Sensory-to-Category Transformation via Dynamic Reorganization of Ensemble Structures in Mouse Auditory Cortex

Auditory cortex learns to discriminate audiovisual cues through selective multisensory enhancement

Multisensory object discrimination is essential in everyday life, yet the neural mechanisms underlying this process remain unclear. In this study, we trained rats to perform a two-alternative forced-choice task using both auditory and visual cues. Our findings reveal that multisensory perceptual learning actively engages auditory cortex (AC) neurons in both visual and audiovisual processing. Importantly, many audiovisual neurons in the AC exhibited experience-dependent associations between their visual and auditory preferences, displaying a unique integration model. This model employed selective multisensory enhancement for the auditory-visual pairing guiding the contralateral choice, which correlated with improved multisensory discrimination. Furthermore, AC neurons effectively distinguished whether a preferred auditory stimulus was paired with its associated visual stimulus using this distinct integrative mechanism. Our results highlight the capability of sensory cortices to develop sophisticated integrative strategies, adapting to task demands to enhance multisensory discrimination abilities.

Expectation-driven sensory adaptations support enhanced acuity during categorical perception

Expectations can influence perception in seemingly contradictory ways, either by directing attention to expected stimuli and enhancing perceptual acuity or by stabilizing perception and diminishing acuity within expected stimulus categories. The neural mechanisms supporting these dual roles of expectation are not well understood. Here, we trained European starlings to classify ambiguous song syllables in both expected and unexpected acoustic contexts. We show that birds employ probabilistic, Bayesian integration to classify syllables, leveraging their expectations to stabilize their perceptual behavior. However, auditory sensory neural populations do not reflect this integration. Instead, expectation enhances the acuity of auditory sensory neurons in high-probability regions of the stimulus space. This modulation diverges from patterns typically observed in motor areas, where Bayesian integration of sensory inputs and expectations predominates. Our results suggest that peripheral sensory systems use expectation to improve sensory representations and maintain high-fidelity representations of the world, allowing downstream circuits to flexibly integrate this information with expectations to drive behavior.

Causal contributions of cell-type-specific circuits in the posterior dorsal striatum to auditory decision-making

In the dorsal striatum (DS), the direct- and indirect-pathway striatal projection neurons (dSPNs and iSPNs) play crucial opposing roles in controlling actions. However, it remains unclear whether and how dSPNs and iSPNs provide distinct and specific contributions to decision-making, a process transforming sensory inputs to actions. Here, we perform causal interrogations on the roles of dSPNs and iSPNs in the posterior DS (pDS) in auditory-guided decision-making. Unilateral activation of dSPNs or iSPNs produces strong opposite drives to choice behaviors regardless of task difficulty. However, inactivation of dSPNs or iSPNs leads to pronounced choice bias preferentially in difficult trials, suggesting decision-specific contributions. Indeed, temporally specific iSPN activation within, but not outside, the decision period significantly biased choices. Finally, concurrent disinhibition of both pathways via inactivating parvalbumin (PV)-positive interneurons leads to contralateral bias primarily in difficult trials. These results reveal specific contributions by coordinated dSPN and iSPN activity to decision-making processes.

Individual-specific strategies inform category learning

Categorization is an essential task for sensory perception. Individuals learn category labels using a variety of strategies to ensure that sensory signals, such as sounds or images, can be assigned to proper categories. Categories are often learned on the basis of extreme examples, and the boundary between categories can differ among individuals. The trajectories for learning also differ among individuals, as different individuals rely on different strategies, such as repeating or alternating choices. However, little is understood about the relationship between individual learning trajectories and learned categorization. To study this relationship, we trained mice to categorize auditory stimuli into two categories using a two-alternative forced choice task. Because the mice took several weeks to learn the task, we were able to quantify the time course of individual strategies and how they relate to how mice categorize stimuli around the categorization boundary. Different mice exhibited different trajectories while learning the task. Mice displayed preferences for a specific category, manifested by a choice bias in their responses, but this bias drifted with learning. We found that this drift in choice bias correlated with variability in the category boundary for sounds with ambiguous category membership. Next, we asked how stimulus-independent, individual-specific strategies informed learning. We found that the tendency to repeat choices, which is a form of perseveration, contributed to long-term learning. These results indicate that long-term trends in individual strategies during category learning affect learned category boundaries.

Individual-specific strategies inform category learning

Categorization is an essential task for sensory perception. Individuals learn category labels using a variety of strategies to ensure that sensory signals, such as sounds or images, can be assigned to proper categories. Categories are often learned on the basis of extreme examples, and the boundary between categories can differ among individuals. The trajectories for learning also differ among individuals, as different individuals rely on different strategies, such as repeating or alternating choices. However, little is understood about the relationship between individual learning trajectories and learned categorization. To study this relationship, we trained mice to categorize auditory stimuli into two categories using a two-alternative forced choice task. Because the mice took several weeks to learn the task, we were able to quantify the time course of individual strategies and how they relate to how mice categorize stimuli around the categorization boundary. Different mice exhibited different trajectories while learning the task. Mice displayed preferences for a specific category, manifested by a choice bias in their responses, but this bias drifted with learning. We found that this drift in choice bias correlated with variability in the category boundary for sounds with ambiguous category membership. Next, we asked how stimulus-independent, individual-specific strategies informed learning. We found that the tendency to repeat choices, which is a form of perseveration, contributed to long-term learning. These results indicate that long-term trends in individual strategies during category learning affect learned category boundaries.

