Information theoretic analysis of dynamical encoding by four identified primary sensory interneurons in the cricket cercal system

Passive electrolocation in terrestrial arthropods: Theoretical modelling of location detection

The recent discovery that some terrestrial arthropods can detect, use, and learn from weak electrical fields adds a new dimension to our understanding of how organisms explore and interact with their environments. For bees and spiders, the filiform mechanosensory systems enable this novel sensory modality by carrying electric charge and deflecting in response to electrical fields. This mode of information acquisition opens avenues for previously unrealised sensory dynamics and capabilities. In this paper, we study one such potential: the possibility for an arthropod to locate electrically charged objects. We begin by illustrating how electrostatic interactions between hairs and surrounding electrical fields enable the process of location detection. After which we examine three scenarios: (1) the determination of the location and magnitude of multiple point charges through a single observation, (2) the learning of electrical and mechanical sensor properties and the characteristics of an electrical field through several observations, (3) the possibility that an observer can infer their location and orientation in a fixed and known electrical field (akin to "stellar navigation"). To conclude, we discuss the potential of electroreception to endow an animal with thus far unappreciated sensory capabilities, such as the mapping of electrical environments. Electroreception by terrestrial arthropods offers a renewed understanding of the sensory processes carried out by filiform hairs, adding to aero-acoustic sensing and opening up the possibility of new emergent collective dynamics and information acquisition by distributed hair sensors.

Stimulator compensation and generation of Gaussian noise stimuli with defined amplitude spectra for studying input–output relations of sensory systems

Gaussian noise is an important stimulus for the study of biological systems, especially sensory and neural systems. Since these systems are inherently nonlinear, the properties of the noise strongly influence the outcome of the analysis. Therefore, it is crucial to use a well-defined and controlled noise stimulus. In this paper, we first use the example of an insect filiform sensillum, a simple mechanoreceptor with a single sensory cell, to show that changes in the amplitude and spectral properties of the noise stimulus indeed affect the linear transfer function of the sensillum. We then explain step-by-step how to use the inverse fast Fourier transform to generate a Gaussian noise that has an arbitrary user-defined amplitude spectrum, including a band-limited white noise with a perfectly sharp cutoff edge. Finally, we demonstrate how such a perfect band-limited Gaussian white noise stimulus can also be generated with a non-perfect stimulator using a simple procedure that compensates for the filtering properties of the stimulator. With this approach, one can generate well-defined Gaussian noise stimuli that can be adapted to any application. For example, one can generate visual, sound, or vibrational stimuli for experimental research in visual physiology, auditory physiology, and biotremology, as well as inputs for testing various models in theoretical research.

ENCODING OF SMALL-SCALE AIR MOTION DYNAMICS IN THE CRICKET ACHETA DOMESTICUS

The cercal sensory system of the cricket mediates the detection, localization and identification of air current signals generated by predators, mates and competitors. This mechanosensory system has been used extensively for experimental and theoretical studies of sensory coding at the cellular and system levels. It is currently thought that sensory interneurons in the terminal abdominal ganglion extract information about the direction, velocity, and acceleration of the air currents in the animal's immediate environment, and project a coarse-coded representation of those parameters to higher centers. All feature detection is thought to be carried out in higher ganglia by more complex, specialized circuits. We present results that force a substantial revision of current hypotheses. Using multiple extracellular recordings and a special sensory stimulation device, we demonstrate that four well-studied interneurons in this system respond with high sensitivity and selectivity to complex dynamic multi-directional features of air currents which have a spatial scale smaller than the physical dimensions of the cerci. The INs showed much greater sensitivity for these features than for unidirectional bulk-flow stimuli used in previous studies. Thus, in addition to participating in the ensemble encoding of bulk air flow stimulus characteristics, these interneurons are capable of operating as feature detectors for naturalistic stimuli. In this sense, these interneurons are encoding and transmitting information about different aspects of their stimulus environment: they are multiplexing information. Major aspects of the stimulus-response specificity of these interneurons can be understood from the dendritic anatomy and connectivity with the sensory afferent map.

Action selection based on multiple-stimulus aspects in wind-elicited escape behavior of crickets

Escape behavior is essential for animals to avoid attacks by predators. In some species, multiple escape responses could be employed. However, it remains unknown what aspects of threat stimuli affect the choice of an escape response. We focused on two distinct escape responses (running and jumping) to short airflow in crickets and examined the effects of multiple stimulus aspects including the angle, velocity, and duration on the choice between these responses. The faster and longer the airflow, the more frequently the crickets jumped. This meant that the choice of an escape response depends on both the velocity and duration of the stimulus and suggests that the neural basis for choosing an escape response includes the integration process of multiple stimulus parameters. In addition, the moving speed and distance changed depending on the stimulus velocity and duration for running but not for jumping. Running away would be more adaptive escape behavior.

Analysis of aerodynamic and electrostatic sensing in mechanoreceptor arthropod hairs

We study the mechanics of mechanoreceptor hairs in response to electro- and acousto-stimuli to expand the theory of tuning within filiform mechano-sensory systems and show the physical, biological and parametric feasibility of electroreception in comparison to aerodynamic sensing. We begin by analysing two well-known mechanosensory systems, the MeD1 spider trichobothria and the cricket cercal hair, offering a systematic appraisal of the physics of mechanosensory hair motion. Then we explore the biologically relevant parameter space of mechanoreceptor hairs by varying each oscillator parameter, thereby extending the theory to general arthropods. In doing so, we readily identify combinations of parameters for which a hair shows an enhanced or distinct response to either electric or aerodynamic stimuli. Overall, we find distinct behaviours in the two systems with novel insight provided through the parameter-space analysis. We show how the parameter space and balance of parameters therein of the resonant spider system are organised to produce a highly tuneable hair system through variation of hair length, whilst the broader parameter space of the non-resonant cricket system responds equally to a wider range of driving frequencies with increased capacity for high temporal resolution. From our analysis, we hypothesise the existence of two distinct types of mechanoreceptive system: the general system where hairs of all lengths are poised to detect both electro- and acousto- stimuli, and a stimuli-specific system where the sensitivity and specificity of the hairs to the different stimuli changes with length.

