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.     

Efficient olfactory coding in the pheromone receptor neuron of a moth

The concept of coding efficiency holds that sensory neurons are adapted, through both evolutionary and developmental processes, to the statistical characteristics of their natural stimulus. Encouraged by the successful invocation of this principle to predict how neurons encode natural auditory and visual stimuli, we attempted its application to olfactory neurons. The pheromone receptor neuron of the male moth Antheraea polyphemus, for which quantitative properties of both the natural stimulus and the reception processes are available, was selected. We predicted several characteristics that the pheromone plume should possess under the hypothesis that the receptors perform optimally, i.e., transfer as much information on the stimulus per unit time as possible. Our results demonstrate that the statistical characteristics of the predicted stimulus, e.g., the probability distribution function of the stimulus concentration, the spectral density function of the stimulation course, and the intermittency, are in good agreement with those measured experimentally in the field. These results should stimulate further quantitative studies on the evolutionary adaptation of olfactory nervous systems to odorant plumes and on the plume characteristics that are most informative for the ‘sniffer’. Both aspects are relevant to the design of olfactory sensors for odour-tracking robots.     

Estimating receptive fields from responses to natural stimuli with asymmetric intensity distributions

The reasons for using natural stimuli to study sensory function are quickly mounting, as recent studies have revealed important differences in neural responses to natural and artificial stimuli. However, natural stimuli typically contain strong correlations and are spherically asymmetric (i.e. stimulus intensities are not symmetrically distributed around the mean), and these statistical complexities can bias receptive field (RF) estimates when standard techniques such as spike-triggered averaging or reverse correlation are used. While a number of approaches have been developed to explicitly correct the bias due to stimulus correlations, there is no complementary technique to correct the bias due to stimulus asymmetries. Here, we develop a method for RF estimation that corrects reverse correlation RF estimates for the spherical asymmetries present in natural stimuli. Using simulated neural responses, we demonstrate how stimulus asymmetries can bias reverse-correlation RF estimates (even for uncorrelated stimuli) and illustrate how this bias can be removed by explicit correction. We demonstrate the utility of the asymmetry correction method under experimental conditions by estimating RFs from the responses of retinal ganglion cells to natural stimuli and using these RFs to predict responses to novel stimuli.     

Associative fear learning enhances sparse network coding in primary sensory cortex

Several models of associative learning predict that stimulus processing changes during association formation. How associative learning reconfigures neural circuits in primary sensory cortex to "learn" associative attributes of a stimulus remains unknown. Using 2-photon in vivo calcium imaging to measure responses of networks of neurons in primary somatosensory cortex, we discovered that associative fear learning, in which whisker stimulation is paired with foot shock, enhances sparse population coding and robustness of the conditional stimulus, yet decreases total network activity. Fewer cortical neurons responded to stimulation of the trained whisker than in controls, yet their response strength was enhanced. These responses were not observed in mice exposed to a nonassociative learning procedure. Our results define how the cortical representation of a sensory stimulus is shaped by associative fear learning. These changes are proposed to enhance efficient sensory processing after associative learning.     

Central processing of sensory information in electric fish

Summary Comparative studies of neural mechanisms underlying the perception of natural stimulus patterns and the control of adaptive behavioral responses have revealed organizational principles that are shared by a wide spectrum of animals. Mechanisms of perception and motor control are commonly executed in a distributed network of neurons that lack pontifical elements. Individual neurons even at an organizational level as high as the optic tectum may still have very general response characteristics, and the recruitment of individual neurons reveals little about the nature of the stimulus situation outside. Only the joint evaluation of messages from large populations of such neurons yields unambiguous pictures of the outside world. Stimulus variables are commonly mapped continuously within a stratum of neurons so that their variation over time can be monitored by mechanisms similar to motion detection in a retina. The ordered representation of a stimulus variable within an array of broadly tuned elements allows for a degree of stimulus resolution that by far exceeds that of individual elements in the array. Neural systems are burdened by their evolutionary history and suffer from imperfections that are overcome by a patchwork of compensations. The existence of multiple neuronal representations of sensory information and multiple circuits for the control of behavioral responses should provide the necessary freedom for evolutionary tinkering and the invention of new designs.     