Dynamic ensemble balance in direct- and indirect-pathway striatal projection neurons underlying decision-related action selection

The posterior dorsal striatum (pDS) plays an essential role in sensory-guided decision-making. However, it remains unclear how the antagonizing direct- and indirect-pathway striatal projection neurons (dSPNs and iSPNs) work in concert to support action selection. Here, we employed deep-brain two-photon imaging to investigate pathway-specific single-neuron and population representations during an auditory-guided decision-making task. We found that the majority of pDS projection neurons predominantly encode choice information. Both dSPNs and iSPNs comprise divergent subpopulations of comparable sizes representing competing choices, rendering a multi-ensemble balance between the two pathways. Intriguingly, such ensemble balance displays a dynamic shift during the decision period: dSPNs show a significantly stronger preference for the contraversive choice than iSPNs. This dynamic shift is further manifested in the inter-neuronal coactivity and population trajectory divergence. Our results support a balance-shift model as a neuronal population mechanism coordinating the direct and indirect striatal pathways for eliciting selected actions during decision-making.

Single neuron responses to perceptual difficulty in the mouse auditory cortex

Perceptual learning leads to improvement in behavioral performance, yet how the brain supports challenging perceptual demands is unknown. We used two photon imaging in the mouse primary auditory cortex during behavior in a Go-NoGo task designed to test perceptual difficulty. Using general linear model analysis, we found a subset of neurons that increased their responses during high perceptual demands. Single neurons increased their responses to both Go and NoGo sounds when mice were engaged in the more difficult perceptual discrimination. This increased responsiveness contributes to enhanced cortical network discriminability for the learned sounds. Under passive listening conditions, the same neurons responded weaker to the more similar sound pairs of the difficult task, and the training protocol by itself induced specific suppression to the learned sounds. Our findings identify how neuronal activity in auditory cortex is modulated during high perceptual demands, which is a fundamental feature associated with perceptual improvement.

Mixed Representations of Sound and Action in the Auditory Midbrain

Linking sensory input and its consequences is a fundamental brain operation. During behavior, the neural activity of neocortical and limbic systems often reflects dynamic combinations of sensory and task-dependent variables, and these "mixed representations" are suggested to be important for perception, learning, and plasticity. However, the extent to which such integrative computations might occur outside of the forebrain is less clear. Here, we conduct cellular-resolution two-photon Ca2+ imaging in the superficial "shell" layers of the inferior colliculus (IC), as head-fixed mice of either sex perform a reward-based psychometric auditory task. We find that the activity of individual shell IC neurons jointly reflects auditory cues, mice's actions, and behavioral trial outcomes, such that trajectories of neural population activity diverge depending on mice's behavioral choice. Consequently, simple classifier models trained on shell IC neuron activity can predict trial-by-trial outcomes, even when training data are restricted to neural activity occurring prior to mice's instrumental actions. Thus, in behaving mice, auditory midbrain neurons transmit a population code that reflects a joint representation of sound, actions, and task-dependent variables.

Differential representation of sensory information and behavioral choice across layers of the mouse auditory cortex

The activity of neurons in sensory areas sometimes covaries with upcoming choices in decision-making tasks. However, the prevalence, causal origin, and functional role of choice-related activity remain controversial. Understanding the circuit-logic of decision signals in sensory areas will require understanding their laminar specificity, but simultaneous recordings of neural activity across the cortical layers in forced-choice discrimination tasks have not yet been performed. Here, we describe neural activity from such recordings in the auditory cortex of mice during a frequency discrimination task with delayed report, which, as we show, requires the auditory cortex. Stimulus-related information was widely distributed across layers but disappeared very quickly after stimulus offset. Choice selectivity emerged toward the end of the delay period-suggesting a top-down origin-but only in the deep layers. Early stimulus-selective and late choice-selective deep neural ensembles were correlated, suggesting that the choice-selective signal fed back to the auditory cortex is not just action specific but develops as a consequence of the sensory-motor contingency imposed by the task.

Controlling spatiotemporal dynamics of neural networks by Lévy noise

We explore numerically how additive Lévy noise influences the spatiotemporal dynamics of a neural network of nonlocally coupled FitzHugh-Nagumo oscillators. Without noise, the network can exhibit various partial or cluster synchronization patterns, such as chimera and solitary states, which can also coexist in the network for certain values of the control parameters. Our studies show that these structures demonstrate different responses to additive Lévy noise and, thus, the dynamics of the neural network can be effectively controlled by varying the scale parameter and the stability index of Lévy noise. Specifically, introducing Lévy noise in the multistability mode can increase the probability of observing chimera states while suppressing solitary states. Nonetheless, decreasing the stability parameter enables one to diminish the noise effect on chimera states and amplify it on solitary states.

Representational maps in the brain: concepts, approaches, and applications

Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.

Primary auditory cortex is necessary for the acquisition and expression of categorical behavior

The primary auditory cortex (ACtx) is critically involved in the association of sensory information with specific behavioral outcomes. Such sensory-guided behaviors are necessarily brain-wide endeavors, requiring a plethora of distinct brain areas, including those that are involved in aspects of decision making, motor planning, motor initiation, and reward prediction. ACtx comprises a number of distinct excitatory cell-types that allow for the brain-wide propagation of behaviorally-relevant sensory information. Exactly how ACtx involvement changes as a function of learning, as well as the functional role of distinct excitatory cell-types is unclear. Here, we addressed these questions by designing a two-choice auditory task in which water-restricted, head-fixed mice were trained to categorize the temporal rate of a sinusoidal amplitude modulated (sAM) noise burst and used transient cell-type specific optogenetics to probe ACtx necessity across the duration of learning. Our data demonstrate that ACtx is necessary for the ability to categorize the rate of sAM noise, and this necessity grows across learning. ACtx silencing substantially altered the behavioral strategies used to solve the task by introducing a fluctuating choice bias and increasing dependence on prior decisions. Furthermore, ACtx silencing did not impact the animal's motor report, suggesting that ACtx is necessary for the conversion of sensation to action. Targeted inhibition of extratelencephalic projections on just 20% of trials had a minimal effect on task performance, but significantly degraded learning. Taken together, our data suggest that distinct cortical cell-types synergistically control auditory-guided behavior and that extratelencephalic neurons play a critical role in learning and plasticity.

