Crossmodal integration improves sensory detection thresholds in the ferret

Cortical cellular encoding of thermotactile integration

Recent evidence suggests that primary sensory cortical regions play a role in the integration of information from multiple sensory modalities. How primary cortical neurons integrate different sources of sensory information is unclear, partly because non-primary sensory input to a cortical sensory region is often weak or modulatory. To address this question, we take advantage of the robust representation of thermal (cooling) and tactile stimuli in mouse forelimb primary somatosensory cortex (fS1). Using a thermotactile detection task, we show that the perception of threshold-level cool or tactile information is enhanced when they are presented simultaneously, compared with presentation alone. To investigate the cortical cellular correlates of thermotactile integration, we performed in vivo extracellular recordings from fS1 in awake resting and anesthetized mice during unimodal and bimodal stimulation of the forepaw. Unimodal stimulation evoked thermal- or tactile- specific excitatory and inhibitory responses of fS1 neurons. The most prominent features of combined thermotactile stimulation are the recruitment of unimodally silent fS1 neurons, non-linear integration features, and response dynamics that favor longer response durations with additional spikes. Together, we identify quantitative and qualitative changes in cortical encoding that may underlie the improvement in perception of thermotactile surfaces during haptic exploration.

Neural Correlates of Multisensory Detection Behavior: Comparison of Primary and Higher-Order Visual Cortex

We act upon stimuli in our surrounding environment by gathering the multisensory information they convey and by integrating this information to decide on a behavioral action. We hypothesized that the anterolateral secondary visual cortex (area AL) of the mouse brain may serve as a hub for sensorimotor transformation of audiovisual information. We imaged neuronal activity in primary visual cortex (V1) and AL of the mouse during a detection task using visual, auditory, and audiovisual stimuli. We found that AL neurons were more sensitive to weak uni- and multisensory stimuli compared to V1. Depending on contrast, different subsets of AL and V1 neurons showed cross-modal modulation of visual responses. During audiovisual stimulation, AL neurons showed stronger differentiation of behaviorally reported versus unreported stimuli compared to V1, whereas V1 showed this distinction during unisensory visual stimulation. Thus, neural population activity in area AL correlates more closely with multisensory detection behavior than V1.

Differential Rapid Plasticity in Auditory and Visual Responses in the Primarily Multisensory Orbitofrontal Cortex

Given the connectivity of orbitofrontal cortex (OFC) with the sensory areas and areas involved in goal execution, it is likely that OFC, along with its function in reward processing, also has a role to play in perception-based multisensory decision-making. To understand mechanisms involved in multisensory decision-making, it is important to first know the encoding of different sensory stimuli in single neurons of the mouse OFC. Ruling out effects of behavioral state, memory, and others, we studied the anesthetized mouse OFC responses to auditory, visual, and audiovisual/multisensory stimuli, multisensory associations and sensory-driven input organization to the OFC. Almost all, OFC single neurons were found to be multisensory in nature, with sublinear to supralinear integration of the component unisensory stimuli. With a novel multisensory oddball stimulus set, we show that the OFC receives both unisensory as well as multisensory inputs, further corroborated by retrograde tracers showing labeling in secondary auditory and visual cortices, which we find to also have similar multisensory integration and responses. With long audiovisual pairing/association, we show rapid plasticity in OFC single neurons, with a strong visual bias, leading to a strong depression of auditory responses and effective enhancement of visual responses. Such rapid multisensory association driven plasticity is absent in the auditory and visual cortices, suggesting its emergence in the OFC. Based on the above results, we propose a hypothetical local circuit model in the OFC that integrates auditory and visual information which participates in computing stimulus value in dynamic multisensory environments.

