Sensory Input Streams Research Articles

EVIDENCE FOR AUDITORY STIMULUS-SPECIFIC ADAPTATION BUT NOT DEVIANCE DETECTION IN LARVAL ZEBRAFISH BRAINS.

Animals receive a constant stream of sensory input, and detecting changes in this sensory landscape is critical to their survival. One signature of change detection in humans is the auditory mismatch negativity (MMN), a neural response to unexpected stimuli that deviate from a predictable sequence. This process requires the auditory system to adapt to specific repeated stimuli while remaining sensitive to novel input (stimulus-specific adaptation). MMN was originally described in humans, and equivalent responses have been found in other mammals and birds, but it is not known to what extent this deviance detection circuitry is evolutionarily conserved. Here we present the first evidence for stimulus-specific adaptation in the brain of a teleost fish, using whole-brain calcium imaging of larval zebrafish at single-neuron resolution with selective plane illumination microscopy. We found frequency-specific responses across the brain with variable response amplitudes for frequencies of the same volume, and created a loudness curve to model this effect. We presented an auditory 'oddball' stimulus in an otherwise predictable train of pure tone stimuli, and did not find a population of neurons with specific responses to deviant tones that were not otherwise explained by stimulus-specific adaptation. Further, we observed no deviance responses to an unexpected omission of a sound in a repetitive sequence of white noise bursts. These findings extend the known scope of auditory adaptation and deviance responses across the evolutionary tree, and lay groundwork for future studies to describe the circuitry underlying auditory adaptation at the level of individual neurons.

The role of the dorsal anterior cingulate cortex in obsessive-compulsive disorder

Despite extensive research efforts, the pathophysiology of obsessive-compulsive disorder (OCD) is still largely unknown. The dorsal anterior cingulate cortex (dACC) plays an important role in cognitive control and is therefore hypothesized to contribute to the pathogenesis of OCD. In this review, we aim to gain a wider understanding of the specific functions of the dACC and its role in the pathophysiology of OCD. The dACC is part of the cortico-basal ganglia-thalamo-cortical loop, where it forms connections between sensory input streams, cognitive and affective processing regions, and structures that regulate behaviour. This position facilitates a broad function for the dACC in multiple domains, which center on goal-directed behaviour and reward-based learning. When presented with a certain threatening stimulus, the dACC instructs downstream structures to select actions to respond to this particular stimulus, based on previous experiences We hypothesize that hyperactivity of the dACC may impair goal-directed behaviour in OCD patients which in turn may lead to obsessive-compulsive symptoms by creating an over-reliance on threatening stimuli and inadequate selection of neutralizing actions. The working mechanisms of cognitive behavioural therapy, serotonergic medication, repetitive transcranial magnetic stimulation and deep brain stimulation in OCD may be in part explained by the normalization of the activity of the dACC within the cortico-basal ganglia-thalamo-cortical (CBGTC) loop.

EEG mismatch responses in a multimodal roving stimulus paradigm provide evidence for probabilistic inference across audition, somatosensation, and vision.

The human brain is constantly subjected to a multimodal stream of probabilistic sensory inputs. Electroencephalography (EEG) signatures, such as the mismatch negativity (MMN) and the P3, can give valuable insight into neuronal probabilistic inference. Although reported for different modalities, mismatch responses have largely been studied in isolation, with a strong focus on the auditory MMN. To investigate the extent to which early and late mismatch responses across modalities represent comparable signatures of uni- and cross-modal probabilistic inference in the hierarchically structured cortex, we recorded EEG from 32 participants undergoing a novel tri-modal roving stimulus paradigm. The employed sequences consisted of high and low intensity stimuli in the auditory, somatosensory and visual modalities and were governed by unimodal transition probabilities and cross-modal conditional dependencies. We found modality specific signatures of MMN (~100-200 ms) in all three modalities, which were source localized to the respective sensory cortices and shared right lateralized prefrontal sources. Additionally, we identified a cross-modal signature of mismatch processing in the P3a time range (~300-350 ms), for which a common network with frontal dominance was found. Across modalities, the mismatch responses showed highly comparable parametric effects of stimulus train length, which were driven by standard and deviant response modulations in opposite directions. Strikingly, P3a responses across modalities were increased for mispredicted stimuli with low cross-modal conditional probability, suggesting sensitivity to multimodal (global) predictive sequence properties. Finally, model comparisons indicated that the observed single trial dynamics were best captured by Bayesian learning models tracking unimodal stimulus transitions as well as cross-modal conditional dependencies.