Fractured columnar small-world functional network organization in volumes of L2/3 of mouse auditory cortex

The sensory cortices of the brain exhibit large-scale functional topographic organization, such as the tonotopic organization of the primary auditory cortex (A1) according to sound frequency. However, at the level of individual neurons, layer 2/3 (L2/3) A1 appears functionally heterogeneous. To identify if there exists a higher-order functional organization of meso-scale neuronal networks within L2/3 that bridges order and disorder, we used in vivo two-photon calcium imaging of pyramidal neurons to identify networks in three-dimensional volumes of L2/3 A1 in awake mice. Using tonal stimuli, we found diverse receptive fields with measurable colocalization of similarly tuned neurons across depth but less so across L2/3 sublayers. These results indicate a fractured microcolumnar organization with a column radius of ∼50 µm, with a more random organization of the receptive field over larger radii. We further characterized the functional networks formed within L2/3 by analyzing the spatial distribution of signal correlations (SCs). Networks show evidence of Rentian scaling in physical space, suggesting effective spatial embedding of subnetworks. Indeed, functional networks have characteristics of small-world topology, implying that there are clusters of functionally similar neurons with sparse connections between differently tuned neurons. These results indicate that underlying the regularity of the tonotopic map on large scales in L2/3 is significant tuning diversity arranged in a hybrid organization with microcolumnar structures and efficient network topologies.

Automating licking bias correction in a two-choice delayed match-to-sample task to accelerate learning

Animals often display choice bias, or a preference for one option over the others, which can significantly impede learning new tasks. Delayed match-to-sample (DMS) tasks with two-alternative choices of lickports on the left and right have been widely used to study sensory processing, working memory, and associative memory in head-fixed animals. However, extensive training time, primarily due to the animals' biased licking responses, limits their practical utility. Here, we present the implementation of an automated side bias correction system in an olfactory DMS task, where the lickport positions and the ratio of left- and right-rewarded trials are dynamically adjusted to counterbalance mouse's biased licking responses during training. The correction algorithm moves the preferred lickport farther away from the mouse's mouth and the non-preferred lickport closer, while also increasing the proportion of non-preferred side trials when biased licking occurs. We found that adjusting lickport distances and the proportions of left- versus right-rewarded trials effectively reduces the mouse's side bias. Further analyses reveal that these adjustments also correlate with subsequent improvements in behavioral performance. Our findings suggest that the automated side bias correction system is a valuable tool for enhancing the applicability of behavioral tasks involving two-alternative lickport choices.

Dynamics and maintenance of categorical responses in primary auditory cortex during task engagement

Grouping sets of sounds into relevant categories is an important cognitive ability that enables the association of stimuli with appropriate goal-directed behavioral responses. In perceptual tasks, the primary auditory cortex (A1) assumes a prominent role by concurrently encoding both sound sensory features and task-related variables. Here, we sought to explore the role of A1 in the initiation of sound categorization, shedding light on its involvement in this cognitive process. We trained ferrets to discriminate click trains of different rates in a Go/No-Go delayed categorization task and recorded neural activity during both active behavior and passive exposure to the same sounds. Purely categorical response components were extracted and analyzed separately from sensory responses to reveal their contributions to the overall population response throughout the trials. We found that categorical activity emerged during sound presentation in the population average and was present in both active behavioral and passive states. However, upon task engagement, categorical responses to the No-Go category became suppressed in the population code, leading to an asymmetrical representation of the Go stimuli relative to the No-Go sounds and pre-stimulus baseline. The population code underwent an abrupt change at stimulus offset, with sustained responses after the Go sounds during the delay period. Notably, the categorical responses observed during the stimulus period exhibited a significant correlation with those extracted from the delay epoch, suggesting an early involvement of A1 in stimulus categorization.

Learning-prolonged maintenance of stimulus information in CA1 and subiculum during trace fear conditioning

Temporal associative learning binds discontiguous conditional stimuli (CSs) and unconditional stimuli (USs), possibly by maintaining CS information in the hippocampus after its offset. Yet, how learning regulates such maintenance of CS information in hippocampal circuits remains largely unclear. Using the auditory trace fear conditioning (TFC) paradigm, we identify a projection from the CA1 to the subiculum critical for TFC. Deep-brain calcium imaging shows that the peak of trace activity in the CA1 and subiculum is extended toward the US and that the CS representation during the trace period is enhanced during learning. Interestingly, such plasticity is consolidated only in the CA1, not the subiculum, after training. Moreover, CA1 neurons, but not subiculum neurons, increasingly become active during CS-and-trace and shock periods, respectively, and correlate with CS-evoked fear retrieval afterward. These results indicate that learning dynamically enhances stimulus information maintenance in the CA1-subiculum circuit during learning while storing CS and US memories primarily in the CA1 area.

Altered population activity and local tuning heterogeneity in auditory cortex of Cacna2d3-deficient mice

The α2δ3 auxiliary subunit of voltage-activated calcium channels is required for normal synaptic transmission and precise temporal processing of sounds in the auditory brainstem. In mice its loss additionally leads to an inability to distinguish amplitude-modulated tones. Furthermore, loss of function of α2δ3 has been associated with autism spectrum disorder in humans. To investigate possible alterations of network activity in the higher-order auditory system in α2δ3 knockout mice, we analyzed neuronal activity patterns and topography of frequency tuning within networks of the auditory cortex (AC) using two-photon Ca2+ imaging. Compared to wild-type mice we found distinct subfield-specific alterations in the primary auditory cortex, expressed in overall lower correlations between the network activity patterns in response to different sounds as well as lower reliability of these patterns upon repetitions of the same sound. Higher AC subfields did not display these alterations but showed a higher amount of well-tuned neurons along with lower local heterogeneity of the neurons' frequency tuning. Our results provide new insight into AC network activity alterations in an autism spectrum disorder-associated mouse model.