To Burst or Not to Burst?

It is well known that some neurons tend to fire packets of action potentials followed by periods of quiescence (bursts) while others within the same stage of sensory processing fire in a tonic manner. However, the respective computational advantages of bursting and tonic neurons for encoding time varying signals largely remain a mystery. Weakly electric fish use cutaneous electroreceptors to convey information about sensory stimuli and it has been shown that some electroreceptors exhibit bursting dynamics while others do not. In this study, we compare the neural coding capabilities of tonically firing and bursting electroreceptor model neurons using information theoretic measures. We find that both bursting and tonically firing model neurons efficiently transmit information about the stimulus. However, the decoding mechanisms that must be used for each differ greatly: a non-linear decoder would be required to extract all the available information transmitted by the bursting model neuron whereas a linear one might suffice for the tonically firing model neuron. Further investigations using stimulus reconstruction techniques reveal that, unlike the tonically firing model neuron, the bursting model neuron does not encode the detailed time course of the stimulus. A novel measure of feature detection reveals that the bursting neuron signals certain stimulus features. Finally, we show that feature extraction and stimulus estimation are mutually exclusive computations occurring in bursting and tonically firing model neurons, respectively. Our results therefore suggest that stimulus estimation and feature extraction might be parallel computations in certain sensory systems rather than being sequential as has been previously proposed.     

Ionic and neuromodulatory regulation of burst discharge controls frequency tuning

Sensory neurons encode natural stimuli by changes in firing rate or by generating specific firing patterns, such as bursts. Many neural computations rely on the fact that neurons can be tuned to specific stimulus frequencies. It is thus important to understand the mechanisms underlying frequency tuning. In the electrosensory system of the weakly electric fish, Apteronotus leptorhynchus, the primary processing of behaviourally relevant sensory signals occurs in pyramidal neurons of the electrosensory lateral line lobe (ELL). These cells encode low frequency prey stimuli with bursts of spikes and high frequency communication signals with single spikes. We describe here how bursting in pyramidal neurons can be regulated by intrinsic conductances in a cell subtype specific fashion across the sensory maps found within the ELL, thereby regulating their frequency tuning. Further, the neuromodulatory regulation of such conductances within individual cells and the consequences to frequency tuning are highlighted. Such alterations in the tuning of the pyramidal neurons may allow weakly electric fish to preferentially select for certain stimuli under various behaviourally relevant circumstances.     

Testing the efficiency of sensory coding with optimal stimulus ensembles

According to Barlow's seminal "efficient coding hypothesis," the coding strategy of sensory neurons should be matched to the statistics of stimuli that occur in an animal's natural habitat. Using an automatic search technique, we here test this hypothesis and identify stimulus ensembles that sensory neurons are optimized for. Focusing on grasshopper auditory receptor neurons, we find that their optimal stimulus ensembles differ from the natural environment, but largely overlap with a behaviorally important sub-ensemble of the natural sounds. This indicates that the receptors are optimized for peak rather than average performance. More generally, our results suggest that the coding strategies of sensory neurons are heavily influenced by differences in behavioral relevance among natural stimuli.     

