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.     

Sensory gating and its modulation by cannabinoids: electrophysiological, computational and mathematical analysis

Gating of sensory information can be assessed using an auditory conditioning-test paradigm which measures the reduction in the auditory evoked response to a test stimulus following an initial conditioning stimulus. Recording brainwaves from specific areas of the brain using multiple electrodes is helpful in the study of the neurobiology of sensory gating. In this paper, we use such technology to investigate the role of cannabinoids in sensory gating in the CA3 region of the rat hippocampus. Our experimental results show that application of the exogenous cannabinoid agonist WIN55,212-2 can abolish sensory gating. We have developed a phenomenological model of cannabinoid dynamics incorporated within a spiking neural network model of CA3 with synaptically interacting pyramidal and basket cells. Direct numerical simulations of this model suggest that the basic mechanism for this effect can be traced to the suppression of inhibition of slow GABA_B synapses. Furthermore, by working with a simpler mathematical firing rate model we are able to show the robustness of this mechanism for the abolition of sensory gating.     

Thermodynamic Costs of Information Processing in Sensory Adaptation

Biological sensory systems react to changes in their surroundings. They are characterized by fast response and slow adaptation to varying environmental cues. Insofar as sensory adaptive systems map environmental changes to changes of their internal degrees of freedom, they can be regarded as computational devices manipulating information. Landauer established that information is ultimately physical, and its manipulation subject to the entropic and energetic bounds of thermodynamics. Thus the fundamental costs of biological sensory adaptation can be elucidated by tracking how the information the system has about its environment is altered. These bounds are particularly relevant for small organisms, which unlike everyday computers, operate at very low energies. In this paper, we establish a general framework for the thermodynamics of information processing in sensing. With it, we quantify how during sensory adaptation information about the past is erased, while information about the present is gathered. This process produces entropy larger than the amount of old information erased and has an energetic cost bounded by the amount of new information written to memory. We apply these principles to the E. coli''s chemotaxis pathway during binary ligand concentration changes. In this regime, we quantify the amount of information stored by each methyl group and show that receptors consume energy in the range of the information-theoretic minimum. Our work provides a basis for further inquiries into more complex phenomena, such as gradient sensing and frequency response.     

Prey detection mechanism of elasmobranchs

Elasmobranchs can detect a little amount of electric fields and they have characteristic approach strategies to find an electric dipole source generated by prey or conspecifics. They appear to align the body at a constant angle with the current flow line of the electric field while swimming towards prey. However, it has not been studied how they process the perception of electric fields for the approach behaviour or what kind of neural mechanism is used. We use a model of electrosensory perception with electrodynamics and explore a possible approach mechanism based on the sensory landscape distributed on electroreceptors. This paper presents that elasmobranchs can estimate the direction of the electric field by swaying their head, which will be a basis information for their particular approach behaviour. A velocity profile of voltage gradients and intensity difference among the ampullary clusters can be another cues to detect a prey source.     

Electromechanical model for object roughness perception during finger sliding

Touch allows us to gather abundant information in the world around us. However, how sensory cells embedded in the fingers convey texture information into their firing patterns is still poorly understood. Here, we develop an electromechanical model for roughness perception by incorporating main ingredients such as voltage-gated ion channels, active ion pumps, mechanosensitive channels, and cell deformation. The model reveals that sensory cells can convey texture wavelengths into the period of their firing patterns as the finger slides across object surfaces, but they can only convey a limited range of texture wavelengths. We also show that an increase in sliding speed broadens the decoding wavelength range at the cost of reduction of lower perception limits. Thus, a smaller sliding speed and a bigger contact force may be needed to successfully discern a smooth surface, consistent with previous psychophysical observations. Moreover, we show that cells with slowly adapting mechanosensitive channels can still fire action potentials under static loadings, indicating that slowly adapting mechanosensitive channels may contribute to the perception of coarse textures under static touch. Our work thus provides a new theoretical framework to study roughness perception and may have important implications for the design of electronic skin, artificial touch, and haptic interfaces.     

Biomimetic vibrissal sensing for robots

Active vibrissal touch can be used to replace or to supplement sensory systems such as computer vision and, therefore, improve the sensory capacity of mobile robots. This paper describes how arrays of whisker-like touch sensors have been incorporated onto mobile robot platforms taking inspiration from biology for their morphology and control. There were two motivations for this work: first, to build a physical platform on which to model, and therefore test, recent neuroethological hypotheses about vibrissal touch; second, to exploit the control strategies and morphology observed in the biological analogue to maximize the quality and quantity of tactile sensory information derived from the artificial whisker array. We describe the design of a new whiskered robot, Shrewbot, endowed with a biomimetic array of individually controlled whiskers and a neuroethologically inspired whisking pattern generation mechanism. We then present results showing how the morphology of the whisker array shapes the sensory surface surrounding the robot's head, and demonstrate the impact of active touch control on the sensory information that can be acquired by the robot. We show that adopting bio-inspired, low latency motor control of the rhythmic motion of the whiskers in response to contact-induced stimuli usefully constrains the sensory range, while also maximizing the number of whisker contacts. The robot experiments also demonstrate that the sensory consequences of active touch control can be usefully investigated in biomimetic robots.     