Adaptive mechanisms facilitate robust performance in noise and in reverberation in an auditory categorization model

For robust vocalization perception, the auditory system must generalize over variability in vocalization production as well as variability arising from the listening environment (e.g., noise and reverberation). We previously demonstrated using guinea pig and marmoset vocalizations that a hierarchical model generalized over production variability by detecting sparse intermediate-complexity features that are maximally informative about vocalization category from a dense spectrotemporal input representation. Here, we explore three biologically feasible model extensions to generalize over environmental variability: (1) training in degraded conditions, (2) adaptation to sound statistics in the spectrotemporal stage and (3) sensitivity adjustment at the feature detection stage. All mechanisms improved vocalization categorization performance, but improvement trends varied across degradation type and vocalization type. One or more adaptive mechanisms were required for model performance to approach the behavioral performance of guinea pigs on a vocalization categorization task. These results highlight the contributions of adaptive mechanisms at multiple auditory processing stages to achieve robust auditory categorization.

Neural Mechanisms That Make Perceptual Decisions Flexible

Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.

Daily two-photon neuronal population imaging with targeted single-cell electrophysiology and subcellular imaging in auditory cortex of behaving mice

Quantitative and mechanistic understanding of learning and long-term memory at the level of single neurons in living brains require highly demanding techniques. A specific need is to precisely label one cell whose firing output property is pinpointed amidst a functionally characterized large population of neurons through the learning process and then investigate the distribution and properties of dendritic inputs. Here, we disseminate an integrated method of daily two-photon neuronal population Ca²⁺ imaging through an auditory associative learning course, followed by targeted single-cell loose-patch recording and electroporation of plasmid for enhanced chronic Ca²⁺ imaging of dendritic spines in the targeted cell. Our method provides a unique solution to the demand, opening a solid path toward the hard-cores of how learning and long-term memory are physiologically carried out at the level of single neurons and synapses.

Cortical sensory processing across motivational states during goal-directed behavior

Behavioral states can influence performance of goal-directed sensorimotor tasks. Yet, it is unclear how altered neuronal sensory representations in these states relate to task performance and learning. We trained water-restricted mice in a two-whisker discrimination task to study cortical circuits underlying perceptual decision-making under different levels of thirst. We identified somatosensory cortices as well as the premotor cortex as part of the circuit necessary for task execution. Two-photon calcium imaging in these areas identified populations selective to sensory or motor events. Analysis of task performance during individual sessions revealed distinct behavioral states induced by decreasing levels of thirst-related motivation. Learning was better explained by improvements in motivational state control rather than sensorimotor association. Whisker sensory representations in the cortex were altered across behavioral states. In particular, whisker stimuli could be better decoded from neuronal activity during high task performance states, suggesting that state-dependent changes of sensory processing influence decision-making.

Reshaping sensory representations by task-specific brain states: Toward cortical circuit mechanisms

Perception is internally constructed by integrating brain states with external sensory inputs, a process depending on the topdown modulation of sensory representations. A wealth of earlier studies described task-dependent modulations of sensory cortex corroborating perceptual and behavioral phenomena. But only with recent technological advancements, the underlying circuit-level mechanisms began to be unveiled. We review recent progress along this line of research. It begins to be appreciated that topdown signals can encode various types of task-related information, ranging from task engagement, and category knowledge to decision execution; these signals are transferred via feedback pathways originating from distinct association cortices and interact with sensory cortical circuits. These plausible mechanisms support a broad range of computations from predictive coding to inference making, ultimately form dynamic percepts and endow behavioral flexibility.

How to discern external acoustic waves in a piezoelectric neuron under noise?

Biological neurons keep sensitive to external stimuli and appropriate firing modes can be triggered to give effective response to external chemical and physical signals. A piezoelectric neural circuit can perceive external voice and nonlinear vibration by generating equivalent piezoelectric voltage, which can generate an equivalent trans-membrane current for inducing a variety of firing modes in the neural activities. Biological neurons can receive external stimuli from more ion channels and synapse synchronously, but the further encoding and priority in mode selection are competitive. In particular, noisy disturbance and electromagnetic radiation make it more difficult in signals identification and mode selection in the firing patterns of neurons driven by multi-channel signals. In this paper, two different periodic signals accompanied by noise are used to excite the piezoelectric neural circuit, and the signal processing in the piezoelectric neuron driven by acoustic waves under noise is reproduced and explained. The physical energy of the piezoelectric neural circuit and Hamilton energy in the neuron driven by mixed signals are calculated to explain the biophysical mechanism of auditory neuron when external stimuli are applied. It is found that the neuron prefers to respond to the external stimulus with higher physical energy and the signal which can increase the Hamilton energy of the neuron. For example, stronger inputs used to inject higher energy and it is detected and responded more sensitively. The involvement of noise is helpful to detect the external signal under stochastic resonance, and the additive noise changes the excitability of neuron as the external stimulus. The results indicate that energy controls the firing patterns and mode selection in neurons, and it provides clues to control the neural activities by injecting appropriate energy into the neurons and network.

Real-Time Image Processing Toolbox for All-Optical Closed-Loop Control of Neuronal Activities

The development of in vivo imaging and optogenetic tools makes it possible to control neural circuit activities in an all-optical, closed-loop manner, but such applications are limited by the lack of software for online analysis of neuronal imaging data. We developed an analysis software ORCA (Online Real-time activity and offline Cross-session Analysis), which performs image registration, neuron segmentation, and activity extraction at over 100 frames per second, fast enough to support real-time detection and readout of neural activity. Our active neuron detection algorithm is purely statistical, achieving a much higher speed than previous methods. We demonstrated closed-loop control of neurons that were identified on the fly, without prior recording or image processing. ORCA also includes a cross-session alignment module that efficiently tracks neurons across multiple sessions. In summary, ORCA is a powerful toolbox for fast imaging data analysis and provides a solution for all-optical closed-loop control of neuronal activity.

