Responses of neurons in the electrosensory lateral line lobe of the weakly electric fish <Emphasis Type="Italic">Gnathonemus petersii</Emphasis> to simple and complex electrosensory stimuli

Mormyrid fish use active electrolocation to detect and analyze objects. The electrosensory lateral line lobe in the brain receives input from electroreceptors and an efference copy of the command to discharge the electric organ. In curarized fish, we recorded extracellularly from neurons of the electrosensory lateral line lobe while stimulating in the periphery with either a local point stimulus or with a more natural whole-body stimulus. Two classes of neurons were found: (1) three types of E-cells, which were excited by a point stimulus; and (2) two types of I-cells, which were inhibited by point stimulus and responded with excitation to the electric organ corollary discharge. While all neurons responded to a point stimulus, only one out of two types of I-units and two of the three types of E-units changed their firing behavior to a whole-body stimulus or when an object was present. In most units, the responses to whole-body stimuli and to point stimuli differed substantially. Many electrosensory lateral line lobe units showed neural plasticity after prolonged sensory stimulation. However, plastic effects during whole body stimulation were often unlike those occurring during point stimuli, suggesting that under natural conditions electrosensory lateral line lobe network effects play an important role in shaping neural plasticity.     

Startle Auditory Stimuli Enhance the Performance of Fast Dynamic Contractions

Fast reaction times and the ability to develop a high rate of force development (RFD) are crucial for sports performance. However, little is known regarding the relationship between these parameters. The aim of this study was to investigate the effects of auditory stimuli of different intensities on the performance of a concentric bench-press exercise. Concentric bench-presses were performed by thirteen trained subjects in response to three different conditions: a visual stimulus (VS); a visual stimulus accompanied by a non-startle auditory stimulus (AS); and a visual stimulus accompanied by a startle auditory stimulus (SS). Peak RFD, peak velocity, onset movement, movement duration and electromyography from pectoralis and tricep muscles were recorded. The SS condition induced an increase in the RFD and peak velocity and a reduction in the movement onset and duration, in comparison with the VS and AS condition. The onset activation of the pectoralis and tricep muscles was shorter for the SS than for the VS and AS conditions. These findings point out to specific enhancement effects of loud auditory stimulation on the rate of force development. This is of relevance since startle stimuli could be used to explore neural adaptations to resistance training.     

Stimulus-dependent Maximum Entropy Models of Neural Population Codes

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME) model—a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population.     

Contrast, probability, and saccadic latency; evidence for independence of detection and decision

Many factors influence how long it takes to respond to a visual stimulus. The lowest-level factors, such as luminance and contrast, determine how easily different elements of a target can be detected. Higher-level factors are to do with whether these elements constitute a stimulus requiring a response; they include prior probability and urgency. It is natural to think of these two processes, detection and decision, as occurring in series, so that overall reaction time is essentially the sum of the contributions of each stage. Here, measurements of saccadic latency to visual targets whose contrast and prior probability are systematically manipulated demonstrate that there are indeed separable stages of detection and decision. Both can be quantitatively described by rise-to-threshold mechanisms; the average rate of rise of the first is a simple logarithmic function of target contrast, whereas the second shows the linear rise characteristic of the LATER model of neural decision making. The implication is that under normal, high-contrast conditions, in which detection is very fast, the random variability that is characteristic of all reaction times is not caused by sensory noise but is gratuitously introduced by the brain itself; paradoxically, by conferring unpredictability it may aid an organism's survival.     