Neural tuning and representational geometry

A central goal of neuroscience is to understand the representations formed by brain activity patterns and their connection to behaviour. The classic approach is to investigate how individual neurons encode stimuli and how their tuning determines the fidelity of the neural representation. Tuning analyses often use the Fisher information to characterize the sensitivity of neural responses to small changes of the stimulus. In recent decades, measurements of large populations of neurons have motivated a complementary approach, which focuses on the information available to linear decoders. The decodable information is captured by the geometry of the representational patterns in the multivariate response space. Here we review neural tuning and representational geometry with the goal of clarifying the relationship between them. The tuning induces the geometry, but different sets of tuned neurons can induce the same geometry. The geometry determines the Fisher information, the mutual information and the behavioural performance of an ideal observer in a range of psychophysical tasks. We argue that future studies can benefit from considering both tuning and geometry to understand neural codes and reveal the connections between stimuli, brain activity and behaviour.

Coding strategies in the otolith system differ for translational head motion vs. static orientation relative to gravity

The detection of gravito-inertial forces by the otolith system is essential for our sense of balance and accurate perception. To date, however, how this system encodes the self-motion stimuli that are experienced during everyday activities remains unknown. Here, we addressed this fundamental question directly by recording from single otolith afferents in monkeys during naturalistic translational self-motion and changes in static head orientation. Otolith afferents with higher intrinsic variability transmitted more information overall about translational self-motion than their regular counterparts, owing to stronger nonlinearities that enabled precise spike timing including phase locking. By contrast, more regular afferents better discriminated between different static head orientations relative to gravity. Using computational methods, we further demonstrated that coupled increases in intrinsic variability and sensitivity accounted for the observed functional differences between afferent classes. Together, our results indicate that irregular and regular otolith afferents use different strategies to encode naturalistic self-motion and static head orientation relative to gravity.

Directional and frequency characteristics of auditory neurons in Culex male mosquitoes

The paired auditory organ of mosquito, the Johnston's organ (JO), being the receiver of particle velocity component of sound, is directional by its structure. However, to date almost no physiological measurements of its directionality was done. In addition, the recent finding on the grouping of the JO auditory neurons into the antiphase pairs demanded confirmation by different methods. Using the vector superposition of the signals produced by two orthogonally oriented speakers, we measured the directional characteristics of individual units as well as their relations in physiologically distinguishable groups – pairs or triplets. The feedback stimulation method allowed to discriminate responses of the two simultaneously recorded units, and to show that they indeed responded in antiphase. Units of different frequency tuning as well as high-sensitive units (thresholds of 27 dB SPVL and below) were found in every angular sector of the JO, providing the mosquito with the ability to produce complex auditory behaviors.

Post-molting development of wind-elicited escape behavior in the cricket

Arthropods including insects grow through several developmental stages by molting. The abrupt changes in their body size and morphology accompanying the molting are responsible for the developmental changes in behavior. While in holometabolous insects, larval behaviors are transformed into adult-specific behaviors with drastic changes in nervous system during the pupal stage, hemimetabolous insects preserve most innate behaviors whole life long, which allow us to trace the maturation process of preserved behaviors after the changes in body. Wind-elicited escape behavior is one of these behaviors and mediated by cercal system, which is a mechanosensory organ equipped by all stages of nymph in orthopteran insects like crickets. However, the maturation process of the escape behavior after the molt is unclear. In this study, we examined time-series of changes in the wind-elicited escape behavior just after the imaginal molt in the cricket. The locomotor activities are developed over the elapsed time, and matured 24h after the molt. In contrast, a stimulus-angle dependency of moving direction was unchanged over time, meaning that the cercal sensory system detecting airflow direction was workable immediately after the molt, independent from the behavioral maturation. The post-molting development of the wind-elicited behavior was considered to result not simply from maturation of the exoskeleton or musculature because the escape response to heat-shock stimulus did not change after the molt. No effect of a temporal immobilization after the imaginal molt on the maturation of the wind-elicited behavior also implies that the maturation may be innately programmed without experience of locomotion.

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding.

Spatial dynamics of action potentials estimated by dendritic Ca(2+) signals in insect projection neurons

The spatial dynamics of action potentials, including their propagation and the location of spike initiation zone (SIZ), are crucial for the computation of a single neuron. Compared with mammalian central neurons, the spike dynamics of invertebrate neurons remain relatively unknown. Thus, we examined the spike dynamics based on single spike-induced Ca(2+) signals in the dendrites of cricket mechanosensory projection neurons, known as giant interneurons (GIs). The Ca(2+) transients induced by a synaptically evoked single spike were larger than those induced by an antidromic spike, whereas subthreshold synaptic potentials caused no elevation of Ca(2+). These results indicate that synaptic activity enhances the dendritic Ca(2+) influx through voltage-gated Ca(2+) channels. Stimulation of the presynaptic sensory afferents ipsilateral to the recording site evoked a dendritic spike with higher amplitude than contralateral stimulation, thereby suggesting that alteration of the spike waveform resulted in synaptic enhancement of the dendritic Ca(2+) transients. The SIZ estimated from the spatial distribution of the difference in the Ca(2+) amplitude was distributed throughout the right and left dendritic branches across the primary neurite connecting them in GIs.

Direction-Specific Adaptation in Neuronal and Behavioral Responses of an Insect Mechanosensory System

Stimulus-specific adaptation (SSA) is considered to be the neural underpinning of habituation to frequent stimuli and novelty detection. However, neither the cellular mechanism underlying SSA nor the link between SSA-like neuronal plasticity and behavioral modulation is well understood. The wind-detection system in crickets is one of the best models for investigating the neural basis of SSA. We found that crickets exhibit stimulus-direction-specific adaptation in wind-elicited avoidance behavior. Repetitive air currents inducing this behavioral adaptation reduced firings to the stimulus and the amplitude of excitatory synaptic potentials in wind-sensitive giant interneurons (GIs) related to the avoidance behavior. Injection of a Ca(2+) chelator into GIs diminished both the attenuation of firings and the synaptic depression induced by the repetitive stimulation, suggesting that adaptation of GIs induced by this stimulation results in Ca(2+)-mediated modulation of postsynaptic responses, including postsynaptic short-term depression. Some types of GIs showed specific adaptation to the direction of repetitive stimuli, resulting in an alteration of their directional tuning curves. The types of GIs for which directional tuning was altered displayed heterogeneous direction selectivity in their Ca(2+) dynamics that was restricted to a specific area of dendrites. In contrast, other types of GIs with constant directionality exhibited direction-independent global Ca(2+) elevation throughout the dendritic arbor. These results suggest that depression induced by local Ca(2+) accumulation at repetitively activated synapses of key neurons underlies direction-specific behavioral adaptation. This input-selective depression mediated by heterogeneous Ca(2+) dynamics could confer the ability to detect novelty at the earliest stages of sensory processing in crickets.