Dynamic Distortion of Orientation Representation after Learning in the Mouse Primary Visual Cortex

Learning is an essential cognitive mechanism allowing behavioral adaptation through adjustments in neuronal processing. It is associated with changes in the activity of sensory cortical neurons evoked by task-relevant stimuli. However, the exact nature of those modifications and the computational advantages they may confer are still debated. Here, we investigated how learning an orientation discrimination task alters the neuronal representations of the cues orientations in the primary visual cortex (V1) of male and female mice. When comparing the activity evoked by the task stimuli in naive mice and the mice performing the task, we found that the representations of the orientation of the rewarded and nonrewarded cues were more accurate and stable in trained mice. This better cue representation in trained mice was associated with a distortion of the orientation representation space such that stimuli flanking the task-relevant orientations were represented as the task stimuli themselves, suggesting that those stimuli were generalized as the task cues. This distortion was context dependent as it was absent in trained mice passively viewing the task cues and enhanced in the behavioral sessions where mice performed best. Those modifications of the V1 population orientation representation in performing mice were supported by a suppression of the activity of neurons tuned for orientations neighboring the orientations of the task cues. Thus, visual processing in V1 is dynamically adapted to enhance the reliability of the representation of the learned cues and favor generalization in the task-relevant computational space.SIGNIFICANCE STATEMENT Performance improvement in a task often requires facilitating the extraction of the information necessary to its execution. Here, we demonstrate the existence of a suppression mechanism that improves the representation of the orientations of the task stimuli in the V1 of mice performing an orientation discrimination task. We also show that this mechanism distorts the V1 orientation representation space, leading stimuli flanking the task stimuli orientations to be generalized as the task stimuli themselves.

A silent two-photon imaging system for studying in vivo auditory neuronal functions

Two-photon laser-scanning microscopy has become an essential tool for imaging neuronal functions in vivo and has been applied to different parts of the neural system, including the auditory system. However, many components of a two-photon microscope, such as galvanometer-based laser scanners, generate mechanical vibrations and thus acoustic artifacts, making it difficult to interpret auditory responses from recorded neurons. Here, we report the development of a silent two-photon imaging system and its applications in the common marmoset (Callithrix Jacchus), a non-human primate species sharing a similar hearing range with humans. By utilizing an orthogonal pair of acousto-optical deflectors (AODs), full-frame raster scanning at video rate was achieved without introducing mechanical vibrations. Imaging depth can be optically controlled by adjusting the chirping speed on the AODs without any mechanical motion along the Z-axis. Furthermore, all other sound-generating components of the system were acoustically isolated, leaving the noise floor of the working system below the marmoset's hearing threshold. Imaging with the system in awake marmosets revealed many auditory cortex neurons that exhibited maximal responses at low sound levels, which were not possible to study using traditional two-photon imaging systems. This is the first demonstration of a silent two-photon imaging system that is capable of imaging auditory neuronal functions in vivo without acoustic artifacts. This capacity opens new opportunities for a better understanding of auditory functions in the brain and helps isolate animal behavior from microscope-generated acoustic interference.

Donut-like organization of inhibition underlies categorical neural responses in the midbrain

Categorical neural responses underlie various forms of selection and decision-making. Such binary-like responses promote robust signaling of the winner in the presence of input ambiguity and neural noise. Here, we show that a 'donut-like' inhibitory mechanism in which each competing option suppresses all options except itself, is highly effective at generating categorical neural responses. It surpasses motifs of feedback inhibition, recurrent excitation, and divisive normalization invoked frequently in decision-making models. We demonstrate experimentally not only that this mechanism operates in the midbrain spatial selection network in barn owls, but also that it is necessary for categorical signaling by it. The functional pattern of neural inhibition in the midbrain forms an exquisitely structured 'multi-holed' donut consistent with this network's combinatorial inhibitory function for stimulus selection. Additionally, modeling reveals a generalizable neural implementation of the donut-like motif for categorical selection. Self-sparing inhibition may, therefore, be a powerful circuit module central to categorization.

Neuronal Circuits Associated with Fear Memory: Potential Therapeutic Targets for Posttraumatic Stress Disorder

Posttraumatic stress disorder (PTSD) is a psychiatric disorder that is associated with long-lasting memories of traumatic experiences. Extinction and discrimination of fear memory have become therapeutic targets for PTSD. Newly developed optogenetics and advanced in vivo imaging techniques have provided unprecedented spatiotemporal tools to characterize the activity, connectivity, and functionality of specific cell types in complicated neuronal circuits. The use of such tools has offered mechanistic insights into the exquisite organization of the circuitry underlying the extinction and discrimination of fear memory. This review focuses on the acquisition of more detailed, comprehensive, and integrated neural circuits to understand how the brain regulates the extinction and discrimination of fear memory. A future challenge is to translate these researches into effective therapeutic treatment for PTSD from the perspective of precise regulation of the neural circuits associated with the extinction and discrimination of fear memories.

Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

Recently developed methods in computational neuroethology have enabled increasingly detailed and comprehensive quantification of animal movements and behavioral kinematics. Vocal communication behavior is well poised for application of similar large-scale quantification methods in the service of physiological and ethological studies. This review describes emerging techniques that can be applied to acoustic and vocal communication signals with the goal of enabling study beyond a small number of model species. We review a range of modern computational methods for bioacoustics, signal processing, and brain-behavior mapping. Along with a discussion of recent advances and techniques, we include challenges and broader goals in establishing a framework for the computational neuroethology of vocal communication.

Confidence-Controlled Hebbian Learning Efficiently Extracts Category Membership From Stimuli Encoded in View of a Categorization Task

In experiments on perceptual decision making, individuals learn a categorization task through trial-and-error protocols. We explore the capacity of a decision-making attractor network to learn a categorization task through reward-based, Hebbian-type modifications of the weights incoming from the stimulus encoding layer. For the latter, we assume a standard layer of a large number of stimulus-specific neurons. Within the general framework of Hebbian learning, we have hypothesized that the learning rate is modulated by the reward at each trial. Surprisingly, we find that when the coding layer has been optimized in view of the categorization task, such reward-modulated Hebbian learning (RMHL) fails to extract efficiently the category membership. In previous work, we showed that the attractor neural networks' nonlinear dynamics accounts for behavioral confidence in sequences of decision trials. Taking advantage of these findings, we propose that learning is controlled by confidence, as computed from the neural activity of the decision-making attractor network. Here we show that this confidence-controlled, reward-based Hebbian learning efficiently extracts categorical information from the optimized coding layer. The proposed learning rule is local and, in contrast to RMHL, does not require storing the average rewards obtained on previous trials. In addition, we find that the confidence-controlled learning rule achieves near-optimal performance. In accordance with this result, we show that the learning rule approximates a gradient descent method on a maximizing reward cost function.

