Causal inference in multisensory perception

Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.     

Neurally Encoding Time for Olfactory Navigation

Accurately encoding time is one of the fundamental challenges faced by the nervous system in mediating behavior. We recently reported that some animals have a specialized population of rhythmically active neurons in their olfactory organs with the potential to peripherally encode temporal information about odor encounters. If these neurons do indeed encode the timing of odor arrivals, it should be possible to demonstrate that this capacity has some functional significance. Here we show how this sensory input can profoundly influence an animal’s ability to locate the source of odor cues in realistic turbulent environments—a common task faced by species that rely on olfactory cues for navigation. Using detailed data from a turbulent plume created in the laboratory, we reconstruct the spatiotemporal behavior of a real odor field. We use recurrence theory to show that information about position relative to the source of the odor plume is embedded in the timing between odor pulses. Then, using a parameterized computational model, we show how an animal can use populations of rhythmically active neurons to capture and encode this temporal information in real time, and use it to efficiently navigate to an odor source. Our results demonstrate that the capacity to accurately encode temporal information about sensory cues may be crucial for efficient olfactory navigation. More generally, our results suggest a mechanism for extracting and encoding temporal information from the sensory environment that could have broad utility for neural information processing.     

An emergent mechanism of selective visual attention in Drosophila

Due to the limited computational capacity of visual systems and the limited capacity to perform several mental operations at once, animals only select a small proportion of the stimuli available at any one time. It remains to be clarified how this process is related to the spatio-temporal dynamics of cell assemblies in the brain. By employing the flight simulator, selective visual attention behavior is studied in Drosophila. It has been found that for the visual objects presented, the tethered fruitflies display various attention patterns. Specifically, the learning memory mutants dunce and amnesiac possess attention patterns totally different from that of the wild-type fly. To explain these results from the viewpoint of dynamic cell assemblies, a neural network has been developed in which a possible link between the activity of cell assemblies, encoding of sensory information, and selective attention in Drosophila is proposed. Received: 3 November 1998 / Accepted in revised form: 1 July 1999     

A Simple Model of Optimal Population Coding for Sensory Systems

A fundamental task of a sensory system is to infer information about the environment. It has long been suggested that an important goal of the first stage of this process is to encode the raw sensory signal efficiently by reducing its redundancy in the neural representation. Some redundancy, however, would be expected because it can provide robustness to noise inherent in the system. Encoding the raw sensory signal itself is also problematic, because it contains distortion and noise. The optimal solution would be constrained further by limited biological resources. Here, we analyze a simple theoretical model that incorporates these key aspects of sensory coding, and apply it to conditions in the retina. The model specifies the optimal way to incorporate redundancy in a population of noisy neurons, while also optimally compensating for sensory distortion and noise. Importantly, it allows an arbitrary input-to-output cell ratio between sensory units (photoreceptors) and encoding units (retinal ganglion cells), providing predictions of retinal codes at different eccentricities. Compared to earlier models based on redundancy reduction, the proposed model conveys more information about the original signal. Interestingly, redundancy reduction can be near-optimal when the number of encoding units is limited, such as in the peripheral retina. We show that there exist multiple, equally-optimal solutions whose receptive field structure and organization vary significantly. Among these, the one which maximizes the spatial locality of the computation, but not the sparsity of either synaptic weights or neural responses, is consistent with known basic properties of retinal receptive fields. The model further predicts that receptive field structure changes less with light adaptation at higher input-to-output cell ratios, such as in the periphery.     

Candidate neural locus for sex differences in reproductive decisions

Sexual selection and signal detection theories predict that females should be selective in their responses to mating signals in mate choice, while the response of males to signals in male competition should be less selective. The neural processes underlying this behavioural sex difference remain obscure. Differences in behavioural selectivity could result from differences in how sensitive sensory systems are to mating signals, distinct thresholds in motor areas regulating behaviour, or sex differences in selectivity at a gateway relaying sensory information to motor systems. We tested these hypotheses in frogs using the expression of egr-1 to quantify the neural responses of each sex to mating signals. We found that egr-1 expression in a midbrain auditory region was elevated in males in response to both conspecific and heterospecific calls, whereas in females, egr-1 induction occurred only in response to conspecific signals. This differential neural selectivity mirrored the sex differences in behavioural responsiveness to these stimuli. By contrast, egr-1 expression in lower brainstem auditory centres was not different in males and females. Our results support a model in which sex differences in behavioural selectivity arise from sex differences in the neural selectivity in midbrain areas relaying sensory information to the forebrain.     

