouse auditory cortex.

(A) Number of neurons responsive (p < 0.01) to a given sound in the active context (light), passive context (dark), or both (white). (B) Schematic depicting the identification of putative prediction error neurons, defined as neurons which respond to a given stimulus type in the active context, but not in the passive context, not at the time of expected sound on omission trials, and not to the expected self-generated sound. Stimulus window of 0 – 60ms post sound onset compared to the 60ms prior to sound onset. (C) Number of neurons that fulfil our putative prediction error criteria for each unexpected trial type.

Figure 4:. Prediction error neurons reflect the violation of a learned expectation.

(A) Quantification of the number of putative prediction error neurons in trained animals, and animals trained on an identical but silent lever task. Each dot represents the fraction of total number of neurons in a recording session that met the criteria for prediction error neurons for a given stimulus. (B) Comparison between the number of prediction error neurons for a stimulus (as in A) and how ‘different’ a stimulus was from the expected sound. Differences were quantified between neural responses to each probe sound and the expected sound in the passive condition (See Methods). Each dot represents one unexpected stimulus in one animal (N = 4), and difference values were mean-normalized within animal to enable a comparison across animals. Linear regression is shown with shaded standard error. P values and correlation coefficients are listed. (C) Identical analysis as (B) but using the absolute magnitude of an animal’s population response to each stimulus heard in the passive condition.

Figure 5:. Prediction error neurons are stimulus-specific.

(A) Visual representation of each prediction error neuron’s responsiveness (white) to task tones heard in the active condition (left), responsiveness in passive condition (middle), and whether a neuron obeyed our prediction error criteria for a given stimulus (right, see Fig 3B). To match our prediction error criteria, a probability value of 0.1 was used as a cutoff for the expected sound (first column in each map), while all others reflect a cutoff of 0.01. Rows with color represent example neurons in (B). (B) Responses of two example neurons to sounds heard actively (top) and passively (bottom). Black PSTHs show significant responses using the p values described in (A). (C) Quantification of the number of different stimuli for which a neuron signals prediction error. (D) Color-coded matrix showing the number of prediction error neurons that are shared across pairs of stimuli.

Figure 6:. Prediction error responses in auditory cortex are short-latency.

(A) Raster of example neuron showing action potential timing following frequency probe sounds, with the first spike on a given trial (orange) used to calculate an average onset latency. (B) Histogram of average onset latency following frequency probe trials for prediction error neurons (Green), all neurons responsive to the frequency probe (Orange), and latency of neurons responsive to the passive frequency probe following passive presentation. No difference between prediction error neuron latencies and general latencies in the active condition (p = 0.97) or to passive sound responses (p = 0.97, p = 0.87, KS Test).