Categorical Perception: A Groundwork for Deep Learning

Classification is one of the major tasks that deep learning is successfully tackling. Categorization is also a fundamental cognitive ability. A well-known perceptual consequence of categorization in humans and other animals, categorical per ception, is notably characterized by a within-category compression and a between-category separation: two items, close in input space, are perceived closer if they belong to the same category than if they belong to different categories. Elaborating on experimental and theoretical results in cognitive science, here we study categorical effects in artificial neural networks. We combine a theoretical analysis that makes use of mutual and Fisher information quantities and a series of numerical simulations on networks of increasing complexity. These formal and numerical analyses provide insights into the geometry of the neural representation in deep layers, with expansion of space near category boundaries and contraction far from category boundaries. We investigate categorical representation by using two complementary approaches: one mimics experiments in psychophysics and cognitive neuroscience by means of morphed continua between stimuli of different categories, while the other introduces a categoricality index that, for each layer in the network, quantifies the separability of the categories at the neural population level. We show on both shallow and deep neural networks that category learning automatically induces categorical perception. We further show that the deeper a layer, the stronger the categorical effects. As an outcome of our study, we propose a coherent view of the efficacy of different heuristic practices of the dropout regularization technique. More generally, our view, which finds echoes in the neuroscience literature, insists on the differential impact of noise in any given layer depending on the geometry of the neural representation that is being learned, that is, on how this geometry reflects the structure of the categories.

Multisensory-Guided Associative Learning Enhances Multisensory Representation in Primary Auditory Cortex

Sensory cortices, classically considered to represent modality-specific sensory information, are also found to engage in multisensory processing. However, how sensory processing in sensory cortices is cross-modally modulated remains an open question. Specifically, we understand little of cross-modal representation in sensory cortices in perceptual tasks and how perceptual learning modifies this process. Here, we recorded neural responses in primary auditory cortex (A1) both while freely moving rats discriminated stimuli in Go/No-Go tasks and when anesthetized. Our data show that cross-modal representation in auditory cortices varies with task contexts. In the task of an audiovisual cue being the target associating with water reward, a significantly higher proportion of auditory neurons showed a visually evoked response. The vast majority of auditory neurons, if processing auditory-visual interactions, exhibit significant multisensory enhancement. However, when the rats performed tasks with unisensory cues being the target, cross-modal inhibition, rather than enhancement, predominated. In addition, multisensory associational learning appeared to leave a trace of plastic change in A1, as a larger proportion of A1 neurons showed multisensory enhancement in anesthesia. These findings indicate that multisensory processing in principle sensory cortices is not static, and having cross-modal interaction in the task requirement can substantially enhance multisensory processing in sensory cortices.

A cortical circuit mechanism for structural knowledge-based flexible sensorimotor decision-making

Making flexible decisions based on prior knowledge about causal environmental structures is a hallmark of goal-directed cognition in mammalian brains. Although several association brain regions, including the orbitofrontal cortex (OFC), have been implicated, the precise neuronal circuit mechanisms underlying knowledge-based decision-making remain elusive. Here, we established an inference-based auditory categorization task where mice performed within-session flexible stimulus re-categorization by inferring the changing task rules. We constructed a reinforcement learning model to recapitulate the inference-based flexible behavior and quantify the hidden variables associated with task structural knowledge. Combining two-photon population imaging and projection-specific optogenetics, we found that auditory cortex (ACx) neurons encoded the hidden task rule variable, which requires feedback input from the OFC. Silencing OFC-ACx input specifically disrupted re-categorization behavior. Direct imaging from OFC axons in the ACx revealed task state-related feedback signals, supporting the knowledge-based updating mechanism. Our data reveal a cortical circuit mechanism underlying structural knowledge-based flexible decision-making.

Neural Decoding Reveals Concurrent Phonemic and Subphonemic Representations of Speech Across Tasks

Robust and efficient speech perception relies on the interpretation of acoustically variable phoneme realizations, yet prior neuroimaging studies are inconclusive regarding the degree to which subphonemic detail is maintained over time as categorical representations arise. It is also unknown whether this depends on the demands of the listening task. We addressed these questions by using neural decoding to quantify the (dis)similarity of brain response patterns evoked during two different tasks. We recorded magnetoencephalography (MEG) as adult participants heard isolated, randomized tokens from a /ba/-/da/ speech continuum. In the passive task, their attention was diverted. In the active task, they categorized each token as ba or da. We found that linear classifiers successfully decoded ba vs. da perception from the MEG data. Data from the left hemisphere were sufficient to decode the percept early in the trial, while the right hemisphere was necessary but not sufficient for decoding at later time points. We also decoded stimulus representations and found that they were maintained longer in the active task than in the passive task; however, these representations did not pattern more like discrete phonemes when an active categorical response was required. Instead, in both tasks, early phonemic patterns gave way to a representation of stimulus ambiguity that coincided in time with reliable percept decoding. Our results suggest that the categorization process does not require the loss of subphonemic detail, and that the neural representation of isolated speech sounds includes concurrent phonemic and subphonemic information.