Rethinking statistical learning as a continuous dynamic stochastic process, from the motor systems perspective.

The brain integrates streams of sensory input and builds accurate predictions, while arriving at stable percepts under disparate time scales. This stochastic process bears different unfolding dynamics for different people, yet statistical learning (SL) currently averages out, as noise, individual fluctuations in data streams registered from the brain as the person learns. We here adopt a new analytical approach that instead of averaging out fluctuations in continuous electroencephalographic (EEG)-based data streams, takes these gross data as the important signals. Our new approach reassesses how individuals dynamically learn predictive information in stable and unstable environments. We find neural correlates for two types of learners in a visuomotor task: narrow-variance learners, who retain explicit knowledge of the regularity embedded in the stimuli. They seem to use an error-correction strategy steadily present in both stable and unstable environments. This strategy can be captured by current optimization-based computational frameworks. In contrast, broad-variance learners emerge only in the unstable environment. Local analyses of the moment-by-moment fluctuations, naïve to the overall outcome, reveal an initial period of memoryless learning, well characterized by a continuous gamma process starting out exponentially distributed whereby all future events are equally probable, with high signal (mean) to noise (variance) ratio. The empirically derived continuous Gamma process smoothly converges to predictive Gaussian signatures comparable to those observed for the error-corrective mode that is captured by current optimization-driven computational models. We coin this initially seemingly purposeless stage exploratory. Globally, we examine a posteriori the fluctuations in distributions' shapes over the empirically estimated stochastic signatures. We then confirm that the exploratory mode of those learners, free of expectation, random and memoryless, but with high signal, precedes the acquisition of the error-correction mode boasting smooth transition from exponential to symmetric distributions' shapes. This early naïve phase of the learning process has been overlooked by current models driven by expected, predictive information and error-based learning. Our work demonstrates that (statistical) learning is a highly dynamic and stochastic process, unfolding at different time scales, and evolving distinct learning strategies on demand.

Affective context and its uncertainty drive momentary affective experience.

Affect fluctuates in a moment-to-moment fashion, reflecting the continuous relationship between the individual and the environment. Despite substantial research, there remain important open questions regarding how a stream of sensory input is dynamically represented in experienced affect. Here, approaching affect as a temporally dependent process, we show that momentary affect is shaped by a combination of the affective impact of stimuli (i.e., visual images for the current studies) and previously experienced affect. We also found that this temporal dependency is influenced by uncertainty of the affective context. Participants in each trial viewed sequentially presented images and subsequently reported their affective experience, which was modeled based on images' normative affect ratings and participants' previously reported affect. Study 1 showed that self-reported valence and arousal in a given trial is partly shaped by the affective impact of the given images and previously experienced affect. In Study 2, we manipulated context uncertainty by controlling occurrence probabilities for normatively pleasant and unpleasant images in separate blocks. Increasing context uncertainty (i.e., random occurrence of pleasant and unpleasant images) was associated with increased negative affect. In addition, the relative contribution of the most recent image to experienced pleasantness increased with increasing context uncertainty. Taken together, these findings provide clear behavioral evidence that momentary affect is a temporally dependent and continuous process, which reflects the affective impact of recent input variables and the previous internal state, and that this process is sensitive to the affective context and its uncertainty. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Frequency-based neural discrimination in fast periodic visual stimulation

Humans capitalize on statistical cues to discriminate fundamental units of information within complex streams of sensory input. We sought neural evidence for this phenomenon by combining fast periodic visual stimulation (FPVS) and EEG recordings. Skilled readers were exposed to sequences of linguistic items with decreasing familiarity, presented at a fast rate and periodically interleaved with oddballs. Crucially, each sequence comprised stimuli of the same category, and the only distinction between base and oddball items was the frequency of occurrence of individual tokens within a stream. Frequency-domain analyses revealed robust neural responses at the oddball presentation rate in all conditions, reflecting the discrimination between two locally-emerged groups of items purely informed by token frequency. Results provide evidence for a fundamental frequency-tuned mechanism that operates under high temporal constraints and could underpin category bootstrapping. Concurrently, they showcase the potential of FPVS for providing a direct neural measure of implicit statistical learning.

