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Task reward structure shapes rapid receptive field plasticity in auditory cortex
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As sensory stimuli and behavioral demands change, the attentive brain quickly identifies task-relevant stimuli and associates them with appropriate motor responses. The effects of attention on sensory processing vary across task paradigms, suggesting that the brain may use multiple strategies and mechanisms to highlight attended stimuli and link them to motor action. To better understand factors that contribute to these variable effects, we studied sensory representations in primary auditory cortex (A1) during two instrumental tasks that shared the same auditory discrimination but required different behavioral responses, either approach or avoidance. In the approach task, ferrets were rewarded for licking a spout when they heard a target tone amid a sequence of reference noise sounds. In the avoidance task, they were punished unless they inhibited licking to the target. To explore how these changes in task reward structure influenced attention-driven rapid plasticity in A1, we measured changes in sensory neural responses during behavior. Responses to the target changed selectively during both tasks but did so with opposite sign. Despite the differences in sign, both effects were consistent with a general neural coding strategy that maximizes discriminability between sound classes. The dependence of the direction of plasticity on task suggests that representations in A1 change not only to sharpen representations of task-relevant stimuli but also to amplify responses to stimuli that signal aversive outcomes and lead to behavioral inhibition. Thus, top-down control of sensory processing can be shaped by task reward structure in addition to the required sensory discrimination.
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... The auditory cortex (AC) is a key candidate brain region for processing incoming sounds during locomotion due to its well-established role in context-, behavior-, and decision-makingdependent sound processing [22][23][24][25][26][27][28][29][30][31][32][33][34]. Intriguingly, previous studies have found that locomotion has a generally suppressive effect on sound-evoked responses in the AC [35][36][37][38][39]. Attenuation of responses to self-generated sounds produced during locomotion is well explained by corollary discharge, which acts to suppress responses to predictable sounds and enhance sensitivity to unpredictable sounds [38,[40][41][42][43] (though see [44][45][46]). ...
... However, the functional benefit of the observed attenuation of AC responses to unpredictable external sounds during locomotion has remained elusive. This finding is particularly enigmatic given the critical need to be able to efficiently process external sounds and their associated meaning during locomotion for survival and the well-established role that AC plays in behavior-and context-dependent sound processing [22][23][24][25][26][27][28][29][30][31][32][33][34]. A proposed explanation for this finding is that during locomotion, neural computational resources shift from auditory to visual processing [35,36]. ...
The ability to process and act upon incoming sounds during locomotion is critical for survival and adaptive behavior. Despite the established role that the auditory cortex (AC) plays in behavior- and context-dependent sound processing, previous studies have found that auditory cortical activity is on average suppressed during locomotion as compared to immobility. While suppression of auditory cortical responses to self-generated sounds results from corollary discharge, which weakens responses to predictable sounds, the functional role of weaker responses to unpredictable external sounds during locomotion remains unclear. In particular, whether suppression of external sound-evoked responses during locomotion reflects reduced involvement of the AC in sound processing or whether it results from masking by an alternative neural computation in this state remains unresolved. Here, we tested the hypothesis that rather than simple inhibition, reduced sound-evoked responses during locomotion reflect a tradeoff with the emergence of explicit and reliable coding of locomotion velocity. To test this hypothesis, we first used neural inactivation in behaving mice and found that the AC plays a critical role in sound-guided behavior during locomotion. To investigate the nature of this processing, we used two-photon calcium imaging of local excitatory auditory cortical neural populations in awake mice. We found that locomotion had diverse influences on activity of different neurons, with a net suppression of baseline-subtracted sound-evoked responses and neural stimulus detection, consistent with previous studies. Importantly, we found that the net inhibitory effect of locomotion on baseline-subtracted sound-evoked responses was strongly shaped by elevated ongoing activity that compressed the response dynamic range, and that rather than reflecting enhanced "noise," this ongoing activity reliably encoded the animal's locomotion speed. Decoding analyses revealed that locomotion speed and sound are robustly co-encoded by auditory cortical ensemble activity. Finally, we found consistent patterns of joint coding of sound and locomotion speed in electrophysiologically recorded activity in freely moving rats. Together, our data suggest that rather than being suppressed by locomotion, auditory cortical ensembles explicitly encode it alongside sound information to support sound perception during locomotion.