SIGNIFICANCE STATEMENT

Stimulus-specific adaptation (SSA) is considered to be the neural underpinning of habituation and novelty detection. We found that crickets exhibit stimulus-direction-specific adaptation in wind-elicited avoidance behavior. Repetitive air currents inducing this behavioral adaptation altered the directional selectivity of wind-sensitive giant interneurons (GIs) via direction-specific adaptation mediated by dendritic Ca(2+) elevation. The GIs for which directional tuning was altered displayed heterogeneous direction selectivity in their Ca(2+) dynamics and the transient increase in Ca(2+) evoked by the repeated puffs was restricted to a specific area of dendrites. These results suggest that depression induced by local Ca(2+) accumulation at repetitively activated synapses of key neurons underlies direction-specific behavioral adaptation. Our findings elucidate the subcellular mechanism underlying SSA-like neuronal plasticity related to behavioral adaptation.

Auditory modulation of wind-elicited walking behavior in the cricket Gryllus bimaculatus

Animals flexibly change their locomotion triggered by an identical stimulus depending on the environmental context and behavioral state. This indicates that additional sensory inputs in different modality from the stimulus triggering the escape response affect the neuronal circuit governing that behavior. However, how the spatio-temporal relationships between these two stimuli effect a behavioral change remains unknown. We studied this question, using crickets, which respond to a short air-puff by oriented walking activity mediated by the cercal sensory system. In addition, an acoustic stimulus, such as conspecific 'song' received by the tympanal organ, elicits a distinct oriented locomotion termed phonotaxis. In this study, we examined the cross-modal effects on wind-elicited walking when an acoustic stimulus was preceded by an air-puff and tested whether the auditory modulation depends on the coincidence of the direction of both stimuli. A preceding 10 kHz pure tone biased the wind-elicited walking in a backward direction and elevated a threshold of the wind-elicited response, whereas other movement parameters, including turn angle, reaction time, walking speed and distance were unaffected. The auditory modulations, however, did not depend on the coincidence of the stimulus directions. A preceding sound consistently altered the wind-elicited walking direction and response probability throughout the experimental sessions, meaning that the auditory modulation did not result from previous experience or associative learning. These results suggest that the cricket nervous system is able to integrate auditory and air-puff stimuli, and modulate the wind-elicited escape behavior depending on the acoustic context.

Ca2+ imaging of cricket protocerebrum responses to air current stimulation

Crickets (Gryllus bimaculatus) use the cercal sensory system at the rear of the abdomen to detect air currents and direct predator avoidance behavior. Sensory information regarding the direction and dynamic properties of air currents is processed within the terminal abdominal ganglion, and conveyed by ascending giant interneurons (GIs) to higher centers including the brain. However, the brain region responsible for decoding cercal sensory information has not yet been identified, nor the response properties within the brain characterized. In this study, we performed in vivo Ca(2+) imaging to investigate wind-evoked neural activities within the cricket protocerebrum. Ca(2+) responses to air current stimuli were observed at peripheral regions of the ventrolateral neuropile (VLNP) where projection of GIs' axon terminals has been observed in larvae. The wind-evoked Ca(2+) response had temporal dynamics and directional sensitivity that varied with different recorded regions displaying transient or sustained Ca(2+) increases. Individual cells showed Ca(2+) elevation in response to air currents from a specific angle, while stimuli from a different angle evoked decreased signals. Removing the antennae reduced the air-current-evoked responses in VLNP, suggesting contribution of sensory inputs from antennae in addition to the cercal inputs. The VLNP is presumably an integrative center for mechanosensory processing from antennae and cerci where directional information is primarily decoded by protocerebral neurons.

Consequences of converting graded to action potentials upon neural information coding and energy efficiency

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na(+) and K(+) channels, with generator potential and graded potential models lacking voltage-gated Na(+) channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na(+) channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a 'footprint' in the generator potential that obscures incoming signals. These three processes reduce information rates by ∼50% in generator potentials, to ∼3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation.

Linking sensory neurons to visually guided behavior: relating MST activity to steering in a virtual environment

Many complex behaviors rely on guidance from sensations. To perform these behaviors, the motor system must decode information relevant to the task from the sensory system. However, identifying the neurons responsible for encoding the appropriate sensory information remains a difficult problem for neurophysiologists. A key step toward identifying candidate systems is finding neurons or groups of neurons capable of representing the stimuli adequately to support behavior. A traditional approach involves quantitatively measuring the performance of single neurons and comparing this to the performance of the animal. One of the strongest pieces of evidence in support of a neuronal population being involved in a behavioral task comes from the signals being sufficient to support behavior. Numerous experiments using perceptual decision tasks show that visual cortical neurons in many areas have this property. However, most visually guided behaviors are not categorical but continuous and dynamic. In this article, we review the concept of sufficiency and the tools used to measure neural and behavioral performance. We show how concepts from information theory can be used to measure the ongoing performance of both neurons and animal behavior. Finally, we apply these tools to dorsal medial superior temporal (MSTd) neurons and demonstrate that these neurons can represent stimuli important to navigation to a distant goal. We find that MSTd neurons represent ongoing steering error in a virtual-reality steering task. Although most individual neurons were insufficient to support the behavior, some very nearly matched the animal's estimation performance. These results are consistent with many results from perceptual experiments and in line with the predictions of Mountcastle's "lower envelope principle."

Nonlinear modeling of dynamic interactions within neuronal ensembles using Principal Dynamic Modes

A methodology for nonlinear modeling of multi-input multi-output (MIMO) neuronal systems is presented that utilizes the concept of Principal Dynamic Modes (PDM). The efficacy of this new methodology is demonstrated in the study of the dynamic interactions between neuronal ensembles in the Pre-Frontal Cortex (PFC) of a behaving non-human primate (NHP) performing a Delayed Match-to-Sample task. Recorded spike trains from Layer-2 and Layer-5 neurons were viewed as the "inputs" and "outputs", respectively, of a putative MIMO system/model that quantifies the dynamic transformation of multi-unit neuronal activity between Layer-2 and Layer-5 of the PFC. Model prediction performance was evaluated by means of computed Receiver Operating Characteristic (ROC) curves. The PDM-based approach seeks to reduce the complexity of MIMO models of neuronal ensembles in order to enable the practicable modeling of large-scale neural systems incorporating hundreds or thousands of neurons, which is emerging as a preeminent issue in the study of neural function. The "scaling-up" issue has attained critical importance as multi-electrode recordings are increasingly used to probe neural systems and advance our understanding of integrated neural function. The initial results indicate that the PDM-based modeling methodology may greatly reduce the complexity of the MIMO model without significant degradation of performance. Furthermore, the PDM-based approach offers the prospect of improved biological/physiological interpretation of the obtained MIMO models.