Cross-Talk of Low-Level Sensory and High-Level Cognitive Processing: Development, Mechanisms, and Relevance for Cross-Modal Abilities of the Brain

The emergence of cross-modal learning capabilities requires the interaction of neural areas accounting for sensory and cognitive processing. Convergence of multiple sensory inputs is observed in low-level sensory cortices including primary somatosensory (S1), visual (V1), and auditory cortex (A1), as well as in high-level areas such as prefrontal cortex (PFC). Evidence shows that local neural activity and functional connectivity between sensory cortices participate in cross-modal processing. However, little is known about the functional interplay between neural areas underlying sensory and cognitive processing required for cross-modal learning capabilities across life. Here we review our current knowledge on the interdependence of low- and high-level cortices for the emergence of cross-modal processing in rodents. First, we summarize the mechanisms underlying the integration of multiple senses and how cross-modal processing in primary sensory cortices might be modified by top-down modulation of the PFC. Second, we examine the critical factors and developmental mechanisms that account for the interaction between neuronal networks involved in sensory and cognitive processing. Finally, we discuss the applicability and relevance of cross-modal processing for brain-inspired intelligent robotics. An in-depth understanding of the factors and mechanisms controlling cross-modal processing might inspire the refinement of robotic systems by better mimicking neural computations.

Visual Motion and Form Integration in the Behaving Ferret

Ferrets have become a standard animal model for the development of early visual stages. Less is known about higher-level vision in ferrets, both during development and in adulthood. Here, as a step towards establishing higher-level vision research in ferrets, we used behavioral experiments to test the motion and form integration capacity of adult ferrets. Motion integration was assessed by training ferrets to discriminate random dot kinematograms (RDK) based on their direction. Task difficulty was varied systematically by changing RDK coherence levels, which allowed the measurement of motion integration thresholds. Form integration was measured analogously by training ferrets to discriminate linear Glass patterns of varying coherence levels based on their orientation. In all experiments, ferrets proved to be good psychophysical subjects that performed tasks reliably. Crucially, the behavioral data showed clear evidence of perceptual motion and form integration. In the monkey, motion and form integration are usually associated with processes occurring in higher-level visual areas. In a second set of experiments, we therefore tested whether PSS, a higher-level motion area in the ferret, could similarly support motion integration behavior in this species. To this end, we measured responses of PSS neurons to RDK of different coherence levels. Indeed, neurometric functions for PSS were in good agreement with the behaviorally derived psychometric functions. In conclusion, our experiments demonstrate that ferrets are well suited for higher-level vision research.

Audiovisual Integration Enhances Stimulus Detection Performance in Mice

The detection of objects in the external world improves when humans and animals integrate object features of multiple sensory modalities. Behavioral and neuronal mechanisms underlying multisensory stimulus detection are poorly understood, mainly because they have not been investigated with suitable behavioral paradigms. Such behavioral paradigms should (i) elicit a robust multisensory gain, (ii) incorporate systematic calibration of stimulus amplitude to the sensory capacities of the individual subject, (iii) yield a high trial count, and (iv) be easily compatible with a large variety of neurophysiological recording techniques. We developed an audiovisual stimulus detection task for head-fixed mice which meets all of these critical behavioral constraints. Behavioral data obtained with this task indicated a robust increase in detection performance of multisensory stimuli compared with unisensory cues, which was maximal when both stimulus constituents were presented at threshold intensity. The multisensory behavioral effect was associated with a change in the perceptual performance which consisted of two components. First, the visual and auditory perceptual systems increased their sensitivity meaning that low intensity stimuli were more often detected. Second, enhanced acuity enabled the systems to better classify whether there was a stimulus or not. Fitting our data to signal detection models revealed that the multisensory gain was more likely to be achieved by integration of sensory signals rather than by stimulus redundancy or competition. This validated behavioral paradigm can be exploited to reliably investigate the neuronal correlates of multisensory stimulus detection at the level of single neurons, microcircuits, and larger perceptual systems.

Development, organization and plasticity of auditory circuits: Lessons from a cherished colleague

Ray Guillery was a neuroscientist known primarily for his ground-breaking studies on the development of the visual pathways and subsequently on the nature of thalamocortical processing loops. The legacy of his work, however, extends well beyond the visual system. Thanks to Ray Guillery's pioneering anatomical studies, the ferret has become a widely used animal model for investigating the development and plasticity of sensory processing. This includes our own work on the auditory system, where experiments in ferrets have revealed the role of sensory experience during development in shaping the neural circuits responsible for sound localization, as well as the capacity of the mature brain to adapt to changes in inputs resulting from hearing loss. Our research has also built on Ray Guillery's ideas about the possible functions of the massive descending projections that link sensory areas of the cerebral cortex to the thalamus and other subcortical targets, by demonstrating a role for corticothalamic feedback in the perception of complex sounds and for corticollicular projection neurons in learning to accommodate altered auditory spatial cues. Finally, his insights into the organization and functions of transthalamic corticocortical connections have inspired a raft of research, including by our own laboratory, which has attempted to identify how information flows through the thalamus.