... One explanation is that mutual information may be reduced if ACx activity is broadly suppressed at the population level during task engagement, as suggested by converging evidence [42][43][44][45] . A subset of target-tracking neurons (6 of 151; see Supplements FR+/VS+) are highly sensitive to the target, such that their masked response at all SNRs is comparable to their response to a robust 60 dB SPL target in quiet-resembling 'grandmother' neurons 46 . ...
Everyday environments often contain multiple concurrent sound sources that fluctuate over time. Normally hearing listeners can benefit from high signal-to-noise ratios (SNRs) in energetic dips of temporally fluctuating background sound, a phenomenon called dip-listening. Specialized mechanisms of dip-listening exist across the entire auditory pathway. Both the instantaneous fluctuating and the long-term overall SNR shape dip-listening. An unresolved issue regarding cortical mechanisms of dip-listening is how target perception remains invariant to overall SNR, specifically, across different tone levels with an ongoing fluctuating masker. Equivalent target detection over both positive and negative overall SNRs (SNR invariance) is reliably achieved in highly-trained listeners. Dip-listening is correlated with the ability to resolve temporal fine structure, which involves temporally-varying spike patterns. Thus the current work tests the hypothesis that at negative SNRs, neuronal readout mechanisms need to increasingly rely on decoding strategies based on temporal spike patterns, as opposed to spike count. Recordings from chronically implanted electrode arrays in core auditory cortex of trained and awake Mongolian gerbils that are engaged in a tone detection task in 10 Hz amplitude-modulated background sound reveal that rate-based decoding is not SNR-invariant, whereas temporal coding is informative at both negative and positive SNRs.
... Decades of work have emphasized the role of the sensory neocortex is to extract physical stimulus features whereas higher cortical areas use the sensory signals to generate decision variables, which suggest a step-by-step hierarchical processing of information in different brain regions [12][13][14][15][16][17] . Recent findings suggest that superficial layers of the sensory cortex encodes learning-related nonsensory signals, such as anticipation, attention and behavioral choice 3,4,11,[18][19][20][21] . ...
During learning, multi-dimensional inputs are integrated within the sensory cortices. However, the strategies by which the sensory cortex employs to achieve learning remains poorly understood. We studied the sensory cortical neuronal coding of trace eyeblink conditioning (TEC) in head-fixed, freely running mice, where whisker deflection was used as a conditioned stimulus (CS) and an air puff to the cornea delivered after an interval was used as unconditioned stimulus (US). After training, mice learned the task with a set of stereotypical behavioral changes, most prominent ones include prolonged closure of eyelids, and increased reverse running between CS and US onset. The local blockade of the primary somatosensory cortex (S1) activities with muscimol abolished the behavior learning suggesting that S1 is required for the TEC. In naive animals, based on the response properties to the CS and US, identities of the small proportion (~20%) of responsive primary neurons (PNs) were divided into two subtypes: CR (i.e. CS-responsive) and UR neurons (i.e. US-responsive). After animals learned the task, identity of CR and UR neurons changed: while the CR neurons are less responsive to CS, UR neurons gain responsiveness to CS, a new phenomenon we defined as 'learning induced neuronal identity switch (LINIS)'. To explore the potential mechanisms underlying LINIS, we found that systemic and local (i.e. in S1) administration of the nicotinic receptor antagonist during TEC training blocked the LINIS, and concomitantly disrupted the behavior learning. Additionally, we monitored responses of two types of cortical interneurons (INs) and observed that the responses of the somatostatin-expressing (SST), but not parvalbumin-expressing (PV) INs are negatively correlated with the learning performance, suggesting that SST-INs contribute to the LINIS. Thus, we conclude that L2/3 PNs in S1 encode perceptual learning by LINIS like mechanisms, and cholinergic pathways and cortical SST interneurons are involved in the formation of LINIS.