Hierarchy in sensory processing reflected by innervation balance on cortical interneurons

Sensory processing is subjected to modulation by behavioral contexts that are often mediated by long-range inputs to cortical interneurons, but their selectivity to different types of interneurons remains largely unknown. Using rabies-virus tracing and optogenetics-assisted recording, we analyzed the long-range connections to various brain regions along the hierarchy of visual processing, including primary visual cortex, medial association cortices, and frontal cortices. We found that hierarchical corticocortical and thalamocortical connectivity is reflected by the relative weights of inputs to parvalbumin-positive (PV⁺) and vasoactive intestinal peptide-positive (VIP⁺) neurons within the conserved local circuit motif, with bottom-up and top-down inputs preferring PV⁺ and VIP⁺ neurons, respectively. Our algorithms based on innervation weights for these two types of local interneurons generated testable predictions of the hierarchical position of many brain areas. These results support the notion that preferential long-range inputs to specific local interneurons are essential for the hierarchical information flow in the brain.

Mouse prefrontal cortex represents learned rules for categorization

The ability to categorize sensory stimuli is crucial for an animal’s survival in a complex environment. Memorizing categories instead of individual exemplars enables greater behavioural flexibility and is computationally advantageous. Neurons that show category selectivity have been found in several areas of the mammalian neocortex^1–4, but the prefrontal cortex seems to have a prominent role^4,5 in this context. Specifically, in primates that are extensively trained on a categorization task, neurons in the prefrontal cortex rapidly and flexibly represent learned categories^6,7. However, how these representations first emerge in naive animals remains unexplored, leaving it unclear whether flexible representations are gradually built up as part of semantic memory or assigned more or less instantly during task execution^8,9. Here we investigate the formation of a neuronal category representation throughout the entire learning process by repeatedly imaging individual cells in the mouse medial prefrontal cortex. We show that mice readily learn rule-based categorization and generalize to novel stimuli. Over the course of learning, neurons in the prefrontal cortex display distinct dynamics in acquiring category selectivity and are differentially engaged during a later switch in rules. A subset of neurons selectively and uniquely respond to categories and reflect generalization behaviour. Thus, a category representation in the mouse prefrontal cortex is gradually acquired during learning rather than recruited ad hoc. This gradual process suggests that neurons in the medial prefrontal cortex are part of a specific semantic memory for visual categories.

Neurons in the mouse medial prefrontal cortex acquire category-selective responses with learning.

Sequence Learning Induces Selectivity to Multiple Task Parameters in Mouse Somatosensory Cortex

Sequential temporal ordering and patterning are key features of natural signals, used by the brain to decode stimuli and perceive them as sensory objects. To explore how cortical neuronal activity underpins sequence discrimination, we developed a task in which mice distinguished between tactile "word" sequences constructed from distinct vibrations delivered to the whiskers, assembled in different orders. Animals licked to report the presence of the target sequence. Mice could respond to the earliest possible cues allowing discrimination, effectively solving the task as a "detection of change" problem, but enhanced their performance when responding later. Optogenetic inactivation showed that the somatosensory cortex was necessary for sequence discrimination. Two-photon imaging in layer 2/3 of the primary somatosensory "barrel" cortex (S1bf) revealed that, in well-trained animals, neurons had heterogeneous selectivity to multiple task variables including not just sensory input but also the animal's action decision and the trial outcome (presence or absence of the predicted reward). Many neurons were activated preceding goal-directed licking, thus reflecting the animal's learned action in response to the target sequence; these neurons were found as soon as mice learned to associate the rewarded sequence with licking. In contrast, learning evoked smaller changes in sensory response tuning: neurons responding to stimulus features were found in naive mice, and training did not generate neurons with enhanced temporal integration or categorical responses. Therefore, in S1bf, sequence learning results in neurons whose activity reflects the learned association between target sequence and licking rather than a refined representation of sensory features.

Mouse visual cortex areas represent perceptual and semantic features of learned visual categories

Associative memories are stored in distributed networks extending across multiple brain regions. However, it is unclear to what extent sensory cortical areas are part of these networks. Using a paradigm for visual category learning in mice, we investigated whether perceptual and semantic features of learned category associations are already represented at the first stages of visual information processing in the neocortex. Mice learned categorizing visual stimuli, discriminating between categories and generalizing within categories. Inactivation experiments showed that categorization performance was contingent on neuronal activity in the visual cortex. Long-term calcium imaging in nine areas of the visual cortex identified changes in feature tuning and category tuning that occurred during this learning process, most prominently in the postrhinal area (POR). These results provide evidence for the view that associative memories form a brain-wide distributed network, with learning in early stages shaping perceptual representations and supporting semantic content downstream.

A distributed dynamic brain network mediates linguistic tone representation and categorization

Successful categorization requires listeners to represent the incoming sensory information, resolve the "blooming, buzzing confusion" inherent to noisy sensory signals, and leverage the accumulated evidence towards making a decision. Despite decades of intense debate, the neural systems underlying speech categorization remain unresolved. Here we assessed the neural representation and categorization of lexical tones by native Mandarin speakers (N = 31) across a range of acoustic and contextual variabilities (talkers, perceptual saliences, and stimulus-contexts) using functional magnetic imaging (fMRI) and an evidence accumulation model of decision-making. Univariate activation and multivariate pattern analyses reveal that the acoustic-variability-tolerant representations of tone category are observed within the middle portion of the left superior temporal gyrus (STG). Activation patterns in the frontal and parietal regions also contained category-relevant information that was differentially sensitive to various forms of variability. The robustness of neural representations of tone category in a distributed fronto-temporoparietal network is associated with trial-by-trial decision-making parameters. These findings support a hybrid model involving a representational core within the STG that operates dynamically within an extensive frontoparietal network to support the representation and categorization of linguistic pitch patterns.

Identification and quantification of neuronal ensembles in optical imaging experiments

Recent technical advances in molecular biology and optical imaging have made it possible to record from up to thousands of densely packed neurons in superficial and deep brain regions in vivo, with cellular subtype specificity and high spatiotemporal fidelity. Such optical neurotechnologies are enabling increasingly fine-scaled studies of neuronal circuits and reliably co-active groups of neurons, so-called ensembles. Neuronal ensembles are thought to constitute the basic functional building blocks of brain systems, potentially exhibiting collective computational properties. While the technical framework of in vivo optical imaging and quantification of neuronal activity follows certain widely held standards, analytical methods for study of neuronal co-activity and ensembles lack consensus and are highly varied across the field. Here we provide a comprehensive step-by-step overview of theoretical, experimental, and analytical considerations for the identification and quantification of neuronal ensemble dynamics in high-resolution in vivo optical imaging studies.

