Cortical ensembles selective for context

Hierarchical deviant processing in auditory cortex of awake mice

Detecting patterns, and noticing unexpected pattern changes, in the environment is a vital aspect of sensory processing. Adaptation and prediction error responses are two components of neural processing related to these tasks, and previous studies in the auditory system in rodents show that these two components are partially dissociable in terms of the topography and latency of neural responses to sensory deviants. However, many previous studies have focused on repetitions of single stimuli, such as pure tones, which have limited ecological validity. In this study, we tested whether the auditory cortical activity shows adaptation to repetition of more complex sound patterns (disyllabic pairs). Specifically, we compared neural responses to violations of sequences based on single stimulus probability only, against responses to more complex violations based on stimulus order. We employed an auditory oddball paradigm and monitored the auditory cortex (AC) activity of awake mice (N = 8) using wide-field calcium imaging. We found that cortical responses were sensitive both to single stimulus probabilities and to more global stimulus patterns, as mismatch signals were elicited following both substitution deviants and transposition deviants. Notably, higher order AC area elicited larger mismatch signaling to those deviants than primary AC, which suggests a hierarchical gradient of prediction error signaling in the auditory cortex. Such a hierarchical gradient was observed for late but not early peaks of calcium transients to deviants, suggesting that the late part of the deviant response may reflect prediction error signaling in response to more complex sensory pattern violations. SIGNIFICANCE STATEMENT: Detecting the unexpected change of patterns from the dynamic environment is vital for sensory processing, as it is essential to survival for humans and animals. Using wide-field calcium imaging, we investigated whether the auditory cortex of awake mice exhibits a hierarchical gradient of prediction error signaling and its sensitivity to violations of sequences based on stimulus features and stimulus order. We discovered the high-order auditory cortex elicited more significant mismatch signaling to those deviants than primary auditory cortex in substitution and transposition deviants. Calcium transients to deviants showed a hierarchical gradient for late but not for early peaks, indicating that the late part of the deviant response may reflect prediction error signaling in response to more complex sensory pattern violations.

Top-down prediction signals from the medial prefrontal cortex govern auditory cortex prediction errors

Under the predictive coding framework, the brain generates a model of the environment based on previous experiences. Incoming sensory information is compared to this model, such that if predictions do not match sensory inputs, a prediction error is generated. Predictions are passed top-down, and prediction errors emerge when bottom-up information does not match the predictions. Prediction errors occur sequentially in the primary auditory cortex (A1) and then the medial prefrontal cortex (mPFC). Here, we test the hypothesis that the mPFC sends predictions that contribute to the generation of prediction errors. We used optogenetics to block top-down signals from the mPFC while recording neuronal prediction errors in the A1 under the classical "oddball" paradigm. Blocking top-down signals reduces prediction errors in the A1 in response to rare sounds, while it does not affect responses to predictable or random sounds. Our results provide empirical evidence for top-down prediction signals from the mPFC that enhance A1 responses to unpredicted stimuli.

Slow cortical dynamics generate context processing and novelty detection

The cortex amplifies responses to novel stimuli while suppressing redundant ones. Novelty detection is necessary to efficiently process sensory information and build predictive models of the environment, and it is also altered in schizophrenia. To investigate the circuit mechanisms underlying novelty detection, we used an auditory "oddball" paradigm and two-photon calcium imaging to measure responses to simple and complex stimuli across mouse auditory cortex. Stimulus statistics and complexity generated specific responses across auditory areas. Neuronal ensembles reliably encoded auditory features and temporal context. Interestingly, stimulus-evoked population responses were particularly long lasting, reflecting stimulus history and affecting future responses. These slow cortical dynamics encoded stimulus temporal context and generated stronger responses to novel stimuli. Recurrent neural network models trained on the oddball task also exhibited slow network dynamics and recapitulated the biological data. We conclude that the slow dynamics of recurrent cortical networks underlie processing and novelty detection.

Context-Sensitive Conscious Interpretation and Layer-5 Pyramidal Neurons in Multistable Perception

INTRODUCTION

There appears to be a fundamental difference between the two ways of how an object becomes perceptually experienced. One occurs when preconscious object-specifying sensory data processing crosses a certain threshold so that sensory constituents of object depiction become consciously experienced. The other occurs when the already consciously experienced sensory features of the object become interpreted as belonging to a certain visual object category. Surprisingly, experimental facts about neural markers of conscious access gathered so far do not allow us to distinguish mechanisms responsible for these two varieties.

METHODS

A cortical multicompartment layer-5 pyramidal neuron-based generic processing model is presented in order to conceptualize a possible mechanistic solution for the explanatory cul-de-sac. To support the argument, a review of pertinent research is compiled as associated with data from studies where physically invariant perceptual stimuli have underwent alternative interpretation(s) by the brain.

RESULTS

Recent developments in the newly emerging field of cellular psycho(physio)logy are introduced, offering a hypothetical solution for distinguishing the mechanisms subserving sensory content experience and conscious interpretation.