A computer model for describing the effect of urethral afferents on simulated lower urinary tract function

A computer model of mechanical properties of the bladder, the urethra and the rhabdosphincter, as well as their neural control is presented in this paper. The model has a rather simple design and processes sensory information from both the bladder wall tension and urethral stretch. It is assumed that afferent signals from the urethra are involved in a sacral excitatory reflex and a supraspinal inhibitory reflex. Pressure and flow signals that resemble experimentally measured normal human behaviour could be simulated with this model. From these simulations the relation between the neural control mechanisms used in the model and the neural control mechanism in vivo cannot be judged entirely because similar behaviour could be simulated with models that are bas ed on different neural control mechanisms. Also behaviour that resembles detrusor overactivity was simulated with our model after an externally induced rise in detrusor pressure was added. Detrusor overactivity, sometimes in combination with urethral relaxation, can occur during a urodynamic investigation. A possible explanation for this detrusor overactivity might be that the micturition reflex is triggered by unknown disturbances and is inhibited immediately after by the same mechanism that normally ceases voiding. The described model provides such a mechanism. Based on these simulations, therefore, it is concluded that urethral afferent signals might be important in lower urinary tract control.     

Fast coding of orientation in primary visual cortex

Understanding how populations of neurons encode sensory information is a major goal of systems neuroscience. Attempts to answer this question have focused on responses measured over several hundred milliseconds, a duration much longer than that frequently used by animals to make decisions about the environment. How reliably sensory information is encoded on briefer time scales, and how best to extract this information, is unknown. Although it has been proposed that neuronal response latency provides a major cue for fast decisions in the visual system, this hypothesis has not been tested systematically and in a quantitative manner. Here we use a simple 'race to threshold' readout mechanism to quantify the information content of spike time latency of primary visual (V1) cortical cells to stimulus orientation. We find that many V1 cells show pronounced tuning of their spike latency to stimulus orientation and that almost as much information can be extracted from spike latencies as from firing rates measured over much longer durations. To extract this information, stimulus onset must be estimated accurately. We show that the responses of cells with weak tuning of spike latency can provide a reliable onset detector. We find that spike latency information can be pooled from a large neuronal population, provided that the decision threshold is scaled linearly with the population size, yielding a processing time of the order of a few tens of milliseconds. Our results provide a novel mechanism for extracting information from neuronal populations over the very brief time scales in which behavioral judgments must sometimes be made.     

The Emergence of Synaesthesia in a Neuronal Network Model via Changes in Perceptual Sensitivity and Plasticity

Synaesthesia is an unusual perceptual experience in which an inducer stimulus triggers a percept in a different domain in addition to its own. To explore the conditions under which synaesthesia evolves, we studied a neuronal network model that represents two recurrently connected neural systems. The interactions in the network evolve according to learning rules that optimize sensory sensitivity. We demonstrate several scenarios, such as sensory deprivation or heightened plasticity, under which synaesthesia can evolve even though the inputs to the two systems are statistically independent and the initial cross-talk interactions are zero. Sensory deprivation is the known causal mechanism for acquired synaesthesia and increased plasticity is implicated in developmental synaesthesia. The model unifies different causes of synaesthesia within a single theoretical framework and repositions synaesthesia not as some quirk of aberrant connectivity, but rather as a functional brain state that can emerge as a consequence of optimising sensory information processing.     

Sensory feedback in a bump attractor model of path integration

Mammalian spatial navigation systems utilize several different sensory information channels. This information is converted into a neural code that represents the animal’s current position in space by engaging place cell, grid cell, and head direction cell networks. In particular, sensory landmark (allothetic) cues can be utilized in concert with an animal’s knowledge of its own velocity (idiothetic) cues to generate a more accurate representation of position than path integration provides on its own (Battaglia et al. The Journal of Neuroscience 24(19):4541–4550 (2004)). We develop a computational model that merges path integration with feedback from external sensory cues that provide a reliable representation of spatial position along an annular track. Starting with a continuous bump attractor model, we explore the impact of synaptic spatial asymmetry and heterogeneity, which disrupt the position code of the path integration process. We use asymptotic analysis to reduce the bump attractor model to a single scalar equation whose potential represents the impact of asymmetry and heterogeneity. Such imperfections cause errors to build up when the network performs path integration, but these errors can be corrected by an external control signal representing the effects of sensory cues. We demonstrate that there is an optimal strength and decay rate of the control signal when cues appear either periodically or randomly. A similar analysis is performed when errors in path integration arise from dynamic noise fluctuations. Again, there is an optimal strength and decay of discrete control that minimizes the path integration error.     