Human preferences for symmetry: subjective experience, cognitive conflict and cortical brain activity

This study examines the links between human perceptions, cognitive biases and neural processing of symmetrical stimuli. While preferences for symmetry have largely been examined in the context of disorders such as obsessive-compulsive disorder and autism spectrum disorders, we examine various these phenomena in non-clinical subjects and suggest that such preferences are distributed throughout the typical population as part of our cognitive and neural architecture. In Experiment 1, 82 young adults reported on the frequency of their obsessive-compulsive spectrum behaviors. Subjects also performed an emotional Stroop or variant of an Implicit Association Task (the OC-CIT) developed to assess cognitive biases for symmetry. Data not only reveal that subjects evidence a cognitive conflict when asked to match images of positive affect with asymmetrical stimuli, and disgust with symmetry, but also that their slowed reaction times when asked to do so were predicted by reports of OC behavior, particularly checking behavior. In Experiment 2, 26 participants were administered an oddball Event-Related Potential task specifically designed to assess sensitivity to symmetry as well as the OC-CIT. These data revealed that reaction times on the OC-CIT were strongly predicted by frontal electrode sites indicating faster processing of an asymmetrical stimulus (unparallel lines) relative to a symmetrical stimulus (parallel lines). The results point to an overall cognitive bias linking disgust with asymmetry and suggest that such cognitive biases are reflected in neural responses to symmetrical/asymmetrical stimuli.     

The fine structure of campaniform sensilla on the halteres of Drosophila melanogaster

The campaniform sensilla on halteres of Drosophila were studied by electron microscopy in order to establish the relationships of functional elements in the sensory system. The surface of the sensillum consists of an oval cuticular cap membrane which may contain resilin, the rubberlike protein. A border of denser cuticle rings the cap membrane, and extending down around the neural process is a third type of cuticle filled with a fourth light fibrous type. The four cuticular components form a system for displacement of the neural process. The neural process is differentiated into a terminal fan-shaped structure projecting from a bulbous dilatation which tapers to a neck region ending proximally with two basal bodies. The neural process is packed with microtubules. Surrounding the dendrite is an inner enveloping cell, attached to the basal body region by septate desmosomes and by desmosomes to which microtubules of the enveloping cell are applied. An outer enveloping cell surrounds the inner one. The tip of the neural process is covered with a dense secretion which is tightly bound to the cap membrane. The dense secretion is surrounded by an extracellular fluid which might be compressed hydraulically by the cuticular system. The stimulus of cuticular distortion could thus be transmitted to the neural process which may be displaced between its fixed ends.     

Stimulus-induced bifurcations in discrete-time neural oscillators

Based on theoretical issues and neurobiological evidence, considerable interest has recently focused on dynamic computational elements in neural systems. Such elements respond to stimuli by altering their dynamical behavior rather than by changing a scalar output. In particular, neural oscillators capable of chaotic dynamics represent a potentially very rich substrate for complex spatiotemporal information processing. However, the response properties of such systems must be studied in detail before they can be used as computational elements in neural models. In this paper, we focus on the response of a very simple discrete-time neural oscillator model to a fixed input. We show that the oscillator responds to the stimulus through a fairly complex set of bifurcations, and shows critical switching between attractors. This information can be used to construct very sophisticated dynamic computational elements with well-understood response properties. Examples of such elements are presented in the paper. We end with a brief discussion of simple architectures for networks of dynamical elements, and the relevance of our results to neurobiological models. Received: 7 August 1997 / Accepted in revised form: 22 April 1998     

Microscopic localization of cholinesterases in the nervous systems of the lobsters, Panulirus argus and Homarus americanus

Using acetylthiocholine as substrate, microscopically localizable cholinesterase (ChE) activity is demonstrated in neural and glial elements of central and peripheral nervous systems of the lobsters, Panulirus argus and Homarus americanus. Moderate to very intense ChE activity occurs in all synaptic regions of the central ganglia and stomatogastric ganglion, in glial sheaths around neuron somata and peripheral nerve axons, and in cytoplasm of a few nerve cell bodies. Axons, identified as motor, contain extremely little ChE. The principal reaction in peripheral nerves occurs in sheath elements of sensory fibres; in most cases, much of the reaction is lost as the nerves lose the sheaths at the point of entry into brain.     