CONCLUSION

The multicompartment single cell-based mechanistic approach to brain process correlates of conscious perception appears to have an added value beyond the traditional inter-areal connectivity-based theoretical stances.

Predictive coding: a more cognitive process than we thought?

In predictive coding (PC), higher-order brain areas generate predictions that are sent to lower-order sensory areas. Top-down predictions are compared with bottom-up sensory data, and mismatches evoke prediction errors. In PC, the prediction errors are encoded in layer 2/3 pyramidal neurons of sensory cortex that feed forward. The PC model has been tested with multiple recording modalities using the global-local oddball paradigm. Consistent with PC, neuroimaging studies reported prediction error responses in sensory and higher-order areas. However, recent studies of neuronal spiking suggest that genuine prediction errors emerge in prefrontal cortex (PFC). This implies that predictive processing is a more cognitive than sensory-based mechanism - an observation that challenges PC and better aligns with a framework we call predictive routing (PR).

Predictive acoustical processing in human cortical layers

In our dynamic environments, predictive processing is vital for auditory perception and its associated behaviors. Predictive coding formalizes inferential processes by implementing them as information exchange across cortical layers and areas. With laminar-specific blood oxygenation level dependent we measured responses to a cascading oddball paradigm, to ground predictive auditory processes on the mesoscopic human cortical architecture. We show that the violation of predictions are potentially hierarchically organized and associated with responses in superficial layers of the planum polare and middle layers of the lateral temporal cortex. Moreover, we relate the updating of the brain's internal model to changes in deep layers. Using a modeling approach, we derive putative changes in neural dynamics while accounting for draining effects. Our results support the role of temporal cortical architecture in the implementation of predictive coding and highlight the ability of laminar fMRI to investigate mesoscopic processes in a large extent of temporal areas.

Ensemble priming via competitive inhibition: local mechanisms of sensory context storage and deviance detection in the neocortical column

The process by which neocortical neurons and circuits amplify their response to an unexpected change in stimulus, often referred to as deviance detection (DD), has long been thought to be the product of specialized cell types and/or routing between mesoscopic brain areas. Here, we explore a different theory, whereby DD emerges from local network-level interactions within a neocortical column. We propose that deviance-driven neural dynamics can emerge through interactions between ensembles of neurons that have a fundamental inhibitory motif: competitive inhibition between reciprocally connected ensembles under modulation from feed-forward selective (dis)inhibition. Using this framework, we were able to simulate a variety of phenomena pertaining to the experimentally observed shifts in neural tuning across neurons, time, and stimulus history. Anchoring our approach in a variety of experimentally observed phenomena, we used computation modeling in two types of neural networks of vastly different levels of biophysical detail to test hypotheses on emergent dynamics and explore the robustness of underlying connectivity parameters. With a number of corollary predictions that can be tested in future in vivo studies, we show that ensemble priming via competitive inhibition under modulation from selective (dis)inhibition acts as a local mechanism for sensory context storage and that DD does not require specialized input from other brain areas-a novel theoretical paradigm that resolves previously confounding aspects of sensory encoding and predictive processing in the neocortex.

Multimodal Characterization of Cortical Neuron Response to Permanent Magnetic Field Induced Nanomagnetic Force Maps

Nanomagnetic forces deliver precise mechanical cues to biological systems through the remote pulling of magnetic nanoparticles under a permanent magnetic field. Cortical neurons respond to nanomagnetic forces with cytosolic calcium influx and event rate shifts. However, the underlying consequences of nanomagnetic force modulation on cortical neurons remain to be elucidated. Here, we integrate electrophysiological and optical recording modalities with nanomagnetic forces to characterize the in vitro functional response to mechanical cues. Neurons exposed to chitosan functionalized magnetic nanoparticles for 24 h and then exposed to magnetic fields capable of generating forces of 2-160 pN present elevated cytosolic calcium in neurons and a time-dynamic electrophysiological spike rate and magnitude response. Extracellular recordings with microelectrode arrays revealed a 2-8 pN force-specific increase in electrophysiological spiking with a trend in reduced activity following 2 min of continuous force exposure. Nanomagnetic forces in the 16-160 pN range produced increased electrophysiological activity and remained excited for up to 4 h under continuous stimulation before silencing. Furthermore, the neuronal response to nanomagnetic forces at 16-160 pN can be electrophysiologically mediated without calcium influx by altering the magnetic nanoparticle-neuron interactions. These results demonstrate that low pN nanomagnetic forces mediate neuronal function and suggest that magnetic nanoparticle interactions and force magnitudes can be harnessed to provoke different responses in cortical neurons.