A Hierarchy of Time-Scales and the Brain

In this paper, we suggest that cortical anatomy recapitulates the temporal hierarchy that is inherent in the dynamics of environmental states. Many aspects of brain function can be understood in terms of a hierarchy of temporal scales at which representations of the environment evolve. The lowest level of this hierarchy corresponds to fast fluctuations associated with sensory processing, whereas the highest levels encode slow contextual changes in the environment, under which faster representations unfold. First, we describe a mathematical model that exploits the temporal structure of fast sensory input to track the slower trajectories of their underlying causes. This model of sensory encoding or perceptual inference establishes a proof of concept that slowly changing neuronal states can encode the paths or trajectories of faster sensory states. We then review empirical evidence that suggests that a temporal hierarchy is recapitulated in the macroscopic organization of the cortex. This anatomic-temporal hierarchy provides a comprehensive framework for understanding cortical function: the specific time-scale that engages a cortical area can be inferred by its location along a rostro-caudal gradient, which reflects the anatomical distance from primary sensory areas. This is most evident in the prefrontal cortex, where complex functions can be explained as operations on representations of the environment that change slowly. The framework provides predictions about, and principled constraints on, cortical structure–function relationships, which can be tested by manipulating the time-scales of sensory input.     

A Normalization Model of Attentional Modulation of Single Unit Responses

Although many studies have shown that attention to a stimulus can enhance the responses of individual cortical sensory neurons, little is known about how attention accomplishes this change in response. Here, we propose that attention-based changes in neuronal responses depend on the same response normalization mechanism that adjusts sensory responses whenever multiple stimuli are present. We have implemented a model of attention that assumes that attention works only through this normalization mechanism, and show that it can replicate key effects of attention. The model successfully explains how attention changes the gain of responses to individual stimuli and also why modulation by attention is more robust and not a simple gain change when multiple stimuli are present inside a neuron''s receptive field. Additionally, the model accounts well for physiological data that measure separately attentional modulation and sensory normalization of the responses of individual neurons in area MT in visual cortex. The proposal that attention works through a normalization mechanism sheds new light a broad range of observations on how attention alters the representation of sensory information in cerebral cortex.     

Testing simulation theory with cross-modal multivariate classification of fMRI data

The discovery of mirror neurons has suggested a potential neural basis for simulation and common coding theories of action perception, theories which propose that we understand other people's actions because perceiving their actions activates some of our neurons in much the same way as when we perform the actions. We propose testing this model directly in humans with functional magnetic resonance imaging (fMRI) by means of cross-modal classification. Cross-modal classification evaluates whether a classifier that has learned to separate stimuli in the sensory domain can also separate the stimuli in the motor domain. Successful classification provides support for simulation theories because it means that the fMRI signal, and presumably brain activity, is similar when perceiving and performing actions. In this paper we demonstrate the feasibility of the technique by showing that classifiers which have learned to discriminate whether a participant heard a hand or a mouth action, based on the activity patterns in the premotor cortex, can also determine, without additional training, whether the participant executed a hand or mouth action. This provides direct evidence that, while perceiving others' actions, (1) the pattern of activity in premotor voxels with sensory properties is a significant source of information regarding the nature of these actions, and (2) that this information shares a common code with motor execution.     

EVALUATION OF PANELIST PERFORMANCE IN DESCRIPTIVE PROFILING OF RANCID SAUSAGES: A MULTIVARIATE STUDY

This paper illustrates an application of principal component analysis (PCA), partial least squares regression (PLS) and generalized procrustes analysis (GPA) to evaluate the ability of a trained group of assessors to perceive rancidity in foods. PCA and regression PLS were utilized to determine to which extent sensory attributes capture the information perceived by a trained sensory panel, and if this can be developed into a predictive model for rancidity in sausages. The data were submitted to a GPA to obtain a map of the products for each subject as compared with a consensus products map. Assessors plots for the sensory attributes were also obtained to reveal the dissimilarities between panelists and to explore clustering.     