... Behavior alternated between blocks of active task engagement and passive listening to task stimuli. During passive listening, licking responses were not rewarded and animals quickly disengaged from the task (David et al., 2012 ). ...
Categorical sensory representations are critical for many behaviors, including speech perception. In the auditory system, categorical information is thought to arise hierarchically, becoming increasingly prominent in higher order cortical regions. The neural mechanisms that support this robust and flexible computation remain poorly understood. Here, we studied sound representations in primary and non-primary auditory cortex while animals engaged in a challenging sound discrimination task. Population-level decoding of simultaneously recorded single neurons revealed that task engagement caused categorical sound representations to emerge in non-primary auditory cortex. In primary auditory cortex, task engagement caused a general enhancement of sound decoding that was not specific to task-relevant categories. These findings are consistent with mixed selectivity models of neural disentanglement, in which early sensory regions build an overcomplete representation of the world and allow neurons in downstream brain regions to flexibly and selectively read out behaviorally relevant, categorical information.
... Neurons throughout the auditory system adapt to the statistics of the acoustic environment, including the frequency of stimuli over time 56,57 , more complex sound patterns 24,58 , and task-related or rewarded stimuli [59][60][61][62][63][64] . In this study, we focused on contrast gain control as a fundamental statistical adaptation that relates to efficient coding 14,[17][18][19] . ...
Neurons throughout the sensory pathway adapt their responses depending on the statistical structure of the sensory environment. Contrast gain control is a form of adaptation in the auditory cortex, but it is unclear whether the dynamics of gain control reflect efficient adaptation, and whether they shape behavioral perception. Here, we trained mice to detect a target presented in background noise shortly after a change in the contrast of the background. The observed changes in cortical gain and behavioral detection followed the dynamics of a normative model of efficient contrast gain control; specifically, target detection and sensitivity improved slowly in low contrast, but degraded rapidly in high contrast. Auditory cortex was required for this task, and cortical responses were not only similarly affected by contrast but predicted variability in behavioral performance. Combined, our results demonstrate that dynamic gain adaptation supports efficient coding in auditory cortex and predicts the perception of sounds in noise.
... passive listening to task stimuli. During passive listening, licking responses were not rewarded and 112 animals quickly disengaged from the task(David et al., 2012). ...
Categorical sensory representations are critical for many behaviors, including speech perception. In the auditory system, categorical information is thought to arise hierarchically, becoming increasingly prominent in higher order cortical regions. The neural mechanisms that support this robust and flexible computation remain poorly understood. Here, we studied sound representations in primary and non-primary auditory cortex while animals engaged in a challenging sound discrimination task. Population-level decoding of simultaneously recorded single neurons revealed that task engagement caused categorical sound representations to emerge in non-primary auditory cortex. In primary auditory cortex, task engagement caused a general enhancement of sound decoding that was not specific to task-relevant categories. These findings are consistent with mixed selectivity models of neural disentanglement, in which early sensory regions build an overcomplete representation of the world and allow neurons in downstream brain regions to flexibly and selectively read out behaviorally relevant, categorical information.
Our environment is made of a myriad of stimuli present in combinations often patterned in predictable ways. For example, there is a strong association between where we are and the sounds we hear. Like many environmental patterns, sound-context associations are learned implicitly, in an unsupervised manner, and are highly informative and predictive of normality. Yet, we know little about where and how unsupervised sound-context associations are coded in the brain. Here we measured plasticity in the auditory midbrain of mice living over days in an enriched task-less environment in which entering a context triggered sound with different degrees of predictability. Plasticity in the auditory midbrain, a hub of auditory input and multimodal feedback, developed over days and reflected learning of contextual information in a manner that depended on the predictability of the sound-context association and not on reinforcement. Plasticity manifested as an increase in response gain and tuning shift that correlated with a general increase in neuronal frequency discrimination. Thus, the auditory midbrain is sensitive to unsupervised predictable sound-context associations, revealing a subcortical engagement in the detection of contextual sounds. By increasing frequency resolution, this detection might facilitate the processing of behaviorally relevant foreground information described to occur in cortical auditory structures.