Identification of movie encoding neurons enables movie recognition AI

Natural visual scenes are dominated by spatiotemporal image dynamics, but how the visual system integrates "movie" information over time is unclear. We characterized optic tectal neuronal receptive fields using sparse noise stimuli and reverse correlation analysis. Neurons recognized movies of ~200-600 ms durations with defined start and stop stimuli. Movie durations from start to stop responses were tuned by sensory experience though a hierarchical algorithm. Neurons encoded families of image sequences following trigonometric functions. Spike sequence and information flow suggest that repetitive circuit motifs underlie movie detection. Principles of frog topographic retinotectal plasticity and cortical simple cells are employed in machine learning networks for static image recognition, suggesting that discoveries of principles of movie encoding in the brain, such as how image sequences and duration are encoded, may benefit movie recognition technology. We built and trained a machine learning network that mimicked neural principles of visual system movie encoders. The network, named MovieNet, outperformed current machine learning image recognition networks in classifying natural movie scenes, while reducing data size and steps to complete the classification task. This study reveals how movie sequences and time are encoded in the brain and demonstrates that brain-based movie processing principles enable efficient machine learning.

Local and long-range input balance: A framework for investigating frontal cognitive circuit maturation in health and disease

Frontal cortical circuits undergo prolonged maturation across childhood and adolescence; however, it remains unknown what specific changes are occurring at the circuit level to establish adult cognitive function. With the recent advent of circuit dissection techniques, it is now feasible to examine circuit-specific changes in connectivity, activity, and function in animal models. Here, we propose that the balance of local and long-range inputs onto frontal cognitive circuits is an understudied metric of circuit maturation. This review highlights research on a frontal-sensory attention circuit that undergoes refinement of local/long-range connectivity, regulated by circuit activity and neuromodulatory signaling, and evaluates how this process may occur generally in the frontal cortex to support adult cognitive behavior. Notably, this balance can be bidirectionally disrupted through various mechanisms relevant to psychiatric disorders. Pharmacological or environmental interventions to normalize or reset the local and long-range balance could hold great therapeutic promise to prevent or rescue cognitive deficits.

A common computational and neural anomaly across mouse models of autism

Computational psychiatry has suggested that humans within the autism spectrum disorder (ASD) inflexibly update their expectations (i.e., Bayesian priors). Here, we leveraged high-yield rodent psychophysics (n = 75 mice), extensive behavioral modeling (including principled and heuristics), and (near) brain-wide single cell extracellular recordings (over 53k units in 150 brain areas) to ask (1) whether mice with different genetic perturbations associated with ASD show this same computational anomaly, and if so, (2) what neurophysiological features are shared across genotypes in subserving this deficit. We demonstrate that mice harboring mutations in Fmr1 , Cntnap2 , and Shank3B show a blunted update of priors during decision-making. Neurally, the differentiating factor between animals flexibly and inflexibly updating their priors was a shift in the weighting of prior encoding from sensory to frontal cortices. Further, in mouse models of ASD frontal areas showed a preponderance of units coding for deviations from the animals' long-run prior, and sensory responses did not differentiate between expected and unexpected observations. These findings demonstrate that distinct genetic instantiations of ASD may yield common neurophysiological and behavioral phenotypes.

Spontaneously emerging internal models of visual sequences combine abstract and event-specific information in the prefrontal cortex

When exposed to sensory sequences, do macaque monkeys spontaneously form abstract internal models that generalize to novel experiences? Here, we show that neuronal populations in macaque ventrolateral prefrontal cortex jointly encode visual sequences by separate codes for the specific pictures presented and for their abstract sequential structure. We recorded prefrontal neurons while macaque monkeys passively viewed visual sequences and sequence mismatches in the local-global paradigm. Even without any overt task or response requirements, prefrontal populations spontaneously form representations of sequence structure, serial order, and image identity within distinct but superimposed neuronal subspaces. Representations of sequence structure rapidly update following single exposure to a mismatch sequence, while distinct populations represent mismatches for sequences of different complexity. Finally, those representations generalize across sequences following the same repetition structure but comprising different images. These results suggest that prefrontal populations spontaneously encode rich internal models of visual sequences reflecting both content-specific and abstract information.

Auditory processing control by the medial prefrontal cortex: A review of the rodent functional organisation

Afferent inputs from the cochlea transmit auditory information to the central nervous system, where information is processed and passed up the hierarchy, ending in the auditory cortex. Through these brain pathways, spectral and temporal features of sounds are processed and sent to the cortex for perception. There are also many mechanisms in place for modulation of these inputs, with a major source of modulation being based in the medial prefrontal cortex (mPFC). Neurons of the rodent mPFC receive input from the auditory cortex and other regions such as thalamus, hippocampus and basal forebrain, allowing them to encode high-order information about sounds such as context, predictability and valence. The mPFC then exerts control over auditory perception via top-down modulation of the central auditory pathway, altering perception of and responses to sounds. The result is a higher-order control of auditory processing that produces such characteristics as deviance detection, attention, avoidance and fear conditioning. This review summarises connections between mPFC and the primary auditory pathway, responses of mPFC neurons to auditory stimuli, how mPFC outputs shape the perception of sounds, and how changes to these systems during hearing loss and tinnitus may contribute to these conditions.