Temporal precision and reliability in the velocity regime of a hair-cell sensory system: the mechanosensory lateral line of goldfish, Carassius auratus

Fish and aquatic frogs detect minute water motion by means of a specialized mechanosensory system, the lateral line. Ubiquitous in fish, the lateral-line system is characterized by hair-cell based sensory structures across the fish's surface called neuromasts. These neuromasts occur free-standing on the skin as superficial neuromasts (SN) or are recessed into canals as canal neuromasts. SNs respond to rapid changes of water velocity in a small layer of fluid around the fish, including the so-called boundary layer. Although omnipresent, the boundary layer's impact on the SN response is still a matter of debate. For the first time using an information-theoretic approach to this sensory system, we have investigated the SN afferents encoding capabilities. Combining covariance analysis, phase analysis, and modeling of recorded neuronal responses of primary lateral line afferents, we show that encoding by the SNs is adequately described as a linear, velocity-responsive mechanism. Afferent responses display a bimodal distribution of opposite Wiener kernels that likely reflected the two hair-cell populations within a given neuromast. Using frozen noise stimuli, we further demonstrate that SN afferents respond in an extremely precise manner and with high reproducibility across a broad frequency band (10-150 Hz), revealing that an optimal decoder would need to rely extensively on a temporal code. This was further substantiated by means of signal reconstruction of spike trains that were time shifted with respect to their original. On average, a time shift of 3.5 ms was enough to diminish the encoding capabilities of primary afferents by 70%. Our results further demonstrate that the SNs' encoding capability is linearly related to the stimulus outside the boundary layer, and that the boundary layer can, therefore, be neglected while interpreting lateral line response of SN afferents to hydrodynamic stimuli.

Pursuit of food versus pursuit of information in a Markovian perception-action loop model of foraging

Efficient coding, redundancy reduction, and other information theoretic optimization principles have successfully explained the organization of many biological phenomena, from the physiology of sensory receptive fields to the variability of certain DNA sequence ensembles. Here we examine the hypothesis that behavioral strategies that are optimal for survival must necessarily involve efficient information processing, and ask whether there can be circumstances in which deliberately sacrificing some information can lead to higher utility? To this end, we present an analytically tractable model for a particular instance of a perception-action loop: a creature searching for a randomly moving food source confined to a 1D ring world. The model incorporates the statistical structure of the creature's world, the effects of the creature's actions on that structure, and the creature's strategic decision process. The underlying model takes the form of a Markov process on an infinite dimensional state space. To analyze it we construct an exact coarse graining that reduces the model to a Markov process on a finite number of "information states". This mathematical technique allows us to make quantitative comparisons between the performance of an information-theoretically optimal strategy with other candidate search strategies on a food gathering task. We find that 1. Information optimal search does not necessarily optimize utility (expected food gain). 2. The rank ordering of search strategies by information performance does not predict their ordering by expected food obtained. 3. The relative advantage of different strategies depends on the statistical structure of the environment, in particular the variability of motion of the source. We conclude that there is no simple relationship between information and utility. Even in the absence of information processing costs or bandwidth constraints, behavioral optimality does not imply information efficiency, nor is there a simple tradeoff between the two objectives of gaining information about a food source versus obtaining the food itself. For a wide range of values of the food source's movement parameter, the strategy of collecting the most information possible about the unknown source location carries an ineliminable structural cost, leading to a situation in which a foraging creature could actually choose to be less well-informed while simultaneously being, on average, better fed.

Air motion sensing hairs of arthropods detect high frequencies at near-maximal mechanical efficiency

Using measurements based on particle image velocimetry in combination with a novel compact theoretical framework to describe hair mechanics, we found that spider and cricket air motion sensing hairs work close to the physical limit of sensitivity and energy transmission in a broad range of relatively high frequencies. In this range, the hairs closely follow the motion of the incoming flow because a minimum of energy is dissipated by forces acting in their basal articulation. This frequency band is located beyond the frequency at which the angular displacement of the hair is maximum which is between about 40 and 600 Hz, depending on hair length (Barth et al. [1] Phil. Trans. R. Soc. Lond. B 340, 445-461 (doi:10.1098/rstb.1993.0084)). Given that the magnitude of natural airborne signals is known to decrease with frequency, our results point towards the possible existence of spectral signatures in the higher frequency range that may be weak but of biological significance.

Quantitative characterization of the filiform mechanosensory hair array on the cricket cercus

BACKGROUND

Crickets and other orthopteran insects sense air currents with a pair of abdominal appendages resembling antennae, called cerci. Each cercus in the common house cricket Acheta domesticus is approximately 1 cm long, and is covered with 500 to 750 filiform mechanosensory hairs. The distribution of the hairs on the cerci, as well as the global patterns of their movement vectors, have been characterized semi-quantitatively in studies over the last 40 years, and have been shown to be very stereotypical across different animals in this species. Although the cercal sensory system has been the focus of many studies in the areas of neuroethology, development, biomechanics, sensory function and neural coding, there has not yet been a quantitative study of the functional morphology of the receptor array of this important model system.

METHODOLOGY/PRINCIPAL FINDINGS

We present a quantitative characterization of the structural characteristics and functional morphology of the cercal filiform hair array. We demonstrate that the excitatory direction along each hair's movement plane can be identified by features of its socket that are visible at the light-microscopic level, and that the length of the hair associated with each socket can also be estimated accurately from a structural parameter of the socket. We characterize the length and directionality of all hairs on the basal half of a sample of three cerci, and present statistical analyses of the distributions.

CONCLUSIONS/SIGNIFICANCE

The inter-animal variation of several global organizational features is low, consistent with constraints imposed by functional effectiveness and/or developmental processes. Contrary to previous reports, however, we show that the filiform hairs are not re-identifiable in the strict sense.