Sound perception is highly malleable, rapidly adjusting to the acoustic environment and behavioral demands. This flexibility is the result of ongoing changes in auditory cortical activity driven by fluctuations in attention, arousal, or prior expectations. Recent work suggests that the orbitofrontal cortex (OFC) may mediate some of these rapid changes, but the anatomical connections between the OFC and the auditory system are not well characterized. Here, we used virally mediated fluorescent tracers to map the projection from OFC to the auditory midbrain, thalamus, and cortex in a classic animal model for auditory research, the Mongolian gerbil (Meriones unguiculatus). We observed no connectivity between the OFC and the auditory midbrain, and an extremely sparse connection between the dorsolateral OFC and higher order auditory thalamic regions. In contrast, we observed a robust connection between the ventral and medial subdivisions of the OFC and the auditory cortex, with a clear bias for secondary auditory cortical regions. OFC axon terminals were found in all auditory cortical lamina but were significantly more concentrated in the infragranular layers. Tissue-clearing and lightsheet microscopy further revealed that auditory cortical-projecting OFC neurons send extensive axon collaterals throughout the brain, targeting both sensory and non-sensory regions involved in learning, decision-making, and memory. These findings provide a more detailed map of orbitofrontal-auditory connections and shed light on the possible role of the OFC in supporting auditory cognition.
We propose a neural model for object-oriented attention in which various visual stimuli (shapes, colors, letters, etc.) are represented by competing, mutually inhibitory, cell assemblies. The model's response to a sequence of cue and target stimuli mimics the neural responses in infero temporal (IT) visual cortex of monkeys performing a visual search task: enhanced response during the display of the stimulus, which decays but remains above a spontaneous rate after the cue disappears. When, subsequently, a display consisting of the target and several distractors is presented, the activity of all stimulus-driven cells is initially enhanced. After a short period of time, however, the activity of the cell assembly representing the cue stimulus is enhanced while the activity of the distractors decays because of mutual competition and a small top-down expectational input. The model fits the measured delayed activity in IT-cortex, recently reported by Chelazzi, Miller, Duncan, and Desimone (1993a), and we suggest that such a process, which is largely independent of the number of distractors, may be used by the visual system for selecting an expected target (appearing at an uncertain location) among distractors.
We used magnetoencephalography (MEG) to assess plasticity of human auditory cortex induced by classical conditioning and contingency reversal. Participants listened to random sequences of high or low tones. A first baseline phase presented these without further associations. In phase 2, one of the frequencies (CS(+)) was paired with shock on half its occurrences, whereas the other frequency (CS(-)) was not. In phase 3, the contingency assigning CS(+) and CS(-) was reversed. Conditioned pupil dilation was observed in phase 2 but extinguished in phase 3. MEG revealed that, during phase-2 initial conditioning, the P1m, N1m, and P2m auditory components, measured from sensors over auditory temporal cortex, came to distinguish between CS(+) and CS(-). After contingency reversal in phase 3, the later P2m component rapidly reversed its selectivity (unlike the pupil response) but the earlier P1m did not, whereas N1m showed some new learning but not reversal. These results confirm plasticity of human auditory responses due to classical conditioning, but go further in revealing distinct constraints on different levels of the auditory hierarchy. The later P2m component can reverse affiliation immediately in accord with an updated expectancy after contingency reversal, whereas the earlier auditory components cannot. These findings indicate distinct cognitive and emotional influences on auditory processing.