Task Engagement Improves Neural Discriminability in the Auditory Midbrain of the Marmoset Monkey

While task-dependent changes have been demonstrated in auditory cortex for a number of behavioral paradigms and mammalian species, less is known about how behavioral state can influence neural coding in the midbrain areas that provide auditory information to cortex. We measured single-unit activity in the inferior colliculus (IC) of common marmosets of both sexes while they performed a tone-in-noise detection task and during passive presentation of identical task stimuli. In contrast to our previous study in the ferret IC, task engagement had little effect on sound-evoked activity in central (lemniscal) IC of the marmoset. However, activity was significantly modulated in noncentral fields, where responses were selectively enhanced for the target tone relative to the distractor noise. This led to an increase in neural discriminability between target and distractors. The results confirm that task engagement can modulate sound coding in the auditory midbrain, and support a hypothesis that subcortical pathways can mediate highly trained auditory behaviors.SIGNIFICANCE STATEMENT While the cerebral cortex is widely viewed as playing an essential role in the learning and performance of complex auditory behaviors, relatively little attention has been paid to the role of brainstem and midbrain areas that process sound information before it reaches cortex. This study demonstrates that the auditory midbrain is also modulated during behavior. These modulations amplify task-relevant sensory information, a process that is traditionally attributed to cortex.

Sensory and Behavioral Components of Neocortical Signal Flow in Discrimination Tasks with Short-Term Memory

In the neocortex, each sensory modality engages distinct sensory areas that route information to association areas. Where signal flow converges for maintaining information in short-term memory and how behavior may influence signal routing remain open questions. Using wide-field calcium imaging, we compared cortex-wide neuronal activity in layer 2/3 for mice trained in auditory and tactile tasks with delayed response. In both tasks, mice were either active or passive during stimulus presentation, moving their body or sitting quietly. Irrespective of behavioral strategy, auditory and tactile stimulation activated distinct subdivisions of the posterior parietal cortex, anterior area A and rostrolateral area RL, which held stimulus-related information necessary for the respective tasks. In the delay period, in contrast, behavioral strategy rather than sensory modality determined short-term memory location, with activity converging frontomedially in active trials and posterolaterally in passive trials. Our results suggest behavior-dependent routing of sensory-driven cortical signals flow from modality-specific posterior parietal cortex (PPC) subdivisions to higher association areas.

Dynamics and Hierarchical Encoding of Non-compact Acoustic Categories in Auditory and Frontal Cortex

Categorical perception is a fundamental cognitive function enabling animals to flexibly assign sounds into behaviorally relevant categories. This study investigates the nature of acoustic category representations, their emergence in an ascending series of ferret auditory and frontal cortical fields, and the dynamics of this representation during passive listening to task-relevant stimuli and during active retrieval from memory while engaging in learned categorization tasks. Ferrets were trained on two auditory Go-NoGo categorization tasks to discriminate two non-compact sound categories (composed of tones or amplitude-modulated noise). Neuronal responses became progressively more categorical in higher cortical fields, especially during task performance. The dynamics of the categorical responses exhibited a cascading top-down modulation pattern that began earliest in the frontal cortex and subsequently flowed downstream to the secondary auditory cortex, followed by the primary auditory cortex. In a subpopulation of neurons, categorical responses persisted even during the passive listening condition, demonstrating memory for task categories and their enhanced categorical boundaries.

Categorical Biases in Human Occipitoparietal Cortex

Categorization allows organisms to generalize existing knowledge to novel stimuli and to discriminate between physically similar yet conceptually different stimuli. Humans, nonhuman primates, and rodents can readily learn arbitrary categories defined by low-level visual features, and learning distorts perceptual sensitivity for category-defining features such that differences between physically similar yet categorically distinct exemplars are enhanced, whereas differences between equally similar but categorically identical stimuli are reduced. We report a possible basis for these distortions in human occipitoparietal cortex. In three experiments, we used an inverted encoding model to recover population-level representations of stimuli from multivoxel and multielectrode patterns of human brain activity while human participants (both sexes) classified continuous stimulus sets into discrete groups. In each experiment, reconstructed representations of to-be-categorized stimuli were systematically biased toward the center of the appropriate category. These biases were largest for exemplars near a category boundary, predicted participants' overt category judgments, emerged shortly after stimulus onset, and could not be explained by mechanisms of response selection or motor preparation. Collectively, our findings suggest that category learning can influence processing at the earliest stages of cortical visual processing.SIGNIFICANCE STATEMENT Category learning enhances perceptual sensitivity for physically similar yet categorically different stimuli. We report a possible mechanism for these changes in human occipitoparietal cortex. In three experiments, we used an inverted encoding model to recover population-level representations of stimuli from multivariate patterns in occipitoparietal cortex while participants categorized sets of continuous stimuli into discrete groups. The recovered representations were systematically biased by category membership, with larger biases for exemplars adjacent to a category boundary. These results suggest that mechanisms of categorization shape information processing at the earliest stages of the visual system.

Understanding the circuit basis of cognitive functions using mouse models

Understanding how cognitive functions arise from computations occurring in the brain requires the ability to measure and perturb neural activity while the relevant circuits are engaged for specific cognitive processes. Rapid technical advances have led to the development of new approaches to transiently activate and suppress neuronal activity as well as to record simultaneously from hundreds to thousands of neurons across multiple brain regions during behavior. To realize the full potential of these approaches for understanding cognition, however, it is critical that behavioral conditions and stimuli are effectively designed to engage the relevant brain networks. Here, we highlight recent innovations that enable this combined approach. In particular, we focus on how to design behavioral experiments that leverage the ever-growing arsenal of technologies for controlling and measuring neural activity in order to understand cognitive functions.