Identifying temporal codes in spontaneously active sensory neurons

The manner in which information is encoded in neural signals is a major issue in Neuroscience. A common distinction is between rate codes, where information in neural responses is encoded as the number of spikes within a specified time frame (encoding window), and temporal codes, where the position of spikes within the encoding window carries some or all of the information about the stimulus. One test for the existence of a temporal code in neural responses is to add artificial time jitter to each spike in the response, and then assess whether or not information in the response has been degraded. If so, temporal encoding might be inferred, on the assumption that the jitter is small enough to alter the position, but not the number, of spikes within the encoding window. Here, the effects of artificial jitter on various spike train and information metrics were derived analytically, and this theory was validated using data from afferent neurons of the turtle vestibular and paddlefish electrosensory systems, and from model neurons. We demonstrate that the jitter procedure will degrade information content even when coding is known to be entirely by rate. For this and additional reasons, we conclude that the jitter procedure by itself is not sufficient to establish the presence of a temporal code.

Temporal encoding in a nervous system

We examined the extent to which temporal encoding may be implemented by single neurons in the cercal sensory system of the house cricket Acheta domesticus. We found that these neurons exhibit a greater-than-expected coding capacity, due in part to an increased precision in brief patterns of action potentials. We developed linear and non-linear models for decoding the activity of these neurons. We found that the stimuli associated with short-interval patterns of spikes (ISIs of 8 ms or less) could be predicted better by second-order models as compared to linear models. Finally, we characterized the difference between these linear and second-order models in a low-dimensional subspace, and showed that modification of the linear models along only a few dimensions improved their predictive power to parity with the second order models. Together these results show that single neurons are capable of using temporal patterns of spikes as fundamental symbols in their neural code, and that they communicate specific stimulus distributions to subsequent neural structures.

The information coding schemes used within nervous systems have been the focus of an entire field within neuroscience. An unresolved issue within the general coding problem is the determination of the neural “symbols” with which information is encoded in neural spike trains, analogous to the determination of the nucleotide sequences used to represent proteins in molecular biology. The goal of our study was to determine if pairs of consecutive action potentials contain more or different information about the stimuli that elicit them than would be predicted from an analysis of individual action potentials. We developed linear and non-linear coding models and used likelihood analysis to address this question for sensory interneurons in the cricket cercal sensory system. Our results show that these neurons' spike trains can be decomposed into sequences of two neural symbols: isolated single spikes and short-interval spike doublets. Given the ubiquitous nature of similar neural activity reported in other systems, we suspect that the implementation of such temporal encoding schemes may be widespread across animal phyla. Knowledge of the basic coding units used by single cells will help in building the large-scale neural network models necessary for understanding how nervous systems function.

Information transmission and detection thresholds in the vestibular nuclei: single neurons vs. population encoding

Understanding how sensory neurons transmit information about relevant stimuli remains a major goal in neuroscience. Of particular relevance are the roles of neural variability and spike timing in neural coding. Peripheral vestibular afferents display differential variability that is correlated with the importance of spike timing; regular afferents display little variability and use a timing code to transmit information about sensory input. Irregular afferents, conversely, display greater variability and instead use a rate code. We studied how central neurons within the vestibular nuclei integrate information from both afferent classes by recording from a group of neurons termed vestibular only (VO) that are known to make contributions to vestibulospinal reflexes and project to higher-order centers. We found that, although individual central neurons had sensitivities that were greater than or equal to those of individual afferents, they transmitted less information. In addition, their velocity detection thresholds were significantly greater than those of individual afferents. This is because VO neurons display greater variability, which is detrimental to information transmission and signal detection. Combining activities from multiple VO neurons increased information transmission. However, the information rates were still much lower than those of equivalent afferent populations. Furthermore, combining responses from multiple VO neurons led to lower velocity detection threshold values approaching those measured from behavior (∼ 2.5 vs. 0.5-1°/s). Our results suggest that the detailed time course of vestibular stimuli encoded by afferents is not transmitted by VO neurons. Instead, they suggest that higher vestibular pathways must integrate information from central vestibular neuron populations to give rise to behaviorally observed detection thresholds.

Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains

One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.

How synaptic release probability shapes neuronal transmission: information-theoretic analysis in a cerebellar granule cell

A nerve cell receives multiple inputs from upstream neurons by way of its synapses. Neuron processing functions are thus influenced by changes in the biophysical properties of the synapse, such as long-term potentiation (LTP) or depression (LTD). This observation has opened new perspectives on the biophysical basis of learning and memory, but its quantitative impact on the information transmission of a neuron remains partially elucidated. One major obstacle is the high dimensionality of the neuronal input-output space, which makes it unfeasible to perform a thorough computational analysis of a neuron with multiple synaptic inputs. In this work, information theory was employed to characterize the information transmission of a cerebellar granule cell over a region of its excitatory input space following synaptic changes. Granule cells have a small dendritic tree (on average, they receive only four mossy fiber afferents), which greatly bounds the input combinatorial space, reducing the complexity of information-theoretic calculations. Numerical simulations and LTP experiments quantified how changes in neurotransmitter release probability (p) modulated information transmission of a cerebellar granule cell. Numerical simulations showed that p shaped the neurotransmission landscape in unexpected ways. As p increased, the optimality of the information transmission of most stimuli did not increase strictly monotonically; instead it reached a plateau at intermediate p levels. Furthermore, our results showed that the spatiotemporal characteristics of the inputs determine the effect of p on neurotransmission, thus permitting the selection of distinctive preferred stimuli for different p values. These selective mechanisms may have important consequences on the encoding of cerebellar mossy fiber inputs and the plasticity and computation at the next circuit stage, including the parallel fiber-Purkinje cell synapses.

Coding of Stimuli by Ampullary Afferents in Gnathonemus petersii

Weakly electric fish use electroreception for both active and passive electrolocation and for electrocommunication. While both active and passive electrolocation systems are prominent in weakly electric Mormyriform fishes, knowledge of their passive electrolocation ability is still scarce. To better estimate the contribution of passive electric sensing to the orientation toward electric stimuli in weakly electric fishes, we investigated frequency tuning applying classical input-output characterization and stimulus reconstruction methods to reveal the encoding capabilities of ampullary receptor afferents. Ampullary receptor afferents were most sensitive (threshold: 40 μV/cm) at low frequencies (<10 Hz) and appear to be tuned to a mix of amplitude and slope of the input signals. The low-frequency tuning was corroborated by behavioral experiments, but behavioral thresholds were one order of magnitude higher. The integration of simultaneously recorded afferents of similar frequency-tuning resulted in strongly enhanced signal-to-noise ratios and increased mutual information rates but did not increase the range of frequencies detectable by the system. Theoretically the neuronal integration of input from receptors experiencing opposite polarities of a stimulus (left and right side of the fish) was shown to enhance encoding of such stimuli, including an increase of bandwidth. Covariance and coherence analysis showed that spiking of ampullary afferents is sufficiently explained by the spike-triggered average, i.e., receptors respond to a single linear feature of the stimulus. Our data support the notion of a division of labor of the active and passive electrosensory systems in weakly electric fishes based on frequency tuning. Future experiments will address the role of central convergence of ampullary input that we expect to lead to higher sensitivity and encoding power of the system.