Purkinje cells translate subjective salience into readiness to act and choice performance

The brain selectively allocates attention from a continuous stream of sensory input. This process is typically attributed to computations in distinct regions of the forebrain and midbrain. Here, we explore whether cerebellar Purkinje cells encode information about the selection of sensory inputs and could thereby contribute to non-motor forms of learning. We show that complex spikes of individual Purkinje cells change the sensory modality they encode to reflect changes in the perceived salience of sensory input. Comparisons with mouse models deficient in cerebellar plasticity suggest that changes in complex spike activity instruct potentiation of Purkinje cells simple spike firing, which is required for efficient learning. Our findings suggest that during learning, climbing fibers do not directly guide motor output, but rather contribute to a general readiness to act via changes in simple spike activity, thereby bridging the sequence from non-motor to motor functions.

Sensory Adaptation in the Whisker-Mediated Tactile System: Physiology, Theory, and Function.

In the natural environment, organisms are constantly exposed to a continuous stream of sensory input. The dynamics of sensory input changes with organism's behaviour and environmental context. The contextual variations may induce >100-fold change in the parameters of the stimulation that an animal experiences. Thus, it is vital for the organism to adapt to the new diet of stimulation. The response properties of neurons, in turn, dynamically adjust to the prevailing properties of sensory stimulation, a process known as “neuronal adaptation.” Neuronal adaptation is a ubiquitous phenomenon across all sensory modalities and occurs at different stages of processing from periphery to cortex. In spite of the wealth of research on contextual modulation and neuronal adaptation in visual and auditory systems, the neuronal and computational basis of sensory adaptation in somatosensory system is less understood. Here, we summarise the recent finding and views about the neuronal adaptation in the rodent whisker-mediated tactile system and further summarise the functional effect of neuronal adaptation on the response dynamics and encoding efficiency of neurons at single cell and population levels along the whisker-mediated touch system in rodents. Based on direct and indirect pieces of evidence presented here, we suggest sensory adaptation provides context-dependent functional mechanisms for noise reduction in sensory processing, salience processing and deviant stimulus detection, shift between integration and coincidence detection, band-pass frequency filtering, adjusting neuronal receptive fields, enhancing neural coding and improving discriminability around adapting stimuli, energy conservation, and disambiguating encoding of principal features of tactile stimuli.

Making sense of raw input

How should a machine intelligence perform unsupervised structure discovery over streams of sensory input? One approach to this problem is to cast it as an apperception task[1]. Here, the task is to construct an explicit interpretable theory that both explains the sensory sequence and also satisfies a set of unity conditions, designed to ensure that the constituents of the theory are connected in a relational structure.However, the original formulation of the apperception task had one fundamental limitation: it assumed the raw sensory input had already been parsed using a set of discrete categories, so that all the system had to do was receive this already-digested symbolic input, and make sense of it. But what if we don't have access to pre-parsed input? What if our sensory sequence is raw unprocessed information?The central contribution of this paper is a neuro-symbolic framework for distilling interpretable theories out of streams of raw, unprocessed sensory experience. First, we extend the definition of the apperception task to include ambiguous (but still symbolic) input: sequences of sets of disjunctions. Next, we use a neural network to map raw sensory input to disjunctive input. Our binary neural network is encoded as a logic program, so the weights of the network and the rules of the theory can be solved jointly as a single SAT problem. This way, we are able to jointly learn how to perceive (mapping raw sensory information to concepts) and apperceive (combining concepts into declarative rules).