In vivo and in vitro patch-clamp recording analysis of the process of sensory transmission in the spinal cord and sensory cortex

Following the integration and modification of the sensory inputs in the spinal cord, the information is transmitted to the primary sensory cortex where the integrated information is further processed and perceived. Processing of the sensory information in the spinal cord has been intensively investigated. However, the mechanisms of how the inputs are processed in the cortex are still unclear. To know the correlation of the sensory processing in the dorsal horn and cortex, in vivo and in vitro patch-clamp recordings were made from rat dorsal horn and sensory cortex. Although dorsal horn neurons showed spontaneous and evoked EPSCs by noxious and non-noxious stimuli, most somatosensory neurons located at 100 to 1000 microm from the surface of the cortex exhibited an oscillatory activity and received synaptic inputs from non-noxious but not noxious receptors. These observations suggest that the synaptic responses in cortical neurons are processed in a more complex manner; and this may be due to the reciprocal synaptic connection between thalamus and cortex.     

How behavioral constraints may determine optimal sensory representations

The sensory-triggered activity of a neuron is typically characterized in terms of a tuning curve, which describes the neuron's average response as a function of a parameter that characterizes a physical stimulus. What determines the shapes of tuning curves in a neuronal population? Previous theoretical studies and related experiments suggest that many response characteristics of sensory neurons are optimal for encoding stimulus-related information. This notion, however, does not explain the two general types of tuning profiles that are commonly observed: unimodal and monotonic. Here I quantify the efficacy of a set of tuning curves according to the possible downstream motor responses that can be constructed from them. Curves that are optimal in this sense may have monotonic or nonmonotonic profiles, where the proportion of monotonic curves and the optimal tuning-curve width depend on the general properties of the target downstream functions. This dependence explains intriguing features of visual cells that are sensitive to binocular disparity and of neurons tuned to echo delay in bats. The numerical results suggest that optimal sensory tuning curves are shaped not only by stimulus statistics and signal-to-noise properties but also according to their impact on downstream neural circuits and, ultimately, on behavior.     

Bistability and the dynamics of periodically forced sensory neurons

Many neurons at the sensory periphery receive periodic input, and their activity exhibits entrainment to this input in the form of a preferred phase for firing. This article describes a modeling study of neurons which skip a random number of cycles of the stimulus between firings over a large range of input intensities. This behavior was investigated using analog and digital simulations of the motion of a particle in a double-well with noise and sinusoidal forcing. Well residence-time dis tributions were found to exhibit the main features of the interspike interval histograms (ISIH) measured on real sensory neurons. The conditions under which it is useful to view neurons as simple bistable systems subject to noise are examined by identifying the features of the data which are expected to arise for such systems. This approach is complementary to previous studies of such data based, e.g., on nonhomogeneous point processes. Apart from looking at models which form the backbone of excitable model s, our work allows us to speculate on the role that stochastic resonance, which can arise in this context, may play in the transmission of sensory information. Received: 22 March 1993/Accepted in revised form: 8 September 1993     

A Bayesian Attractor Model for Perceptual Decision Making

Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks.     

Hippocampal representation of touch-guided behavior in rats: persistent and independent traces of stimulus and reward location

Understanding the mechanisms by which sensory experiences are stored remains a compelling challenge for neuroscience. Previous work has described how the activity of neurons in the sensory cortex allows rats to discriminate the physical features of an object contacted with their whiskers. But to date there is no evidence about how neurons represent the behavioural significance of tactile stimuli, or how they are encoded in memory. To investigate these issues, we recorded single-unit firing and local field potentials from the CA1 region of hippocampus while rats performed a task in which tactile stimuli specified reward location. On each trial the rat touched a textured plate with its whiskers, and then turned towards the Left or Right water spout. Two textures were associated with each reward location. To determine the influence of the rat's position on sensory coding, we placed it on a second platform in the same room where it performed the identical texture discrimination task. Over 25 percent of the sampled neurons encoded texture identity--their firing differed for two stimuli associated with the same reward location--and over 50 percent of neurons encoded the reward location with which the stimuli were associated. The neuronal population carried texture and reward location signals continuously, from the moment of stimulus contact until the end of reward collection. The set of neurons discriminating between one texture pair was found to be independent of, and partially overlapping, the set of neurons encoding the discrimination between a different texture pair. In a given neuron, the presence of a tactile signal was uncorrelated with the presence, magnitude, or timing of reward location signals. These experiments indicate that neurons in CA1 form a texture representation independently of the action the stimulus is associated with and retain the stimulus representation through reward collection.     