Activity in the primary auditory cortex (A1) is essential for normal sound localization behavior, but previous studies of the spatial sensitivity of neurons in A1 have found broad spatial tuning. We tested the hypothesis that spatial tuning sharpens when an animal engages in an auditory task. Cats performed a task that required evaluation of the locations of sounds and one that required active listening, but in which sound location was irrelevant. Some 26-44% of the units recorded in A1 showed substantially sharpened spatial tuning during the behavioral tasks as compared with idle conditions, with the greatest sharpening occurring during the location-relevant task. Spatial sharpening occurred on a scale of tens of seconds and could be replicated multiple times in ∼1.5-h test sessions. Sharpening resulted primarily from increased suppression of responses to sounds at least-preferred locations. That and an observed increase in latencies suggest an important role of inhibitory mechanisms.
Top-down signals from frontal cortex are thought to be important in cognitive control of sensory processing. To explore this interaction, we compared activity in ferret frontal cortex and primary auditory cortex (A1) during auditory and visual tasks requiring discrimination between classes of reference and target stimuli. Frontal cortex responses were behaviorally gated, selectively encoded the timing and invariant behavioral meaning of target stimuli, could be rapid in onset, and sometimes persisted for hours following behavior. These results are consistent with earlier findings in A1 that attention triggered rapid, selective, persistent, task-related changes in spectrotemporal receptive fields. Simultaneously recorded local field potentials revealed behaviorally gated changes in inter-areal coherence that were selectively modulated between frontal cortex and focal regions of A1 that were responsive to target sounds. These results suggest that A1 and frontal cortex dynamically establish a functional connection during auditory behavior that shapes the flow of sensory information and maintains a persistent trace of recent task-relevant stimulus features.
Learned changes in behavior can be elicited by either appetitive or aversive reinforcers. It is, however, not clear whether the two types of motivation, (approaching appetitive stimuli and avoiding aversive stimuli) drive learning in the same or different ways, nor is their interaction understood in situations where the two types are combined in a single experiment. To investigate this question we have developed a novel learning paradigm for Mongolian gerbils, which not only allows rewards and punishments to be presented in isolation or in combination with each other, but also can use these opposite reinforcers to drive the same learned behavior. Specifically, we studied learning of tone-conditioned hurdle crossing in a shuttle box driven by either an appetitive reinforcer (brain stimulation reward) or an aversive reinforcer (electrical footshock), or by a combination of both. Combination of the two reinforcers potentiated speed of acquisition, led to maximum possible performance, and delayed extinction as compared to either reinforcer alone. Additional experiments, using partial reinforcement protocols and experiments in which one of the reinforcers was omitted after the animals had been previously trained with the combination of both reinforcers, indicated that appetitive and aversive reinforcers operated together but acted in different ways: in this particular experimental context, punishment appeared to be more effective for initial acquisition and reward more effective to maintain a high level of conditioned responses (CRs). The results imply that learning mechanisms in problem solving were maximally effective when the initial punishment of mistakes was combined with the subsequent rewarding of correct performance.
Visual attention can improve behavioral performance by allowing observers to focus on the important information in a complex scene. Attention also typically increases the firing rates of cortical sensory neurons. Rate increases improve the signal-to-noise ratio of individual neurons, and this improvement has been assumed to underlie attention-related improvements in behavior. We recorded dozens of neurons simultaneously in visual area V4 and found that changes in single neurons accounted for only a small fraction of the improvement in the sensitivity of the population. Instead, over 80% of the attentional improvement in the population signal was caused by decreases in the correlations between the trial-to-trial fluctuations in the responses of pairs of neurons. These results suggest that the representation of sensory information in populations of neurons and the way attention affects the sensitivity of the population may only be understood by considering the interactions between neurons.
Receptive field properties of neurons in A1 can rapidly adapt their shapes during task performance in accord with specific task demands and salient sensory cues (Fritz et al., Hearing Research, 206:159-176, 2005a, Nature Neuroscience, 6: 1216-1223, 2003). Such modulatory changes selectively enhance overall cortical responsiveness to target (foreground) sounds and thus increase the likelihood of detection against the background of reference sounds. In this study, we develop a mathematical model to describe how enhancing discrimination between two arbitrary classes of sounds can lead to the observed receptive field changes in a variety of spectral and temporal discrimination tasks. Cortical receptive fields are modeled as filters that change their spectro-temporal tuning properties so as to respond best to the discriminatory acoustic features between foreground and background stimuli. We also illustrate how biologically plausible constraints on the spectro-temporal tuning of the receptive fields can be used to optimize the plasticity. Results of the model simulations are compared to published data from a variety of experimental paradigms.