Behavior-relevant top-down cross-modal predictions in mouse neocortex

Animals adapt to a constantly changing world by predicting their environment and the consequences of their actions. The predictive coding hypothesis proposes that the brain generates predictions and continuously compares them with sensory inputs to guide behavior. However, how the brain reconciles conflicting top-down predictions and bottom-up sensory information remains unclear. To address this question, we simultaneously imaged neuronal populations in the mouse somatosensory barrel cortex and posterior parietal cortex during an auditory-cued texture discrimination task. In mice that had learned the task with fixed tone-texture matching, the presentation of mismatched pairing induced conflicts between tone-based texture predictions and actual texture inputs. When decisions were based on the predicted rather than the actual texture, top-down information flow was dominant and texture representations in both areas were modified, whereas dominant bottom-up information flow led to correct representations and behavioral choice. Our findings provide evidence for hierarchical predictive coding in the mouse neocortex.

Predictive neural computations in the cerebellum contribute to motor planning and faster behavioral responses in larval zebrafish

The ability to predict the future based on past experience lies at the core of the brain's ability to adapt behavior. However, the neural mechanisms that participate in generating and updating predictions are not clearly understood. Further, the evolutionary antecedents and the prevalence of predictive processing among vertebrates are even less explored. Here, we show evidence of predictive processing via the involvement of cerebellar circuits in larval zebrafish. We presented stereotyped optic flow stimuli to larval zebrafish to evoke swims and discovered that lesioning the cerebellum abolished prediction-dependent modulation of swim latency. When expectations of optic flow direction did not match with reality, error signals arrive at Purkinje cells via the olivary climbing fibers, whereas granule cells and Purkinje cells encode signals of expectation. Strong neural representations of expectation correlate with faster swim responses and vice versa. In sum, our results show evidence for predictive processing in nonmammalian vertebrates with the involvement of cerebellum, an evolutionarily conserved brain structure.

Modern Methods for Unraveling Cell- and Circuit-Level Mechanisms of Neurophysiological Biomarkers in Psychiatry

Methods for studying the mammalian brain in vivo have advanced dramatically in the past two decades. State-of-the-art optical and electrophysiological techniques allow direct recordings of the functional dynamics of thousands of neurons across distributed brain circuits with single-cell resolution. With transgenic tools, specific neuron types, pathways, and/or neurotransmitters can be targeted in user-determined brain areas for precise measurement and manipulation. In this chapter, we catalog these advancements. We emphasize that the impact of this methodological revolution on neuropsychiatry remains uncertain. This stems from the fact that these tools remain mostly limited to research in mice. And while translational paradigms are needed, recapitulations of human psychiatric disease states (e.g., schizophrenia) in animal models are inherently challenging to validate and may have limited utility in heterogeneous disease populations. Here we focus on an alternative strategy aimed at the study of neurophysiological biomarkers-the subject of this volume-translated to animal models, where precision neuroscience tools can be applied to provide molecular, cellular, and circuit-level insights and novel therapeutic targets. We summarize several examples of this approach throughout the chapter and emphasize the importance of careful experimental design and choice of dependent measures.

Change detection in the primate auditory cortex through feedback of prediction error signals

Although cortical feedback signals are essential for modulating feedforward processing, no feedback error signal across hierarchical cortical areas has been reported. Here, we observed such a signal in the auditory cortex of awake common marmoset during an oddball paradigm to induce auditory duration mismatch negativity. Prediction errors to a deviant tone presentation were generated as offset calcium responses of layer 2/3 neurons in the rostral parabelt (RPB) of higher-order auditory cortex, while responses to non-deviant tones were strongly suppressed. Within several hundred milliseconds, the error signals propagated broadly into layer 1 of the primary auditory cortex (A1) and accumulated locally on top of incoming auditory signals. Blockade of RPB activity prevented deviance detection in A1. Optogenetic activation of RPB following tone presentation nonlinearly enhanced A1 tone response. Thus, the feedback error signal is critical for automatic detection of unpredicted stimuli in physiological auditory processing and may serve as backpropagation-like learning.

Top-down input modulates visual context processing through an interneuron-specific circuit

Visual stimuli that deviate from the current context elicit augmented responses in the primary visual cortex (V1). These heightened responses, known as "deviance detection," require local inhibition in the V1 and top-down input from the anterior cingulate area (ACa). Here, we investigated the mechanisms by which the ACa and V1 interact to support deviance detection. Local field potential recordings in mice during an oddball paradigm showed that ACa-V1 synchrony peaks in the theta/alpha band (≈10 Hz). Two-photon imaging in the V1 revealed that mainly pyramidal neurons exhibited deviance detection, while contextually redundant stimuli increased vasoactive intestinal peptide (VIP)-positive interneuron (VIP) activity and decreased somatostatin-positive interneuron (SST) activity. Optogenetic drive of ACa-V1 inputs at 10 Hz activated V1-VIPs but inhibited V1-SSTs, mirroring the dynamics present during the oddball paradigm. Chemogenetic inhibition of V1-VIPs disrupted Aca-V1 synchrony and deviance detection in the V1. These results outline temporal and interneuron-specific mechanisms of top-down modulation that support visual context processing.