A physical characterization of biological information and communication system model of ecosystems

What is information for living organisms? An answer to this question is given on a physical basis and a contrast between genetic information and sensory information is stressed with a relation to information theory. A simple model of an environment of living organisms is investigated on the basis of communication systems model proposed by the author and a cost of information transmission is taken into consideration through capacity cost theory. It is shown that channel capacity of information theory can be interpreted as an environment, and furthermore that a large diversity of genetic messages needs a large capacity of the environment. In addition, a definition of life in terms of information is proposed and a unified view on life processes is suggested.     

The role of neuronal synchronization in selective attention

Attention selectively enhances the influence of neuronal responses conveying information about relevant sensory attributes. Accumulating evidence suggests that this selective neuronal modulation relies on rhythmic synchronization at local and long-range spatial scales: attention selectively synchronizes the rhythmic responses of those neurons that are tuned to the spatial and featural attributes of the attended sensory input. The strength of synchronization is thereby functionally related to perceptual accuracy and behavioural efficiency. Complementing this synchronization at a local level, attention has recently been demonstrated to regulate which locally synchronized neuronal groups phase-synchronize their rhythmic activity across long-range connections. These results point to a general computational role for selective synchronization in dynamically controlling which neurons communicate information about sensory inputs effectively.     

自然图像效率编码

效率编码理论认为经过漫长历史进化,大脑感知系统有效地适应了自然环境.自然图像统计规律计算建模对视觉信息处理机理的理解大有裨益.本文简要回顾近期视觉系统对自然图像效率编码的最新进展.     

Gamma oscillations in human primary somatosensory cortex reflect pain perception

Successful behavior requires selection and preferred processing of relevant sensory information. The cortical representation of relevant sensory information has been related to neuronal oscillations in the gamma frequency band. Pain is of invariably high behavioral relevance and, thus, nociceptive stimuli receive preferred processing. Here, by using magnetoencephalography, we show that selective nociceptive stimuli induce gamma oscillations between 60 and 95 Hz in primary somatosensory cortex. Amplitudes of pain-induced gamma oscillations vary with objective stimulus intensity and subjective pain intensity. However, around pain threshold, perceived stimuli yielded stronger gamma oscillations than unperceived stimuli of equal stimulus intensity. These results show that pain induces gamma oscillations in primary somatosensory cortex that are particularly related to the subjective perception of pain. Our findings support the hypothesis that gamma oscillations are related to the internal representation of behaviorally relevant stimuli that should receive preferred processing.     

Selective attention and memory: evoked potentials in visual and auditory verbal competition

The brain mechanisms of the selective verbal attention were studied using evoked potential (EP) technique. It was shown that the late "cognitive" EP components (400-800 ms) related to memory function were more positive to the words presented via a relevant sensory channel and more negative to irrelevant words. The hypothesis is advanced that words delivered via two competing sensory channels, such as visual and auditory, are perceived, i.e., the subject sees and hears them. However, irrelevant signals are not stored in episodic memory due to the active inhibition of the information transmission to the hippocampal structures.     

Neural encoding schemes of tactile information in afferent activity of the vibrissal system

When rats acquire sensory information by actively moving their vibrissae, a neural code is manifested at different levels of the sensory system. Behavioral studies in tactile discrimination agree that rats can distinguish different roughness surfaces by whisking their vibrissae. The present study explores the existence of neural encoding in the afferent activity of one vibrissal nerve. Two neural encoding schemes based on “events” were proposed (cumulative event count and median inter-event time). The events were detected by using an event detection algorithm based on multiscale decomposition of the signal (Continuous Wavelet Transform). The encoding schemes were quantitatively evaluated through the maximum amount of information which was obtained by the Shannon’s mutual information formula. Moreover, the effect of difference distances between rat snout and swept surfaces on the information values was also studied. We found that roughness information was encoded by events of 0.8 ms duration in the cumulative event count and event of 1.0 to 1.6 ms duration in the median inter-event count. It was also observed that an extreme decrease of the distance between rat snout and swept surfaces significantly reduces the information values and the capacity to discriminate among the sweep situations.     