There is a growing consensus that the auditory system is dynamic in its representation of behaviorally relevant sounds. The auditory cortex in particular seems to be an important locus for plasticity that may reflect the memory of such sounds, or functionally improve their processing. The mechanisms that underlie these changes may be either intrinsic because they depend on the receiver's physiological state, or extrinsic because they arise from the context in which behavioral relevance is gained. Research in a mouse model of acoustic communication between offspring and adult females offers the opportunity to explore both of these contributions to auditory cortical plasticity in a natural context. Recent works have found that after the vocalizations of infant mice become behaviorally relevant to mothers, auditory cortical activity is significantly changed in a way that may improve their processing. Here we consider the hypothesis that maternal hormones (intrinsic factor) and sensory experience (extrinsic factor) contribute together to drive these changes, focusing specifically on the evidence that well-known experience-dependent mechanisms of cortical plasticity can be modulated by hormones.
The spectrotemporal receptive field (STRF) is a functional descriptor of the linear processing of time-varying acoustic spectra by the auditory system. By cross-correlating sustained neuronal activity with the dynamic spectrum of a spectrotemporally rich stimulus ensemble, one obtains an estimate of the STRF. In this article, the relationship between the spectrotemporal structure of any given stimulus and the quality of the STRF estimate is explored and exploited. Invoking the Fourier theorem, arbitrary dynamic spectra are described as sums of basic sinusoidal components—that is, moving ripples. Accurate estimation is found to be especially reliant on the prominence of components whose spectral and temporal characteristics are of relevance to the auditory locus under study and is sensitive to the phase relationships between components with identical temporal signatures. These and other observations have guided the development and use of stimuli with deterministic dynamic spectra composed of the superposition of many temporally orthogonal moving ripples having a restricted, relevant range of spectral scales and temporal rates. The method, termed sum-of-ripples, is similar in spirit to the white-noise approach butenjoys the same practical advantages—which equate to faster and moreaccurate estimation—attributable to the time-domain sum-of-sinusoidsmethod previously employed in vision research. Application of the methodis exemplified with both modeled data and experimental data from ferretprimary auditory cortex (AI).
Associative memory for auditory-cued events involves specific plasticity in the primary auditory cortex (A1) that facilitates responses to tones which gain behavioral significance, by modifying representational parameters of sensory coding. Learning strategy, rather than the amount or content of learning, can determine this learning-induced cortical (high order) associative representational plasticity (HARP). Thus, tone-contingent learning with signaled errors can be accomplished either by (1) responding only during tone duration (“tone-duration” strategy, T-Dur), or (2) responding from tone onset until receiving an error signal for responses made immediately after tone offset (“tone-onset-to-error”, TOTE). While rats using both strategies achieve the same high level of performance, only those using the TOTE strategy develop HARP, viz., frequency-specific decreased threshold (increased sensitivity) and decreased bandwidth (increased selectivity) (Berlau & Weinberger, 2008). The present study challenged the generality of learning strategy by determining if high motivation dominates in the formation of HARP. Two groups of adult male rats were trained to bar-press during a 5.0 kHz (10 s, 70 dB) tone for a water reward under either high (HiMot) or moderate (ModMot) levels of motivation. The HiMot group achieved a higher level of correct performance. However, terminal mapping of A1 showed that only the ModMot group developed HARP, i.e., increased sensitivity and selectivity in the signal-frequency band. Behavioral analysis revealed that the ModMot group used the TOTE strategy while HiMot subjects used the T-Dur strategy. Thus, type of learning strategy, not level of learning or motivation, is dominant for the formation of cortical plasticity.