How 'visual' is the visual cortex? The interactions between the visual cortex and other sensory, motivational and motor systems as enabling factors for visual perception

The definition of the visual cortex is primarily based on the evidence that lesions of this area impair visual perception. However, this does not exclude that the visual cortex may process more information than of retinal origin alone, or that other brain structures contribute to vision. Indeed, research across the past decades has shown that non-visual information, such as neural activity related to reward expectation and value, locomotion, working memory and other sensory modalities, can modulate primary visual cortical responses to retinal inputs. Nevertheless, the function of this non-visual information is poorly understood. Here we review recent evidence, coming primarily from studies in rodents, arguing that non-visual and motor effects in visual cortex play a role in visual processing itself, for instance disentangling direct auditory effects on visual cortex from effects of sound-evoked orofacial movement. These findings are placed in a broader framework casting vision in terms of predictive processing under control of frontal, reward- and motor-related systems. In contrast to the prevalent notion that vision is exclusively constructed by the visual cortical system, we propose that visual percepts are generated by a larger network-the extended visual system-spanning other sensory cortices, supramodal areas and frontal systems. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

Robust multisensory deviance detection in the mouse parietal associative area

Context modulates how information is processed in the mammalian brain. For example, brain responses are amplified to contextually unusual stimuli. This phenomenon, known as "deviance detection,"1,2 is well documented in early, primary sensory cortex, where large responses are generated to simple stimuli that deviate from their context in low-order properties, such as line orientation, size, or pitch.2,3,4,5 However, the extent to which neural deviance detection manifests (1) in broader cortical networks and (2) to simple versus complex stimuli, which deviate only in their higher-order, multisensory properties, is not known. Consistent with a predictive processing framework,6,7 we hypothesized that deviance detection manifests in a hierarchical manner across cortical networks,8,9 emerging later and further downstream when stimulus deviance is complex. To test this, we examined brain responses of awake mice to simple unisensory deviants (e.g., visual line gratings, deviating from context in their orientation alone) versus complex multisensory deviants (i.e., audiovisual pairs, deviating from context only in their audiovisual pairing but not visual or auditory content alone). We find that mouse parietal associative area-a higher cortical region-displays robust multisensory deviance detection. In contrast, primary visual cortex exhibits strong unisensory visual deviance detection but weaker multisensory deviance detection. These results suggest that deviance detection signals in the cortex may be conceptualized as "prediction errors," which are primarily fed forward-or downstream-in cortical networks.6,7.

Long- and short-term history effects in a spiking network model of statistical learning

The statistical structure of the environment is often important when making decisions. There are multiple theories of how the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability distributions. In other words, the network spends more time in states which encode high-probability stimuli. Starting from the neural assembly, increasingly thought of to be the building block for computation in the brain, we focus on how arbitrary prior knowledge about the external world can both be learned and spontaneously recollected. We present a model based upon learning the inverse of the cumulative distribution function. Learning is entirely unsupervised using biophysical neurons and biologically plausible learning rules. We show how this prior knowledge can then be accessed to compute expectations and signal surprise in downstream networks. Sensory history effects emerge from the model as a consequence of ongoing learning.

Nuances in intensity deviant asymmetric responses as a biomarker for tinnitus

We attempted to replicate a potential tinnitus biomarker in humans based on the Sensory Precision Integrative Model of Tinnitus called the Intensity Mismatch Asymmetry. A few advances on the design were also included, including tighter matching of participants for gender, and a control stimulus frequency of 1 kHz to investigate whether any differences between control and tinnitus groups are specific to the tinnitus frequency or domain-general. The expectation was that there would be asymmetry in the MMN responses between tinnitus and control groups at the tinnitus frequency, but not at the control frequency, where the tinnitus group would have larger, more negative responses to upward deviants than downward deviants, and the control group would have the opposite pattern or lack of a deviant direction effect. However, no significant group differences were found. There was a striking difference in response amplitude to control frequency stimuli compared to tinnitus frequency stimuli, which could be an intrinsic quality of responses to these frequencies or could reflect high frequency hearing loss in the sample. Additionally, the upward deviants elicited stronger MMN responses in both groups at tinnitus frequency, but not at the control frequency. Factors contributing to these discrepant results at the tinnitus frequency could include hyperacusis, attention, and wider contextual effects of other frequencies used in the experiment (i.e. the control frequency in other blocks).