Natural scene statistics and nonlinear neural interactions between frequency-selective mechanisms

Linear filtering is a basic concept in neural models of early sensory information processing. In particular the visual system has been described to perform a wavelet-like multi-channel decomposition by a set of independent spatial-frequency selective filter mechanisms. Here we suggest that this principle of linear filtering deserves a critical re-evaluation. We propose that an optimal adaptation to natural scene statistics would require AND-like nonlinear interactions between the frequency-selective filter channels. We describe how this hypothesis can be tested by predicted violations of the principle of linearity that should be observable if cortical neurons would actually implement the proposed nonlinearities. We further explain why these effects might have been easily overlooked in earlier tests of the linearity of neurons in primary visual cortex.     

The study of sensory bases of the feeding behavior of the African catfish Clarias gariepinus (Clariidae,Siluriformes)

An experimental study of the role of vision and some other sensory systems in the feeding behavior of the African catfish Clarias gariepinus (SL 20–24 cm, weight 80–95 g) was performed. It was shown that catfish similarly efficiently detect pellets of artificial food in the light and in the dark; in the dark, they display a greater preference for liver pieces than for pellets of artificial food of the same size. Blue pellets were most attractive for fish; red pellets, less attractive; and green pellets were the least attractive. Color selectivity is exhibited by catfish in the light at various combinations of pellets of different colors delivered simultaneously, but is lost in total darkness. The data obtained indicate the polysensory basis of the feeding behavior of catfish. In the light, a well-developed visual reception provides not only for a successful search for food, but also for a selective choice of food items by color. The olfactory and taste properties of food are important regulators of feeding under various light conditions. Thus, C. gariepinus, like other euryphagous fish, lacks a profound sensory specialization in the feeding behavior. When environmental conditions change, the role of the leading sensory system may pass from one sense organ to another.     

Synaptic Plasticity Controls Sensory Responses through Frequency-Dependent Gamma Oscillation Resonance

Synchronized gamma frequency oscillations in neural networks are thought to be important to sensory information processing, and their effects have been intensively studied. Here we describe a mechanism by which the nervous system can readily control gamma oscillation effects, depending selectively on visual stimuli. Using a model neural network simulation, we found that sensory response in the primary visual cortex is significantly modulated by the resonance between “spontaneous” and “stimulus-driven” oscillations. This gamma resonance can be precisely controlled by the synaptic plasticity of thalamocortical connections, and cortical response is regulated differentially according to the resonance condition. The mechanism produces a selective synchronization between the afferent and downstream neural population. Our simulation results explain experimental observations such as stimulus-dependent synchronization between the thalamus and the cortex at different oscillation frequencies. The model generally shows how sensory information can be selectively routed depending on its frequency components.     

Natural patterns of neural activity

Physiological mechanisms of neuronal information processing have been shaped during evolution by a continual interplay between organisms and their sensory surroundings. Thus, when asking for the functional significance of such mechanisms, the natural conditions under which they operate must be considered. This has been done successfully in several studies that employ sensory stimulation under in vivo conditions. These studies address the question of how physiological mechanisms within neurons are properly adjusted to the characteristics of natural stimuli and to the demands imposed on the system being studied. Results from diverse animal models show how neurons exploit natural stimulus statistics efficiently by utilizing specific filtering capacities. Mechanisms that allow neurons to adapt to the currently relevant range from an often immense stimulus spectrum are outlined, and examples are provided that suggest that information transfer between neurons is shaped by the system-specific computational tasks in the behavioral context.     

Natural patterns of neural activity: how physiological mechanisms are orchestrated to cope with real life

Physiological mechanisms of neuronal information processing have been shaped during evolution by a continual interplay between organisms and their sensory surroundings. Thus, when asking for the functional significance of such mechanisms, the natural conditions under which they operate must be considered. This has been done successfully in several studies that employ sensory stimulation under in vivo conditions. These studies address the question of how physiological mechanisms within neurons are properly adjusted to the characteristics of natural stimuli and to the demands imposed on the system being studied. Results from diverse animal models show how neurons exploit natural stimulus statistics efficiently by utilizing specific filtering capacities. Mechanisms that allow neurons to adapt to the currently relevant range from an often immense stimulus spectrum are outlined, and examples are provided that suggest that information transfer between neurons is shaped by the system-specific computational tasks in the behavioral context.     