Characterizing the fine structure of a neural sensory code through information distortion

We present an application of the information distortion approach to neural coding. The approach allows the discovery of neural symbols and the corresponding stimulus space of a neuron or neural ensemble simultaneously and quantitatively, making few assumptions about the nature of either code or relevant features. The neural codebook is derived by quantitizing sensory stimuli and neural responses into small reproduction sets, and optimizing the quantization to minimize the information distortion function. The application of this approach to the analysis of coding in sensory interneurons involved a further restriction of the space of allowed quantitizers to a smaller family of parametric distributions. We show that, for some cells in this system, a significant amount of information is encoded in patterns of spikes that would not be discovered through analyses based on linear stimulus-response measures.

Neural heterogeneity and efficient population codes for communication signals

Efficient sensory coding implies that populations of neurons should represent information-rich aspects of a signal with little redundancy. Recent studies have shown that neural heterogeneity in higher brain areas enhances the efficiency of encoding by reducing redundancy across the population. Here, we study how neural heterogeneity in the early stages of sensory processing influences the efficiency of population codes. Through the analysis of in vivo recordings, we contrast the encoding of two types of communication signals of electric fishes in the most peripheral sensory area of the CNS, the electrosensory lateral line lobe (ELL). We show that communication signals used during courtship (big chirps) and during aggressive encounters (small chirps) are encoded by different populations of ELL pyramidal cells, namely I-cells and E-cells, respectively. Most importantly, we show that the encoding strategy differs for the two signals and we argue that these differences allow these cell types to encode specifically information-rich features of the signals. Small chirps are detected, and their timing is accurately signaled through stereotyped spike bursts, whereas the shape of big chirps is accurately represented by variable increases in firing rate. Furthermore, we show that the heterogeneity across I-cells enhances the efficiency of the population code and thus permits the accurate discrimination of different quality courtship signals. Our study shows the importance of neural heterogeneity early in a sensory system and that it initiates the sparsification of sensory representation thereby contributing to the efficiency of the neural code.

Temporal Coding by Populations of Auditory Receptor Neurons

Auditory receptor neurons of crickets are most sensitive to either low or high sound frequencies. Earlier work showed that the temporal coding properties of first-order auditory interneurons are matched to the temporal characteristics of natural low- and high-frequency stimuli (cricket songs and bat echolocation calls, respectively). We studied the temporal coding properties of receptor neurons and used modeling to investigate how activity within populations of low- and high-frequency receptors might contribute to the coding properties of interneurons. We confirm earlier findings that individual low-frequency-tuned receptors code stimulus temporal pattern poorly, but show that coding performance of a receptor population increases markedly with population size, due in part to low redundancy among the spike trains of different receptors. By contrast, individual high-frequency-tuned receptors code a stimulus temporal pattern fairly well and, because their spike trains are redundant, there is only a slight increase in coding performance with population size. The coding properties of low- and high-frequency receptor populations resemble those of interneurons in response to low- and high-frequency stimuli, suggesting that coding at the interneuron level is partly determined by the nature and organization of afferent input. Consistent with this, the sound-frequency-specific coding properties of an interneuron, previously demonstrated by analyzing its spike train, are also apparent in the subthreshold fluctuations in membrane potential that are generated by synaptic input from receptor neurons.

Nitric oxide-cyclic guanosine monophosphate signaling in the local circuit of the cricket abdominal nervous system

The distribution of potential nitric oxide (NO) donor neurons and NO-responsive target neurons was revealed in the terminal abdominal ganglion (TAG) of the cricket. The expression of nitric oxide synthase (NOS) in the nervous system was examined by Western blotting using universal nitric oxide synthase (uNOS) antibody that gave about a 130 kDa protein band. Immunohistochemistry using the uNOS antibody detected neurons whose cell bodies are located at the lateral region of the TAG. These neurons expanded their neuronal branches into the dorsal-median region or the dorsal-lateral region of the TAG. NADPH-diaphorase histochemistry was performed to confirm the distribution of NOS-containing neurons. The distributions of cell bodies and stained neuronal branches were similar to those revealed by uNOS immunohistochemistry. NO-induced cGMP immunohistochemistry was performed to reveal NO-responsive target neurons. Most of the cell bodies stained by immunohistochemistry appeared at the dorsal side of the TAG. At the dorsal-median region, some unpaired neuronal cell bodies were strongly stained. Some efferent neurons whose axon innervate into each nerve root were strongly stained. The generation of NO in the TAG was detected by NO electrode. We found that NO is generally produced to maintain a basal concentration of 70 nM. Hemoglobin scavenged released NO from the ganglion. The concentration of NO was partly recovered when hemoglobin was replaced by normal saline. Application of 10 microM L-arginine that is a substrate of NOS increased NO release by approximately 10 nM. Furthermore, an excitatory neurotransmitter acetylcholine (ACh) also increased NO generation by approximately 40-50 nM in concentration in addition to the basal level of 70 nM. Optical imaging with fluorescent NO-indicator demonstrated that ACh-induced enhancement of NO release was transiently observed in the outer-edge region of TAG, where cell bodies of NOS-immunoreactive neurons were located. These results suggest that ACh accelerates NO production via neuronal events activated by ACh in the TAG.