A model for feature linking via collective oscillations in the primary visual cortex

A neural network model for explaining experimentally observed neuronal responses in cat primary visual cortex is proposed. In our model, the basic functional unit is an orientation column which is represented by a large homogeneous population of neurons modeled as integrate-and-fire type excitable elements. The orientation column exhibits spontaneous collective oscillations in activity in response to suitable visual stimuli. Such oscillations are caused by mutual synchronization among the neurons within the column. Numerical simulation for various stimulus patterns shows that as a result of activity correlations between different columns, the amplitude and the phase of the oscillation in each column depend strongly on the global feature of the stimulus pattern. These results satisfactorily account for experimental observations.     

Information transmission in oscillatory neural activity

Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al. in Spikes: exploring the neural code. MIT Press, Cambridge, 1999; Brenner et al. in Neural Comput 12(7):1531-1552, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.     

大鼠初级传入纤维与脊髓背角神经元间的动作电序列的突触传递

外周感觉神经元通过动作电位序列对信号进行编码,这些动作电位序列经过突触传递最终到达脑部。但是各种脉冲序列如何通过神经元之间的化学突触进行传递依然是一个悬而未决的问题。研究了初级传入A6纤维与背角神经元之间各种动作电位序列的突触传递过程。用于刺激的规则,周期、随机脉冲序列由短簇脉冲或单个脉冲构成。定义“事件”(event)为峰峰问期(intefspike interval)小于或等于规定阈值的最长动作电位串,然后从脉冲序列中提取事件间间期(interevent interval,IEI)。用时间,IEI图与回归映射的方法分析IEI序列,结果表明在突触后输出脉冲序列中可以检测到突触前脉冲序列的主要时间结构特征,特别是在短簇脉冲作为刺激单位时。通过计算输入与输出脉冲序列的互信息,发现短簇脉冲可以更可靠地跨突触传递由输入序列携带的神经信息。这些结果表明外周输入脉冲序列的主要时间结构特征可以跨突触传递,在突触传递神经信息的过程中短簇脉冲更为有效。这一研究在从突触传递角度探索神经信息编码方面迈出了一步。     

Characteristics of Human Luminance Discrimination and Modeling a Neural Network Based on the Response Properties of the Visual Cortex

Reaction time (RT) and error rate that depend on stimulus duration were measured in a luminance-discrimination reaction time task. Two patches of light with different luminance were presented to participants for ‘short’ (150 ms) or ‘long’ (1 s) period on each trial. When the stimulus duration was ‘short’, the participants responded more rapidly with poorer discrimination performance than they did in the longer duration. The results suggested that different sensory responses in the visual cortices were responsible for the dependence of response speed and accuracy on the stimulus duration during the luminance-discrimination reaction time task. It was shown that the simple winner-take-all-type neural network model receiving transient and sustained stimulus information from the primary visual cortex successfully reproduced RT distributions for correct responses and error rates. Moreover, temporal spike sequences obtained from the model network closely resembled to the neural activity in the monkey prefrontal or parietal area during other visual decision tasks such as motion discrimination and oddball detection tasks.     

An informational approach to reaction times

Simple reaction time is the minimum time required to respond to a signal such as a steady light or tone. Such a reaction time is taken to be the time required for transmission of a fixed quantity of information, ΔH, from stimulus to subject. That is, information summation replaces energy summation. This information is calculated from consideration of the quantum nature of the stimulus. The theoretically derived equation for reaction time is fitted to experimental data. Piéron's empirical law for reaction time is obtained as an approximation from a proposed informational equation. The exponent in Piéron's law is found to be the same as the exponent in the power law of sensation. Threshold appears to be the smallest stimulus capable of transmitting the quantity of information ΔH.     