The confusion effect in predatory neural networks

A simple artificial neural network model of image reconstruction in sensory maps is presented to explain the difficulty predators experience in targeting prey in large groups (the confusion effect). Networks are trained to reconstruct multiple randomly conformed "retinal" images of prey groups in an internal spatial map of their immediate environment. They are then used to simulate prey targeting by predators on groups of specific conformation. Networks trained with the biologically plausible associative reward-penalty method produce a more realistic model of the confusion effect than those trained with the popular but biologically implausible backpropagation method. The associative reward-penalty model makes the novel prediction that the accuracy-group size relationship is U shaped, and this prediction is confirmed by empirical data gathered from interactive computer simulation experiments with humans as "predators." The model further predicts all factors known from previous empirical work (and most factors suspected) to alleviate the confusion effect: increased relative intensity of the target object, heterogeneity of group composition, and isolation of the target. Interestingly, group compaction per se is not predicted to worsen predator confusion. This study indicates that the relatively simple, nonattentional mechanism of information degradation in the sensory mapping process is potentially important in generating the confusion effect.     

模拟昆虫视觉-行为抉择的强化学习模型

视觉信息用于行为抉择的过程是一个极其复杂的脑信息处理过程,昆虫或动物对外界环境的学习是以价值来控制的,并可影响其行为抉择,研究这一过程对揭示人类自身脑运行机制有重要意义.文章在郭爱克研究小组果蝇实验提供的生物依据基础上,提出了一种模拟果蝇视觉-行为抉择的神经网络模型.该模型引入了价值和基于价值的强化学习算法,应用于输入视觉图像的强化学习,以此建立果蝇脑内多巴胺和蘑菇体对于抉择判断的价值体系.模拟的结果表明,该模型可以模拟果蝇视觉信息的学习和行为抉择过程,其结果与生物实验相符,同时也为机器人视觉信息控制行为抉择的应用提供了基础.     

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.     

Duration channels mediate human time perception

The task of deciding how long sensory events seem to last is one that the human nervous system appears to perform rapidly and, for sub-second intervals, seemingly without conscious effort. That these estimates can be performed within and between multiple sensory and motor domains suggest time perception forms one of the core, fundamental processes of our perception of the world around us. Given this significance, the current paucity in our understanding of how this process operates is surprising. One candidate mechanism for duration perception posits that duration may be mediated via a system of duration-selective 'channels', which are differentially activated depending on the match between afferent duration information and the channels' 'preferred' duration. However, this model awaits experimental validation. In the current study, we use the technique of sensory adaptation, and we present data that are well described by banks of duration channels that are limited in their bandwidth, sensory-specific, and appear to operate at a relatively early stage of visual and auditory sensory processing. Our results suggest that many of the computational principles the nervous system applies to coding visual spatial and auditory spectral information are common to its processing of temporal extent.     

Using sensory weighting to model the influence of canal,otolith and visual cues on spatial orientation and eye movements

 The sensory weighting model is a general model of sensory integration that consists of three processing layers. First, each sensor provides the central nervous system (CNS) with information regarding a specific physical variable. Due to sensor dynamics, this measure is only reliable for the frequency range over which the sensor is accurate. Therefore, we hypothesize that the CNS improves on the reliability of the individual sensor outside this frequency range by using information from other sensors, a process referred to as “frequency completion.” Frequency completion uses internal models of sensory dynamics. This “improved” sensory signal is designated as the “sensory estimate” of the physical variable. Second, before being combined, information with different physical meanings is first transformed into a common representation; sensory estimates are converted to intermediate estimates. This conversion uses internal models of body dynamics and physical relationships. Third, several sensory systems may provide information about the same physical variable (e.g., semicircular canals and vision both measure self-rotation). Therefore, we hypothesize that the “central estimate” of a physical variable is computed as a weighted sum of all available intermediate estimates of this physical variable, a process referred to as “multicue weighted averaging.” The resulting central estimate is fed back to the first two layers. The sensory weighting model is applied to three-dimensional (3D) visual–vestibular interactions and their associated eye movements and perceptual responses. The model inputs are 3D angular and translational stimuli. The sensory inputs are the 3D sensory signals coming from the semicircular canals, otolith organs, and the visual system. The angular and translational components of visual movement are assumed to be available as separate stimuli measured by the visual system using retinal slip and image deformation. In addition, both tonic (“regular”) and phasic (“irregular”) otolithic afferents are implemented. Whereas neither tonic nor phasic otolithic afferents distinguish gravity from linear acceleration, the model uses tonic afferents to estimate gravity and phasic afferents to estimate linear acceleration. The model outputs are the internal estimates of physical motion variables and 3D slow-phase eye movements. The model also includes a smooth pursuit module. The model matches eye responses and perceptual effects measured during various motion paradigms in darkness (e.g., centered and eccentric yaw rotation about an earth-vertical axis, yaw rotation about an earth-horizontal axis) and with visual cues (e.g., stabilized visual stimulation or optokinetic stimulation). Received: 20 September 2000 / Accepted in revised form: 28 September 2001