Dendritic design implements algorithm for synaptic extraction of sensory information

While sensory information is encoded by firing patterns of individual sensory neurons, it is also represented by spatiotemporal patterns of activity in populations of the neurons. Postsynaptic interneurons decode the population response and extract specific sensory information. This extraction of information represented by presynaptic activities is a process critical to defining the input-output function of postsynaptic neuron. To understand the "algorithm" for the extraction, we examined directional sensitivities of presynaptic and postsynaptic Ca(2+) responses in dendrites of two types of wind-sensitive interneurons (INs) with different dendritic geometries in the cricket cercal sensory system. In IN 10-3, whose dendrites arborize with various electrotonic distances to the spike-initiating zone (SIZ), the directional sensitivity of dendritic Ca(2+) responses corresponded to those indicated by Ca(2+) signals in presynaptic afferents arborizing on that dendrite. The directional tuning properties of individual dendrites varied from each other, and the directional sensitivity of the nearest dendrite to the SIZ dominates the tuning properties of the spiking response. In IN 10-2 with dendrites isometric to the SIZ, directional tuning properties of different dendrites were similar to each other, and each response property could be explained by the directional profile of the spatial overlap between that dendrite and Ca(2+)-elevated presynaptic terminals. For IN 10-2, the directional sensitivities extracted by the different dendritic-branches would contribute equally to the overall tuning. It is possible that the differences in the distribution of synaptic weights because of the dendritic geometry are related to the algorithm for extraction of sensory information in the postsynaptic interneurons.

Computational mechanisms of mechanosensory processing in the cricket

Crickets and many other orthopteran insects face the challenge of gathering sensory information from the environment from a set of multi-modal sensory organs and transforming these stimuli into patterns of neural activity that can encode behaviorally relevant stimuli. The cercal mechanosensory system transduces low frequency air movements near the animal's body and is involved in many behaviors including escape from predators, orientation with respect to gravity, flight steering, aggression and mating behaviors. Three populations of neurons are sensitive to both the direction and dynamics of air currents:an array of mechanoreceptor-coupled sensory neurons, identified local interneurons and identified projection interneurons. The sensory neurons form a functional map of air current direction within the central nervous system that represents the direction of air currents as three-dimensional spatio-temporal activity patterns. These dynamic activity patterns provide excitatory input to interneurons whose sensitivity and spiking output depend on the location of the neuronal arbors within the sensory map and the biophysical and electronic properties of the cell structure. Sets of bilaterally symmetric interneurons can encode the direction of an air current stimulus by their ensemble activity patterns, functioning much like a Cartesian coordinate system. These interneurons are capable of responding to specific dynamic stimuli with precise temporal patterns of action potentials that may encode these stimuli using temporal encoding schemes. Thus, a relatively simple mechanosensory system employs a variety of complex computational mechanisms to provide the animal with relevant information about its environment.

Information Rate and Spike-Timing Precision of Proprioceptive Afferents

Proprioception in the first two joints of crustacean limbs is mediated by chordotonal organs that utilize spike-mediated information coding and transmission and by nonspiking proprioceptive afferents that use graded transmission at information rates in excess of 2,500 bits/s. Chordotonal organs operate in parallel with the graded receptors, but the information rates of the spiking chordotonal afferents have not been previously determined. Lower-bound estimates of chordotonal afferent information rates were calculated using stimulus reconstruction, which assumes linear encoding of the stimulus. The information rate was also directly estimated from the spike train entropy, which makes no a priori assumptions with respect to the coding scheme used by the system. Lower-bound information rate estimates ranged from 43 to 69 bits/s, whereas the direct estimates ranged from 24 to 278 bits/s. Comparison of both estimates derived from the same data set indicates that a linear decoder could recover an average of 59% of the information from the spike train. Afferent spike timing was found to be extremely precise, with spikes evoked with an average timing jitter of 0.55 ms. Information rate was correlated with the mean jitter and the noise entropy of the spike train could be predicted from the mean firing rate and mean jitter. Direct stimulation of single afferents by current injection into the soma revealed that the average timing jitter was <0.1 ms, indicating that intrinsic membrane properties, spike generation, and mechanotransduction mechanisms are the major sources of timing jitter in this system.

Interaction between arthropod filiform hairs in a fluid environment

Many arthropods use filiform hairs as mechanoreceptors to detect air motion. In common house crickets (Acheta domestica) the hairs cover two antenna-like appendages called cerci at the rear of the abdomen. The biomechanical stimulus-response properties of individual filiform hairs have been investigated and modeled extensively in several earlier studies. However, only a few previous studies have considered viscosity-mediated coupling between pairs of hairs, and only in particular configurations. Here, we present a model capable of calculating hair-to-hair coupling in arbitrary configurations. We simulate the coupled motion of a small group of mechanosensory hairs on a cylindrical section of cercus. We have found that the coupling effects are non-negligible, and likely constrain the operational characteristics of the cercal sensory array.

Neural variability, detection thresholds, and information transmission in the vestibular system

A fundamental issue in neural coding is the role of spike timing variation in information transmission of sensory stimuli. Vestibular afferents are particularly well suited to study this issue because they are classified as either regular or irregular based on resting discharge variability as well as morphology. Here, we compared the responses of each afferent class to sinusoidal and random head rotations using both information theoretic and gain measures. Information theoretic measures demonstrated that regular afferents transmitted, on average, two times more information than irregular afferents, despite having significantly lower gains. Moreover, consistent with information theoretic measures, regular afferents had angular velocity detection thresholds that were 50% lower than those of irregular afferents (approximately 4 vs 8 degrees/s). Finally, to quantify the information carried by spike times, we added spike-timing jitter to the spike trains of both regular and irregular afferents. Our results showed that this significantly reduced information transmitted by regular afferents whereas it had little effect on irregular afferents. Thus, information is carried in the spike times of regular but not irregular afferents. Using a simple leaky integrate and fire model with a dynamic threshold, we show that differential levels of intrinsic noise can explain differences in the resting discharge, the responses to sensory stimuli, as well as the information carried by action potential timings of each afferent class. Our experimental and modeling results provide new insights as to how neural variability influences the strategy used by two different classes of sensory neurons to encode behaviorally relevant stimuli.

Flexible multielectrodes can resolve multiple muscles in an insect appendage

Research into the neuromechanical basis of behavior, either in biomechanics, neuroethology, or neuroscience, is frequently limited by methods of data collection. Two of the most pressing needs are for methods with which to (1) record from multiple neurons or muscles simultaneously and (2) perform this recording in intact, behaving animals. In this paper we present the fabrication and testing of flexible multielectrode arrays (fMEAs) that move us significantly towards these goals. The fMEAs were used to record the activity of several distinct units in the coxa of the cockroach Blaberus discoidalis. The devices fabricated here address the first goal in two ways: (1) their flexibility allows them to be inserted into an animal and guided through internal tissues in order to access distinct groups of neurons and muscles and (2) their recording site geometry has been tuned to suit the anatomy under study, yielding multichannel spike waveforms that are easily separable under conditions of spike overlap. The flexible nature of the devices simultaneously addresses the second goal, in that it is less likely to interfere with the natural movement of the animal.