Task-induced neural covariability as a signature of approximate Bayesian learning and inference

Perception is often characterized computationally as an inference process in which uncertain or ambiguous sensory inputs are combined with prior expectations. Although behavioral studies have shown that observers can change their prior expectations in the context of a task, robust neural signatures of task-specific priors have been elusive. Here, we analytically derive such signatures under the general assumption that the responses of sensory neurons encode posterior beliefs that combine sensory inputs with task-specific expectations. Specifically, we derive predictions for the task-dependence of correlated neural variability and decision-related signals in sensory neurons. The qualitative aspects of our results are parameter-free and specific to the statistics of each task. The predictions for correlated variability also differ from predictions of classic feedforward models of sensory processing and are therefore a strong test of theories of hierarchical Bayesian inference in the brain. Importantly, we find that Bayesian learning predicts an increase in so-called “differential correlations” as the observer’s internal model learns the stimulus distribution, and the observer’s behavioral performance improves. This stands in contrast to classic feedforward encoding/decoding models of sensory processing, since such correlations are fundamentally information-limiting. We find support for our predictions in data from existing neurophysiological studies across a variety of tasks and brain areas. Finally, we show in simulation how measurements of sensory neural responses can reveal information about a subject’s internal beliefs about the task. Taken together, our results reinterpret task-dependent sources of neural covariability as signatures of Bayesian inference and provide new insights into their cause and their function.     

Spatial sensing of stimulus gradients can be superior to temporal sensing for free-swimming bacteria.

Predictions of the minimal size an organism must have to swim along stimulus gradients were used to compare the relative advantages of sensory systems employing spatial (simultaneous) and temporal (sequential) gradient detection mechanisms for small free-swimming bacteria, leading to the following conclusions: 1) there are environmental conditions where spatial detection mechanisms can function for smaller organisms than can temporal mechanisms, 2) temporal mechanisms are superior (have a smaller size limit) for the difficult conditions of low concentration and shallow gradients, but 3) observed bacterial chemotaxis occurs mostly under conditions where spatial mechanisms have a smaller size limit, and 4) relevant conditions in the natural environment favor temporal mechanisms in some cases and spatial mechanisms in others. Thus, sensory ecology considerations do not preclude free-swimming bacteria from employing spatial detection mechanisms, as has been thought, and microbiologists should be on the lookout for them. If spatial mechanisms do not occur, the explanation should be sought elsewhere.     

Spatiotemporal burst coding for extracting features of spatiotemporally varying stimuli

Encoding features of spatiotemporally varying stimuli is quite important for understanding the neural mechanisms of various sensory coding. Temporal coding can encode features of time-varying stimulus, and population coding with temporal coding is adequate for encoding spatiotemporal correlation of stimulus features into spatiotemporal activity of neurons. However, little is known about how spatiotemporal features of stimulus are encoded by spatiotemporal property of neural activity. To address this issue, we propose here a population coding with burst spikes, called here spatiotemporal burst (STB) coding. In STB coding, the temporal variation of stimuli is encoded by the precise onset timing of burst spike, and the spatiotemporal correlation of stimuli is emphasized by one specific aspect of burst firing, or spike packet followed by silent interval. To show concretely the role of STB coding, we study the electrosensory system of a weakly electric fish. Weakly electric fish must perceive the information about an object nearby by analyzing spatiotemporal modulations of electric field around it. On the basis of well-characterized circuitry, we constructed a neural network model of the electrosensory system. Here we show that STB coding encodes well the information of object distance and size by extracting the spatiotemporal correlation of the distorted electric field. The burst activity of electrosensory neurons is also affected by feedback signals through synaptic plasticity. We show that the control of burst activity caused by the synaptic plasticity leads to extracting the stimulus features depending on the stimulus context. Our results suggest that sensory systems use burst spikes as a unit of sensory coding in order to extract spatiotemporal features of stimuli from spatially distributed stimuli.     