Trimodal processing of complex stimuli in inferior parietal cortex is modality-independent

In humans, multisensory mechanisms facilitate object processing through integration of sensory signals that match in their temporal and spatial occurrence as well as their meaning. The generalizability of such integration processes across different sensory modalities is, however, to date not well understood. As such, it remains unknown whether there are cerebral areas that process object-related signals independently of the specific senses from which they arise, and whether these areas show different response profiles depending on the number of sensory channels that carry information.To address these questions, we presented participants with dynamic stimuli that simultaneously emitted object-related sensory information via one, two, or three channels (sight, sound, smell) in the MR scanner. By comparing neural activation patterns between various integration processes differing in type and number of stimulated senses, we showed that the left inferior frontal gyrus and areas within the left inferior parietal cortex were engaged independently of the number and type of sensory input streams. Activation in these areas was enhanced during bimodal stimulation, compared to the sum of unimodal activations, and increased even further during trimodal stimulation.Taken together, our findings demonstrate that activation of the inferior parietal cortex during processing and integration of meaningful multisensory stimuli is both modality-independent and modulated by the number of available sensory modalities. This suggests that the processing demand placed on the parietal cortex increases with the number of sensory input streams carrying meaningful information, likely due to the increasing complexity of such stimuli.

Evolutionary and homeostatic changes in morphology of visual dendrites of Mauthner cells in Astyanax blind cavefish.

Mauthner cells are the largest neurons in the hindbrain of teleost fish and most amphibians. Each cell has two major dendrites thought to receive segregated streams of sensory input: the lateral dendrite receives mechanosensory input while the ventral dendrite receives visual input. These inputs, which mediate escape responses to sudden stimuli, may be modulated by the availability of sensory information to the animal. To understand the impact of the absence of visual information on the morphologies of Mauthner cells during developmental and evolutionary time scales, we examined the teleost Astyanax mexicanus. This species of tetra is found in two morphs: a seeing surface fish and a blind cavefish. We compared the structure of Mauthner cells in surface fish raised under daily light conditions, in surface fish raised in constant darkness, and in two independent lineages of cave populations. The length of ventral dendrites of Mauthner cells in dark-raised surface fish larvae were longer and more branched, while in both cave morphs the ventral dendrites were smaller or absent. The absence of visual input in surface fish with normal eye development leads to a homeostatic increase in dendrite size, whereas over evolution, the absence of light led to the loss of eyes and a reduction in dendrite size.

Tea With Milk? A Hierarchical Generative Framework of Sequential Event Comprehension.

To make sense of the world around us, we must be able to segment a continual stream of sensory inputs into discrete events. In this review, I propose that in order to comprehend events, we engage hierarchical generative models that "reverse engineer" the intentions of other agents as they produce sequential action in real time. By generating probabilistic predictions for upcoming events, generative models ensure that we are able to keep up with the rapid pace at which perceptual inputs unfold. By tracking our certainty about other agents' goals and the magnitude of prediction errors at multiple temporal scales, generative models enable us to detect event boundaries by inferring when a goal has changed. Moreover, by adapting flexibly to the broader dynamics of the environment and our own comprehension goals, generative models allow us to optimally allocate limited resources. Finally, I argue that we use generative models not only to comprehend events but also to produce events (carry out goal-relevant sequential action) and to continually learn about new events from our surroundings. Taken together, this hierarchical generative framework provides new insights into how the human brain processes events so effortlessly while highlighting the fundamental links between event comprehension, production, and learning.

Treefrogs exploit temporal coherence to form perceptual objects of communication signals.

For many animals, navigating their environment requires an ability to organize continuous streams of sensory input into discrete 'perceptual objects' that correspond to physical entities in visual and auditory scenes. The human visual and auditory systems follow several Gestalt laws of perceptual organization to bind constituent features into coherent perceptual objects. A largely unexplored question is whether nonhuman animals follow similar Gestalt laws in perceiving behaviourally relevant stimuli, such as communication signals. We used females of Cope's grey treefrog (Hyla chrysoscelis) to test the hypothesis that temporal coherence-a powerful Gestalt principle in human auditory scene analysis-promotes perceptual binding in forming auditory objects of species-typical vocalizations. According to the principle of temporal coherence, sound elements that start and stop at the same time or that modulate coherently over time are likely to become bound together into the same auditory object. We found that the natural temporal coherence between two spectral components of advertisement calls promotes their perceptual binding into auditory objects of advertisement calls. Our findings confirm the broad ecological validity of temporal coherence as a Gestalt law of auditory perceptual organization guiding the formation of biologically relevant perceptual objects in animal behaviour.