Stimulus and network dynamics collide in a ratiometric model of the antennal lobe macroglomerular complex

Time is considered to be an important encoding dimension in olfaction, as neural populations generate odour-specific spatiotemporal responses to constant stimuli. However, during pheromone mediated anemotactic search insects must discriminate specific ratios of blend components from rapidly time varying input. The dynamics intrinsic to olfactory processing and those of naturalistic stimuli can therefore potentially collide, thereby confounding ratiometric information. In this paper we use a computational model of the macroglomerular complex of the insect antennal lobe to study the impact on ratiometric information of this potential collision between network and stimulus dynamics. We show that the model exhibits two different dynamical regimes depending upon the connectivity pattern between inhibitory interneurons (that we refer to as fixed point attractor and limit cycle attractor), which both generate ratio-specific trajectories in the projection neuron output population that are reminiscent of temporal patterning and periodic hyperpolarisation observed in olfactory antennal lobe neurons. We compare the performance of the two corresponding population codes for reporting ratiometric blend information to higher centres of the insect brain. Our key finding is that whilst the dynamically rich limit cycle attractor spatiotemporal code is faster and more efficient in transmitting blend information under certain conditions it is also more prone to interference between network and stimulus dynamics, thus degrading ratiometric information under naturalistic input conditions. Our results suggest that rich intrinsically generated network dynamics can provide a powerful means of encoding multidimensional stimuli with high accuracy and efficiency, but only when isolated from stimulus dynamics. This interference between temporal dynamics of the stimulus and temporal patterns of neural activity constitutes a real challenge that must be successfully solved by the nervous system when faced with naturalistic input.     

Turning the other cheek: the viewpoint dependence of facial expression after-effects

How do we visually encode facial expressions? Is this done by viewpoint-dependent mechanisms representing facial expressions as two-dimensional templates or do we build more complex viewpoint independent three-dimensional representations? Recent facial adaptation techniques offer a powerful way to address these questions. Prolonged viewing of a stimulus (adaptation) changes the perception of subsequently viewed stimuli (an after-effect). Adaptation to a particular attribute is believed to target those neural mechanisms encoding that attribute. We gathered images of facial expressions taken simultaneously from five different viewpoints evenly spread from the three-quarter leftward to the three-quarter rightward facing view. We measured the strength of expression after-effects as a function of the difference between adaptation and test viewpoints. Our data show that, although there is a decrease in after-effect over test viewpoint, there remains a substantial after-effect when adapt and test are at differing three-quarter views. We take these results to indicate that neural systems encoding facial expressions contain a mixture of viewpoint-dependent and viewpoint-independent elements. This accords with evidence from single cell recording studies in macaque and is consonant with a view in which viewpoint-independent expression encoding arises from a combination of view-dependent expression-sensitive responses.     

Detection Theory Applied to the Absolute Sensitivity of Sensory Systems

Mainly the skin senses touch and warmth have been investigated. It is shown that the decision model describes the experimental data better than the threshold model. The experiments lead to the assumption that an internal noise exists, which is a neural activity being undistinguishable from the neural activity caused by small stimuli and which adds to the neural activity caused by the stimulus. The probability distribution of this internal noise can be considered to be gaussian. The relation between stimulus and neural activity is alinear for the touch sense. The question of whether noise of a multiplicative nature must be assumed is discussed.     

Neural network mechanisms underlying stimulus driven variability reduction

It is well established that the variability of the neural activity across trials, as measured by the Fano factor, is elevated. This fact poses limits on information encoding by the neural activity. However, a series of recent neurophysiological experiments have changed this traditional view. Single cell recordings across a variety of species, brain areas, brain states and stimulus conditions demonstrate a remarkable reduction of the neural variability when an external stimulation is applied and when attention is allocated towards a stimulus within a neuron's receptive field, suggesting an enhancement of information encoding. Using an heterogeneously connected neural network model whose dynamics exhibits multiple attractors, we demonstrate here how this variability reduction can arise from a network effect. In the spontaneous state, we show that the high degree of neural variability is mainly due to fluctuation-driven excursions from attractor to attractor. This occurs when, in the parameter space, the network working point is around the bifurcation allowing multistable attractors. The application of an external excitatory drive by stimulation or attention stabilizes one specific attractor, eliminating in this way the transitions between the different attractors and resulting in a net decrease in neural variability over trials. Importantly, non-responsive neurons also exhibit a reduction of variability. Finally, this reduced variability is found to arise from an increased regularity of the neural spike trains. In conclusion, these results suggest that the variability reduction under stimulation and attention is a property of neural circuits.     