Individuals with autism spectrum disorder have altered visual encoding capacity

Perceptual anomalies in individuals with autism spectrum disorder (ASD) have been attributed to an imbalance in weighting incoming sensory evidence with prior knowledge when interpreting sensory information. Here, we show that sensory encoding and how it adapts to changing stimulus statistics during feedback also characteristically differs between neurotypical and ASD groups. In a visual orientation estimation task, we extracted the accuracy of sensory encoding from psychophysical data by using an information theoretic measure. Initially, sensory representations in both groups reflected the statistics of visual orientations in natural scenes, but encoding capacity was overall lower in the ASD group. Exposure to an artificial (i.e., uniform) distribution of visual orientations coupled with performance feedback altered the sensory representations of the neurotypical group toward the novel experimental statistics, while also increasing their total encoding capacity. In contrast, neither total encoding capacity nor its allocation significantly changed in the ASD group. Across both groups, the degree of adaptation was correlated with participants’ initial encoding capacity. These findings highlight substantial deficits in sensory encoding—independent from and potentially in addition to deficits in decoding—in individuals with ASD.

It is increasingly recognized that individuals with Autism Spectrum Disorder (ASD) show anomalies in perception, and these have been recently attributed to altered decoding (i.e. interpretation of sensory signals). This study reveals that independent of these changes, individuals with ASD show upstream deficits in sensory encoding (i.e., how samples are drawn from the environment).     

Adaptation and selective information transmission in the cricket auditory neuron AN2

Sensory systems adapt their neural code to changes in the sensory environment, often on multiple time scales. Here, we report a new form of adaptation in a first-order auditory interneuron (AN2) of crickets. We characterize the response of the AN2 neuron to amplitude-modulated sound stimuli and find that adaptation shifts the stimulus-response curves toward higher stimulus intensities, with a time constant of 1.5 s for adaptation and recovery. The spike responses were thus reduced for low-intensity sounds. We then address the question whether adaptation leads to an improvement of the signal's representation and compare the experimental results with the predictions of two competing hypotheses: infomax, which predicts that information conveyed about the entire signal range should be maximized, and selective coding, which predicts that "foreground" signals should be enhanced while "background" signals should be selectively suppressed. We test how adaptation changes the input-response curve when presenting signals with two or three peaks in their amplitude distributions, for which selective coding and infomax predict conflicting changes. By means of Bayesian data analysis, we quantify the shifts of the measured response curves and also find a slight reduction of their slopes. These decreases in slopes are smaller, and the absolute response thresholds are higher than those predicted by infomax. Most remarkably, and in contrast to the infomax principle, adaptation actually reduces the amount of encoded information when considering the whole range of input signals. The response curve changes are also not consistent with the selective coding hypothesis, because the amount of information conveyed about the loudest part of the signal does not increase as predicted but remains nearly constant. Less information is transmitted about signals with lower intensity.     

Encoding odorant identity by spiking packets of rate-invariant neurons in awake mice

Background

How do neural networks encode sensory information? Following sensory stimulation, neural coding is commonly assumed to be based on neurons changing their firing rate. In contrast, both theoretical works and experiments in several sensory systems showed that neurons could encode information as coordinated cell assemblies by adjusting their spike timing and without changing their firing rate. Nevertheless, in the olfactory system, there is little experimental evidence supporting such model.

Methodology/Principal Findings

To study these issues, we implanted tetrodes in the olfactory bulb of awake mice to record the odorant-evoked activity of mitral/tufted (M/T) cells. We showed that following odorant presentation, most M/T neurons do not significantly change their firing rate over a breathing cycle but rather respond to odorant stimulation by redistributing their firing activity within respiratory cycles. In addition, we showed that sensory information can be encoded by cell assemblies composed of such neurons, thus supporting the idea that coordinated populations of globally rate-invariant neurons could be efficiently used to convey information about the odorant identity. We showed that different coding schemes can convey high amount of odorant information for specific read-out time window. Finally we showed that the optimal readout time window corresponds to the duration of gamma oscillations cycles.

Conclusion

We propose that odorant can be encoded by population of cells that exhibit fine temporal tuning of spiking activity while displaying weak or no firing rate change. These cell assemblies may transfer sensory information in spiking packets sequence using the gamma oscillations as a clock. This would allow the system to reach a tradeoff between rapid and accurate odorant discrimination.     

Active sensing: matching motor and sensory space

A recent study has shown that, unusually, both the sensory and motor capabilities of an electric fish are omnidirectional. This matching of motor and sensory spaces helps the fish to hunt prey efficiently - particularly important given their energetically costly active sensory system.