The Effect of Alpha tACS on the Temporal Resolution of Visual Perception.

We experience the world around us as a smooth and continuous flow. However, there is growing evidence that the stream of sensory inputs is not elaborated in an analog way but is instead organized in discrete or quasi-discrete temporal processing windows. These discrete windows are suggested to depend on rhythmic neural activity in the alpha (and theta) frequency bands, which in turn reflect changes in neural activity within, and coupling between, cortical areas. In the present study, we investigated a possible causal link between oscillatory brain activity in the alpha range (8–12 Hz) and the temporal resolution of visual perception, which determines whether sequential stimuli are perceived as distinct entities or combined into a single representation. To this aim, we employed a two-flash fusion task while participants received focal transcranial alternating current stimulation (tACS) in extra-striate visual regions including V5/MT of the right hemisphere. Our findings show that 10-Hz tACS, as opposed to a placebo (sham tACS), reduces the temporal resolution of perception, inducing participants to integrate the two stimuli into a unique percept more often. This pattern was observed only in the contralateral visual hemifield, providing further support for a specific effect of alpha tACS. The present findings corroborate the idea of a causal link between temporal windows of integration/segregation and oscillatory alpha activity in V5/MT and extra-striate visual regions. They also stimulate future research on possible ways to shape the temporal resolution of human vision in an individualized manner.

Attention strengthens across-trial pre-stimulus phase coherence in visual cortex, enhancing stimulus processing

Attention selectively routes the most behaviorally relevant information from the stream of sensory inputs through the hierarchy of cortical areas. Previous studies have shown that visual attention depends on the phase of oscillatory brain activities. These studies mainly focused on the stimulus presentation period, rather than the pre-stimulus period. Here, we hypothesize that selective attention controls the phase of oscillatory neural activities to efficiently process relevant information. We document an attentional modulation of pre-stimulus inter-trial phase coherence (a measure of deviation between instantaneous phases of trials) of low frequency local field potentials (LFP) in visual area MT of macaque monkeys. Our data reveal that phase coherence increases following a spatial cue deploying attention towards the receptive field of the recorded neural population. We further show that the attentional enhancement of phase coherence is positively correlated with the modulation of the stimulus-induced firing rate, and importantly, a higher phase coherence is associated with a faster behavioral response. These results suggest a functional utilization of intrinsic neural oscillatory activities for an enhanced processing of upcoming stimuli.

Structured sequence processing and combinatorial binding: neurobiologically and computationally informed hypotheses.

Understanding how the brain forms representations of structured information distributed in time is a challenging endeavour for the neuroscientific community, requiring computationally and neurobiologically informed approaches. The neural mechanisms for segmenting continuous streams of sensory input and establishing representations of dependencies remain largely unknown, as do the transformations and computations occurring between the brain regions involved in these aspects of sequence processing. We propose a blueprint for a neurobiologically informed and informing computational model of sequence processing (entitled: Vector-symbolic Sequencing of Binding INstantiating Dependencies, or VS-BIND). This model is designed to support the transformation of serially ordered elements in sensory sequences into structured representations of bound dependencies, readily operates on multiple timescales, and encodes or decodes sequences with respect to chunked items wherever dependencies occur in time. The model integrates established vector symbolic additive and conjunctive binding operators with neurobiologically plausible oscillatory dynamics, and is compatible with modern spiking neural network simulation methods. We show that the model is capable of simulating previous findings from structured sequence processing tasks that engage fronto-temporal regions, specifying mechanistic roles for regions such as prefrontal areas 44/45 and the frontal operculum during interactions with sensory representations in temporal cortex. Finally, we are able to make predictions based on the configuration of the model alone that underscore the importance of serial position information, which requires input from time-sensitive cells, known to reside in the hippocampus and dorsolateral prefrontal cortex.This article is part of the theme issue ‘Towards mechanistic models of meaning composition’.

Measurement of cognitive dynamics during video watching through event-related potentials (ERPs) and oscillations (EROs).