Spatiotemporal dynamics across visual cortical laminae support a predictive coding framework for interpreting mismatch responses

Context modulates neocortical processing of sensory data. Unexpected visual stimuli elicit large responses in primary visual cortex (V1)-a phenomenon known as deviance detection (DD) at the neural level, or "mismatch negativity" (MMN) when measured with EEG. It remains unclear how visual DD/MMN signals emerge across cortical layers, in temporal relation to the onset of deviant stimuli, and with respect to brain oscillations. Here we employed a visual "oddball" sequence-a classic paradigm for studying aberrant DD/MMN in neuropsychiatric populations-and recorded local field potentials in V1 of awake mice with 16-channel multielectrode arrays. Multiunit activity and current source density profiles showed that although basic adaptation to redundant stimuli was present early (50 ms) in layer 4 responses, DD emerged later (150-230 ms) in supragranular layers (L2/3). This DD signal coincided with increased delta/theta (2-7 Hz) and high-gamma (70-80 Hz) oscillations in L2/3 and decreased beta oscillations (26-36 Hz) in L1. These results clarify the neocortical dynamics elicited during an oddball paradigm at a microcircuit level. They are consistent with a predictive coding framework, which posits that predictive suppression is present in cortical feed-back circuits, which synapse in L1, whereas "prediction errors" engage cortical feed-forward processing streams, which emanate from L2/3.

The Computational and Neural Bases of Context-Dependent Learning

Flexible behavior requires the creation, updating, and expression of memories to depend on context. While the neural underpinnings of each of these processes have been intensively studied, recent advances in computational modeling revealed a key challenge in context-dependent learning that had been largely ignored previously: Under naturalistic conditions, context is typically uncertain, necessitating contextual inference. We review a theoretical approach to formalizing context-dependent learning in the face of contextual uncertainty and the core computations it requires. We show how this approach begins to organize a large body of disparate experimental observations, from multiple levels of brain organization (including circuits, systems, and behavior) and multiple brain regions (most prominently the prefrontal cortex, the hippocampus, and motor cortices), into a coherent framework. We argue that contextual inference may also be key to understanding continual learning in the brain. This theory-driven perspective places contextual inference as a core component of learning.

Spatiotemporal dynamics across visual cortical laminae support a predictive coding framework for interpreting mismatch responses

Context modulates neocortical processing of sensory data. Unexpected visual stimuli elicit large responses in primary visual cortex (V1) -- a phenomenon known as deviance detection (DD) at the neural level, or "mismatch negativity" (MMN) when measured with EEG. It remains unclear how visual DD/MMN signals emerge across cortical layers, in temporal relation to the onset of deviant stimuli, and with respect to brain oscillations. Here we employed a visual "oddball" sequence - a classic paradigm for studying aberrant DD/MMN in neuropsychiatric populations - and recorded local field potentials in V1 of awake mice with 16-channel multielectrode arrays. Multiunit activity and current source density profiles showed that while basic adaptation to redundant stimuli was present early (50ms) in layer 4 responses, DD emerged later (150-230ms) in supragranular layers (L2/3). This DD signal coincided with increased delta/theta (2-7Hz) and high-gamma (70-80Hz) oscillations in L2/3 and decreased beta oscillations (26-36hz) in L1. These results clarify the neocortical dynamics elicited during an oddball paradigm at a microcircuit level. They are consistent with a predictive coding framework, which posits that predictive suppression is present in cortical feed-back circuits, which synapse in L1, while "prediction errors" engage cortical feed-forward processing streams, which emanate from L2/3.

Multimodal mismatch responses in associative but not primary visual cortex support hierarchical predictive coding in cortical networks

A key function of the mammalian neocortex is to process sensory data in the context of current and past stimuli. Primary sensory cortices, such as V1, respond weakly to stimuli that typical in their context but strongly to novel stimuli, an effect known as "deviance detection". How deviance detection occurs in associative cortical regions that are downstream of V1 is not well-understood. Here we investigated parietal associative area (PTLp) responses to auditory, visual, and audio-visual mismatches with two-photon calcium imaging and local field potential recordings. We employed basic unisensory auditory and visual oddball paradigms as well as a novel multisensory oddball paradigm, involving typical parings (VaAc or VbAd) presented at p=.88 with rare "deviant" pairings (e.g. VaAd or VbAc) presented at p=.12. We found that PTLp displayed robust deviance detection responses to auditory-visual mismatches, both in individual neurons and in population theta and gamma-band oscillations. In contrast, V1 neurons displayed deviance detection only to visual deviants in a unisensory context, but not to auditory or auditory-visual mismatches. Taken together, these results accord with a predictive processing framework for cortical responses, wherein modality specific prediction errors (i.e. deviance detection responses) are computed in functionally specified cortical areas and feed-forward to update higher brain regions.