Coding "what" and "when" in the Archer fish retina

Traditionally, the information content of the neural response is quantified using statistics of the responses relative to stimulus onset time with the assumption that the brain uses onset time to infer stimulus identity. However, stimulus onset time must also be estimated by the brain, making the utility of such an approach questionable. How can stimulus onset be estimated from the neural responses with sufficient accuracy to ensure reliable stimulus identification? We address this question using the framework of colour coding by the archer fish retinal ganglion cell. We found that stimulus identity, “what”, can be estimated from the responses of best single cells with an accuracy comparable to that of the animal''s psychophysical estimation. However, to extract this information, an accurate estimation of stimulus onset is essential. We show that stimulus onset time, “when”, can be estimated using a linear-nonlinear readout mechanism that requires the response of a population of 100 cells. Thus, stimulus onset time can be estimated using a relatively simple readout. However, large nerve cell populations are required to achieve sufficient accuracy.

Authors Summary

In our interaction with the environment we are flooded with a stream of numerous objects and events. Our brain needs to understand the nature of these complex and rich stimuli in order to react. Research has shown ways in which a ‘what’ stimulus was presented can be encoded by the neural responses. However, to understand ‘what was the nature of the stimulus’ the brain needs to know ‘when’ the stimulus was presented. Here, we investigated how the onset of visual stimulus can be signalled by the retina to higher brain regions. We used archer fish as a framework to test the notion that the answer to the question of ‘when’ something has been presented lies within the larger cell population, whereas the answer to the question of ‘what’ has been presented may be found at the single-neuron level. The utility of the archer fish as model animal stems from its remarkable ability to shoot down insects settling on the foliage above the water level, and its ability to distinguish between artificial targets. Thus, the archer fish can provide the fish equivalent of a monkey or a human that can report psychophysical decisions.     

Stimulus motion propels traveling waves in binocular rivalry

State transitions in the nervous system often take shape as traveling waves, whereby one neural state is replaced by another across space in a wave-like manner. In visual perception, transitions between the two mutually exclusive percepts that alternate when the two eyes view conflicting stimuli (binocular rivalry) may also take shape as traveling waves. The properties of these waves point to a neural substrate of binocular rivalry alternations that have the hallmark signs of lower cortical areas. In a series of experiments, we show a potent interaction between traveling waves in binocular rivalry and stimulus motion. The course of the traveling wave is biased in the motion direction of the suppressed stimulus that gains dominance by means of the wave-like transition. Thus, stimulus motion may propel the traveling wave across the stimulus to the extent that the stimulus motion dictates the traveling wave's direction completely. Using a computational model, we show that a speed-dependent asymmetry in lateral inhibitory connections between retinotopically organized and motion-sensitive neurons can explain our results. We argue that such a change in suppressive connections may play a vital role in the resolution of dynamic occlusion situations.     

Neural specializations for speech and pitch: moving beyond the dichotomies

The idea that speech processing relies on unique, encapsulated, domain-specific mechanisms has been around for some time. Another well-known idea, often espoused as being in opposition to the first proposal, is that processing of speech sounds entails general-purpose neural mechanisms sensitive to the acoustic features that are present in speech. Here, we suggest that these dichotomous views need not be mutually exclusive. Specifically, there is now extensive evidence that spectral and temporal acoustical properties predict the relative specialization of right and left auditory cortices, and that this is a parsimonious way to account not only for the processing of speech sounds, but also for non-speech sounds such as musical tones. We also point out that there is equally compelling evidence that neural responses elicited by speech sounds can differ depending on more abstract, linguistically relevant properties of a stimulus (such as whether it forms part of one's language or not). Tonal languages provide a particularly valuable window to understand the interplay between these processes. The key to reconciling these phenomena probably lies in understanding the interactions between afferent pathways that carry stimulus information, with top-down processing mechanisms that modulate these processes. Although we are still far from the point of having a complete picture, we argue that moving forward will require us to abandon the dichotomy argument in favour of a more integrated approach.