Event-related potentials (ERPs) and oscillations (EROs) are reliable measures of cognition, but they require time-locked electroencephalographic (EEG) data to repetitive triggers that are not available in continuous sensory input streams. However, such real-life-like stimulation by videos or virtual-reality environments may serve as powerful means of creating specific cognitive or affective states and help to investigate dysfunctions in psychiatric and neurological disorders more efficiently. This study aims to develop a method to generate ERPs and EROs during watching videos. Repeated luminance changes were introduced on short video segments, while EEGs of 10 subjects were recorded. The ERP/EROs time-locked to these distortions were analyzed in time and time-frequency domains and tested for their cognitive significance through a long term memory test that included frames from the watched videos. For each subject, ERPs and EROs corresponding to video segments of recalled images with 25% shortest and 25% longest reaction times were compared. ERPs produced by transient luminance changes displayed statistically significant fluctuations both in time and time-frequency domains. Statistical analyses showed that a positivity around 450ms, a negativity around 500ms and delta and theta EROs correlated with memory performance. Few studies mixed video streams with simultaneous ERP/ERO experiments with discrete task-relevant or passively presented auditory or somatosensory stimuli, while the present study, by obtaining ERPs and EROs to task-irrelevant events in the same sensory modality as that of the continuous sensory input, produces minimal interference with the main focus of attention on the video stream.

Low-Frequency Spike-Field Coherence Is a Fingerprint of Periodicity Coding in the Auditory Cortex

SummaryThe extraction of temporal information from sensory input streams is of paramount importance in the auditory system. In this study, amplitude-modulated sounds were used as stimuli to drive auditory cortex (AC) neurons of the bat species Carollia perspicillata, to assess the interactions between cortical spikes and local-field potentials (LFPs) for the processing of temporal acoustic cues. We observed that neurons in the AC capable of eliciting synchronized spiking to periodic acoustic envelopes were significantly more coherent to theta- and alpha-band LFPs than their non-synchronized counterparts. These differences occurred independently of the modulation rate tested and could not be explained by power or phase modulations of the field potentials. We argue that the coupling between neuronal spiking and the phase of low-frequency LFPs might be important for orchestrating the coding of temporal acoustic structures in the AC.

The transformation of multi-sensory experiences into memories during sleep.

Our everyday lives present us with a continuous stream of multi-modal sensory inputs. While most of this information is soon forgotten, sensory information associated with salient experiences can leave long-lasting memories in our minds. Extensive human and animal research has established that the hippocampus is critically involved in this process of memory formation and consolidation. However, the underlying mechanistic details are still only partially understood. Specifically, the hippocampus has often been suggested to encode information during experience, temporarily store it, and gradually transfer this information to the cortex during sleep. In rodents, ample evidence has supported this notion in the context of spatial memory, yet whether this process adequately describes the consolidation of multi-sensory experiences into memories is unclear. Here, focusing on rodent studies, I examine how multi-sensory experiences are consolidated into long term memories by hippocampal and cortical circuits during sleep. I propose that in contrast to the classical model of memory consolidation, the cortex is a "fast learner" that has a rapid and instructive role in shaping hippocampal-dependent memory consolidation. The proposed model may offer mechanistic insight into memory biasing using sensory cues during sleep.

Spiking Activity in the Auditory Cortex Is Synchronized to Beta-Band Local Field Potential Oscillations

The extraction of temporal information from sensory input streams is of paramount importance in the auditory system. In this study, amplitude modulated sounds were used as stimuli to drive auditory cortex neurons of the bat species Carollia perspicillata, in order to assess the interactions between cortical spikes and local-field potentials (LFPs) for the processing of temporal acoustic cues. We show that the occurrence of cortical spikes is synchronized to beta-band (12-30 Hz) LFP oscillations, and that the latter is independent of the temporal modulation of the sound that triggers the spikes. Spike activity can track amplitude modulation rates only if they are lower than 30 Hz, while LFPs can be entrained by acoustic streams modulated at frequencies higher than 100 Hz. The presented data indicate that coherent beta-band oscillations in the auditory cortex are intrinsically related with stimulus-locked phasic neuronal discharges.