A frontosensory circuit for visual context processing is synchronous in the theta/alpha band

Visual processing is strongly influenced by context. Stimuli that deviate from contextual regularities elicit augmented responses in primary visual cortex (V1). These heightened responses, known as "deviance detection," require both inhibition local to V1 and top-down modulation from higher areas of cortex. Here we investigated the spatiotemporal mechanisms by which these circuit elements interact to support deviance detection. Local field potential recordings in mice in anterior cingulate area (ACa) and V1 during a visual oddball paradigm showed that interregional synchrony peaks in the theta/alpha band (6-12 Hz). Two-photon imaging in V1 revealed that mainly pyramidal neurons exhibited deviance detection, while vasointestinal peptide-positive interneurons (VIPs) increased activity and somatostatin-positive interneurons (SSTs) decreased activity (adapted) to redundant stimuli (prior to deviants). Optogenetic drive of ACa-V1 inputs at 6-12 Hz activated V1-VIPs but inhibited V1-SSTs, mirroring the dynamics present during the oddball paradigm. Chemogenetic inhibition of VIP interneurons disrupted ACa-V1 synchrony and deviance detection responses in V1. These results outline spatiotemporal and interneuron-specific mechanisms of top-down modulation that support visual context processing.

Hierarchical Deviant Processing in Auditory Cortex of Awake Mice

Detecting patterns, and noticing unexpected pattern changes, in the environment is a vital aspect of sensory processing. Adaptation and prediction error responses are two components of neural processing related to these tasks, and previous studies in the auditory system in rodents show that these two components are partially dissociable in terms of the topography and latency of neural responses to sensory deviants. However, many previous studies have focused on repetitions of single stimuli, such as pure tones, which have limited ecological validity. In this study, we tested whether the auditory cortical activity shows adaptation to repetition of more complex sound patterns (bisyllabic pairs). Specifically, we compared neural responses to violations of sequences based on single stimulus probability only, against responses to more complex violations based on stimulus order. We employed an auditory oddball paradigm and monitored the auditory cortex (ACtx) activity of awake mice (N=8) using wide-field calcium imaging. We found that cortical responses were sensitive both to single stimulus probabilities and to more global stimulus patterns, as mismatch signals were elicited following both substitution deviants and transposition deviants. Notably, A2 area elicited larger mismatch signaling to those deviants than primary ACtx (A1), which suggests a hierarchical gradient of prediction error signaling in the auditory cortex. Such a hierarchical gradient was observed for late but not early peaks of calcium transients to deviants, suggesting that the late part of the deviant response may reflect prediction error signaling in response to more complex sensory pattern violations.

Where is the error? Hierarchical predictive coding through dendritic error computation

Top-down feedback in cortex is critical for guiding sensory processing, which has prominently been formalized in the theory of hierarchical predictive coding (hPC). However, experimental evidence for error units, which are central to the theory, is inconclusive and it remains unclear how hPC can be implemented with spiking neurons. To address this, we connect hPC to existing work on efficient coding in balanced networks with lateral inhibition and predictive computation at apical dendrites. Together, this work points to an efficient implementation of hPC with spiking neurons, where prediction errors are computed not in separate units, but locally in dendritic compartments. We then discuss the correspondence of this model to experimentally observed connectivity patterns, plasticity, and dynamics in cortex.

Learning enhances encoding of time and temporal surprise in mouse primary sensory cortex

Primary sensory cortex has long been believed to play a straightforward role in the initial processing of sensory information. Yet, the superficial layers of cortex overall are sparsely active, even during sensory stimulation; additionally, cortical activity is influenced by other modalities, task context, reward, and behavioral state. Our study demonstrates that reinforcement learning dramatically alters representations among longitudinally imaged neurons in superficial layers of mouse primary somatosensory cortex. Learning an object detection task recruits previously unresponsive neurons, enlarging the neuronal population sensitive to touch and behavioral choice. Cortical responses decrease upon repeated stimulus presentation outside of the behavioral task. Moreover, training improves population encoding of the passage of time, and unexpected deviations in trial timing elicit even stronger responses than touches do. In conclusion, the superficial layers of sensory cortex exhibit a high degree of learning-dependent plasticity and are strongly modulated by non-sensory but behaviorally-relevant features, such as timing and surprise.

Activity in the superficial layers of the sensory cortex is believed to be largely driven by incoming sensory stimuli. Here the authors demonstrate how learning changes neural responses to sensations according to both behavioral relevance and timing, suggesting a high degree of non-sensory modulation.

Novel stimuli evoke excess activity in the mouse primary visual cortex

Rapid detection and processing of stimulus novelty are key elements of adaptive behavior. Predictive coding theories postulate that novel stimuli should be encoded differently from familiar stimuli. Here, we show that the majority of neurons in layer 2/3 of the mouse primary visual cortex exhibit a significant excess response to novel visual stimuli. The distinction between novel and familiar images developed rapidly, requiring only a few repeated presentations. We show that this phenomenon can be described by a model of cascading adaptation. This ubiquitous mechanism makes it likely that similar computations could be carried out in many brain areas.