Laminar processing in the visual cortical column

Sensory regions of neocortex are organized as arrays of vertical columns composed of cells that share similar response properties, with the orientation columns of the cat's visual cortex being the best known example. Interest in how sensitivity to different stimulus features first emerges in the columns and how this selectivity is refined by subsequent processing has fueled decades of research. A natural starting point in approaching these issues is anatomy. Each column traverses the six cortical layers and each layer has a unique pattern of inputs, intrinsic connections and outputs. Thus, it makes sense to explore the possibility of corresponding laminar differences in sensory function, that is, to examine relationships between morphology and physiology. In addition, to help identify general patterns of cortical organization, it is useful to compare results obtained from different sensory systems and diverse species. The picture that emerges from such comparisons is that each cortical layer serves a distinct role in sensory function. Furthermore, different cortices appear to share some common strategies for processing information but also have specialized mechanisms adapted for the demands of specific sensory tasks.     

Sensory experience modifies spontaneous state dynamics in a large-scale barrel cortical model

Experimental evidence suggests that spontaneous neuronal activity may shape and be shaped by sensory experience. However, we lack information on how sensory experience modulates the underlying synaptic dynamics and how such modulation influences the response of the network to future events. Here we study whether spike-timing-dependent plasticity (STDP) can mediate sensory-induced modifications in the spontaneous dynamics of a new large-scale model of layers II, III and IV of the rodent barrel cortex. Our model incorporates significant physiological detail, including the types of neurons present, the probabilities and delays of connections, and the STDP profiles at each excitatory synapse. We stimulated the neuronal network with a protocol of repeated sensory inputs resembling those generated by the protraction-retraction motion of whiskers when rodents explore their environment, and studied the changes in network dynamics. By applying dimensionality reduction techniques to the synaptic weight space, we show that the initial spontaneous state is modified by each repetition of the stimulus and that this reverberation of the sensory experience induces long-term, structured modifications in the synaptic weight space. The post-stimulus spontaneous state encodes a memory of the stimulus presented, since a different dynamical response is observed when the network is presented with shuffled stimuli. These results suggest that repeated exposure to the same sensory experience could induce long-term circuitry modifications via 'Hebbian' STDP plasticity.     

Spike-based population coding and working memory

Compelling behavioral evidence suggests that humans can make optimal decisions despite the uncertainty inherent in perceptual or motor tasks. A key question in neuroscience is how populations of spiking neurons can implement such probabilistic computations. In this article, we develop a comprehensive framework for optimal, spike-based sensory integration and working memory in a dynamic environment. We propose that probability distributions are inferred spike-per-spike in recurrently connected networks of integrate-and-fire neurons. As a result, these networks can combine sensory cues optimally, track the state of a time-varying stimulus and memorize accumulated evidence over periods much longer than the time constant of single neurons. Importantly, we propose that population responses and persistent working memory states represent entire probability distributions and not only single stimulus values. These memories are reflected by sustained, asynchronous patterns of activity which make relevant information available to downstream neurons within their short time window of integration. Model neurons act as predictive encoders, only firing spikes which account for new information that has not yet been signaled. Thus, spike times signal deterministically a prediction error, contrary to rate codes in which spike times are considered to be random samples of an underlying firing rate. As a consequence of this coding scheme, a multitude of spike patterns can reliably encode the same information. This results in weakly correlated, Poisson-like spike trains that are sensitive to initial conditions but robust to even high levels of external neural noise. This spike train variability reproduces the one observed in cortical sensory spike trains, but cannot be equated to noise. On the contrary, it is a consequence of optimal spike-based inference. In contrast, we show that rate-based models perform poorly when implemented with stochastically spiking neurons.     

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.