Asymmetric Direct Reciprocal Connections Between Primary Visual and Somatosensory Cortices of the Mouse

Several studies show direct connections between primary sensory cortices involved in multisensory integration. The purpose of this study is to understand the microcircuitry of the reciprocal connections between visual and somatosensory cortices. The laminar distribution of retrogradely labeled cell bodies in V1 and in the somatosensory cortex both in (S1BF) and outside (S1) the barrel field was studied to provide layer indices in order to determine whether the connections are of feedforward, feedback or lateral type. Single axons were reconstructed and the size of their swellings was stereologically sampled. The negative layer indices in S1 and S1BF and the layer index near zero in V1 indicate that the connection from S1BF to V1 is of feedback type while the opposite is of lateral type. The greater incidence of larger axonal swellings in the projection from V1 to S1BF strongly suggests that S1BF receives a stronger driver input from V1 and that S1BF inputs to V1 have a predominant modulatory influence.

Humans treat unreliable filled-in percepts as more real than veridical ones

Humans often evaluate sensory signals according to their reliability for optimal decision-making. However, how do we evaluate percepts generated in the absence of direct input that are, therefore, completely unreliable? Here, we utilize the phenomenon of filling-in occurring at the physiological blind-spots to compare partially inferred and veridical percepts. Subjects chose between stimuli that elicit filling-in, and perceptually equivalent ones presented outside the blind-spots, looking for a Gabor stimulus without a small orthogonal inset. In ambiguous conditions, when the stimuli were physically identical and the inset was absent in both, subjects behaved opposite to optimal, preferring the blind-spot stimulus as the better example of a collinear stimulus, even though no relevant veridical information was available. Thus, a percept that is partially inferred is paradoxically considered more reliable than a percept based on external input. In other words: Humans treat filled-in inferred percepts as more real than veridical ones.

Optimal nonlinear cue integration for sound localization

Integration of multiple sensory cues can improve performance in detection and estimation tasks. There is an open theoretical question of the conditions under which linear or nonlinear cue combination is Bayes-optimal. We demonstrate that a neural population decoded by a population vector requires nonlinear cue combination to approximate Bayesian inference. Specifically, if cues are conditionally independent, multiplicative cue combination is optimal for the population vector. The model was tested on neural and behavioral responses in the barn owl's sound localization system where space-specific neurons owe their selectivity to multiplicative tuning to sound localization cues interaural phase (IPD) and level (ILD) differences. We found that IPD and ILD cues are approximately conditionally independent. As a result, the multiplicative combination selectivity to IPD and ILD of midbrain space-specific neurons permits a population vector to perform Bayesian cue combination. We further show that this model describes the owl's localization behavior in azimuth and elevation. This work provides theoretical justification and experimental evidence supporting the optimality of nonlinear cue combination.

Behavioural benefits of multisensory processing in ferrets

Enhanced detection and discrimination, along with faster reaction times, are the most typical behavioural manifestations of the brain's capacity to integrate multisensory signals arising from the same object. In this study, we examined whether multisensory behavioural gains are observable across different components of the localization response that are potentially under the command of distinct brain regions. We measured the ability of ferrets to localize unisensory (auditory or visual) and spatiotemporally coincident auditory-visual stimuli of different durations that were presented from one of seven locations spanning the frontal hemifield. During the localization task, we recorded the head movements made following stimulus presentation, as a metric for assessing the initial orienting response of the ferrets, as well as the subsequent choice of which target location to approach to receive a reward. Head-orienting responses to auditory-visual stimuli were more accurate and faster than those made to visual but not auditory targets, suggesting that these movements were guided principally by sound alone. In contrast, approach-to-target localization responses were more accurate and faster to spatially congruent auditory-visual stimuli throughout the frontal hemifield than to either visual or auditory stimuli alone. Race model inequality analysis of head-orienting reaction times and approach-to-target response times indicates that different processes, probability summation and neural integration, respectively, are likely to be responsible for the effects of multisensory stimulation on these two measures of localization behaviour.