To explore how neural circuits represent novel versus familiar inputs, we presented mice with repeated sets of images with novel images sparsely substituted. Using two-photon calcium imaging to record from layer 2/3 neurons in the mouse primary visual cortex, we found that novel images evoked excess activity in the majority of neurons. This novelty response rapidly emerged, arising with a time constant of 2.6 ± 0.9 s. When a new image set was repeatedly presented, a majority of neurons had similarly elevated activity for the first few presentations, which decayed to steady state with a time constant of 1.4 ± 0.4 s. When we increased the number of images in the set, the novelty response’s amplitude decreased, defining a capacity to store ∼15 familiar images under our conditions. These results could be explained quantitatively using an adaptive subunit model in which presynaptic neurons have individual tuning and gain control. This result shows that local neural circuits can create different representations for novel versus familiar inputs using generic, widely available mechanisms.

Corticothalamic Pathways in Auditory Processing: Recent Advances and Insights From Other Sensory Systems

The corticothalamic (CT) pathways emanate from either Layer 5 (L5) or 6 (L6) of the neocortex and largely outnumber the ascending, thalamocortical pathways. The CT pathways provide the anatomical foundations for an intricate, bidirectional communication between thalamus and cortex. They act as dynamic circuits of information transfer with the ability to modulate or even drive the response properties of target neurons at each synaptic node of the circuit. L6 CT feedback pathways enable the cortex to shape the nature of its driving inputs, by directly modulating the sensory message arriving at the thalamus. L5 CT pathways can drive the postsynaptic neurons and initiate a transthalamic corticocortical circuit by which cortical areas communicate with each other. For this reason, L5 CT pathways place the thalamus at the heart of information transfer through the cortical hierarchy. Recent evidence goes even further to suggest that the thalamus via CT pathways regulates functional connectivity within and across cortical regions, and might be engaged in cognition, behavior, and perceptual inference. As descending pathways that enable reciprocal and context-dependent communication between thalamus and cortex, we venture that CT projections are particularly interesting in the context of hierarchical perceptual inference formulations such as those contemplated in predictive processing schemes, which so far heavily rely on cortical implementations. We discuss recent proposals suggesting that the thalamus, and particularly higher order thalamus via transthalamic pathways, could coordinate and contextualize hierarchical inference in cortical hierarchies. We will explore these ideas with a focus on the auditory system.

The posterior auditory field is the chief generator of prediction error signals in the auditory cortex

The auditory cortex (AC) encompasses distinct fields subserving partly different aspects of sound processing. One essential function of the AC is the detection of unpredicted sounds, as revealed by differential neural activity to predictable and unpredictable sounds. According to the predictive coding framework, this effect can be explained by repetition suppression and/or prediction error signaling. The present study investigates functional specialization of the rat AC fields in repetition suppression and prediction error by combining a tone frequency oddball paradigm (involving high-probable standard and low-probable deviant tones) with two different control sequences (many-standards and cascade). Tones in the control sequences were comparable to deviant events with respect to neural adaptation but were not violating a regularity. Therefore, a difference in the neural activity between deviant and control tones indicates a prediction error effect, whereas a difference between control and standard tones indicates a repetition suppression effect. Single-unit recordings revealed by far the largest prediction error effects for the posterior auditory field, while the primary auditory cortex, the anterior auditory field, the ventral auditory field, and the suprarhinal auditory field were dominated by repetition suppression effects. Statistically significant repetition suppression effects occurred in all AC fields, whereas prediction error effects were less robust in the primary auditory cortex and the anterior auditory field. Results indicate that the non-lemniscal, posterior auditory field is more engaged in context-dependent processing underlying deviance-detection than the other AC fields, which are more sensitive to stimulus-dependent effects underlying differential degrees of neural adaptation.

Preliminary Findings on Visual Event-Related Potential P3 in Asymptomatic Patients with Cerebral Small Vessel Disease

BACKGROUND

Cerebral small vessel disease is the primary cause of cognitive impairment. Therefore, early recognition is of great significance. Some studies have shown that asymptomatic cerebral small vessel disease (aCSVD) patients have abnormal neurocognitive function, but this is not readily apparent at the initial stage. The objective of this paper was to assess visual spatial attention by event-related potential (ERP) examination and to analyze the relationship between ERP data and clinical characteristics in patients with aCSVD.

METHODS

We selected 25 aCSVD patients and enrolled 23 age-matched normal subjects as the control group. We measured the latency and amplitude of original/corresponding differential ERP components using the modified visual oddball paradigm, which included a standard stimulus, target stimulus, and new stimulus. Additionally, we selected aberrant ERP components to study the correlations between the ERP data and clinical characteristics of the patients with aCSVD.

RESULTS

We found not only lower amplitude but also significantly longer P3 latency in the aCSVD patients. The above results were further verified by analyzing the different components (target minus standard and novel minus standard) of P3. Furthermore, abnormal ERPs in the aCSVD patients were closely related to the changes observed with imaging.

CONCLUSION

It was demonstrated that the speed and capability of processing visual spatial information was impaired in aCSVD patients compared with healthy controls. Thus, ERP examination could detect the presence of attentional deficits and might become a rapid and sensitive method for the early diagnosis of aCSVD. However, its availability needs further investigation.