Multiple thresholds in a model system of noisy ion channels

Voltage-activated ion channels vary randomly between open and closed states, influenced by the membrane potential and other factors. Signal transduction is enhanced by noise in a simple ion channel model. The enhancement occurs in a finite range of signals; the range can be extended using populations of channels. The range increases more rapidly in multiple-threshold channel populations than in single-threshold populations. The diversity of ion channels may thus be present as a strategy to reduce the metabolic costs of handling a broad class of electrochemical signals.

Encoding of Naturalistic Optic Flow by a Population of Blowfly Motion-Sensitive Neurons

In sensory systems information is encoded by the activity of populations of neurons. To analyze the coding properties of neuronal populations sensory stimuli have usually been used that were much simpler than those encountered in real life. It has been possible only recently to stimulate visual interneurons of the blowfly with naturalistic visual stimuli reconstructed from eye movements measured during free flight. Therefore we now investigate with naturalistic optic flow the coding properties of a small neuronal population of identified visual interneurons in the blowfly, the so-called VS and HS neurons. These neurons are motion sensitive and directionally selective and are assumed to extract information about the animal's self-motion from optic flow. We could show that neuronal responses of VS and HS neurons are mainly shaped by the characteristic dynamical properties of the fly's saccadic flight and gaze strategy. Individual neurons encode information about both the rotational and the translational components of the animal's self-motion. Thus the information carried by individual neurons is ambiguous. The ambiguities can be reduced by considering neuronal population activity. The joint responses of different subpopulations of VS and HS neurons can provide unambiguous information about the three rotational and the three translational components of the animal's self-motion and also, indirectly, about the three-dimensional layout of the environment.

Representation of behaviourally relevant information by blowfly motion-sensitive visual interneurons requires precise compensatory head movements

Flying blowflies shift their gaze by saccadic turns of body and head, keeping their gaze basically fixed between saccades. For the head, this results in almost pure translational optic flow between saccades, enabling visual interneurons in the fly motion pathway to extract information about translation of the animal and thereby about the spatial layout of the environment. There are noticeable differences between head and body movements during flight. Head saccades are faster and shorter than body saccades, and the head orientation is more stable between saccades than the body orientation. Here, we analyse the functional importance of these differences by probing visual interneurons of the blowfly motion pathway with optic flow based on either head movements or body movements, as recorded accurately with a magnetic search coil technique. We find that the precise head-body coordination is essential for the visual system to separate the translational from the rotational optic flow. If the head were tightly coupled to the body, the resulting optic flow would not contain the behaviourally important information on translation. Since it is difficult to resolve head orientation in many experimental paradigms, even when employing state-of-the-art digital video techniques, we introduce a 'headifying algorithm', which transforms the time-dependent body orientation in free flight into an estimate of head orientation. We show that application of this algorithm leads to an estimated head orientation between saccades that is sufficiently stable to enable recovering information on translation. The algorithm may therefore be of practical use when head orientation is needed but cannot be measured.

Nonlinear information processing in a model sensory system

Understanding the mechanisms by which sensory neurons encode and decode information remains an important goal in neuroscience. We quantified the performance of optimal linear and nonlinear encoding models in a well-characterized sensory system: the electric sense of weakly electric fish. We show that linear encoding models generally perform better under spatially localized stimulation than under spatially diffuse stimulation. Through pharmacological blockade of feedback input and spatial saturation of the receptive field center, we show that there is significantly less synaptic noise under spatially diffuse stimuli as compared with spatially localized stimuli. Modeling results suggest that pyramidal cells nonlinearly encode sensory information through shunting in their dendrites and clarify the influence of synaptic noise on the performance of linear encoding models. Finally, we used information theory to quantify the performance of linear decoders. While the optimal linear decoder for spatially localized stimuli could capture 60% of the information in pyramidal cell spike trains, the optimal linear decoder for spatially diffuse stimuli could only capture 40% of the information. These results show that nonlinear decoders are necessary to fully access information in pyramidal cell spike trains, and we discuss potential mechanisms by which higher-order neurons could decode this information.

Effects of stimulus transformations on estimates of sensory neuron selectivity

Stimulus selectivity of sensory systems is often characterized by analyzing response-conditioned stimulus ensembles. However, in many cases these response-triggered stimulus sets have structure that is more complex than assumed. If not taken into account, when present it will bias the estimates of many simple statistics, and distort the estimated stimulus selectivity of a neural sensory system. We present an approach that mitigates these problems by modeling some of the response-conditioned stimulus structure as being generated by a set of transformations acting on a simple stimulus distribution. This approach corrects the estimates of key statistics and counters biases introduced by the transformations. In cases involving temporal spike jitter or spatial jitter of images, the main observed effects of transformations are blurring of the conditional mean and introduction of artefacts in the spectral decomposition of the conditional covariance matrix. We illustrate this approach by analyzing and correcting a set of model stimuli perturbed by temporal and spatial jitter. We apply the approach to neurophysiological data from the cricket cercal sensory system to correct the effects of temporal jitter.

Visualization of ensemble activity patterns of mechanosensory afferents in the cricket cercal sensory system with calcium imaging

The cercal sensory system of the cricket mediates the detection and analysis of low velocity air currents in the animal's immediate environment, and is implemented around an internal representation of air current direction that demonstrates the essential features of a continuous neural map. Previous neurophysiological and anatomical studies have yielded predictions of the global spatio-temporal patterns of activity that should be evoked in the sensory afferent map by air current stimuli of different directions. We tested those predictions by direct visualization of ensemble afferent activity patterns using Ca2+ -sensitive indicators. The AM ester of the fluorescent Ca2+ indicator (Oregon Green 488 BAPTA-1 AM) was injected under the sheath of a cercal sensory nerve containing all of the mechanosensory afferent axons from one cercus. Optical signals were recorded with a digital intensified CCD camera. Control experiments using direct electrical stimulation of stained and unstained nerves demonstrated that the observed Ca2+ signals within the terminal abdominal ganglion (TAG) were due to activation of the dye-loaded sensory afferent neurons. To visualize the spatial patterns of air-current-evoked ensemble activity, unidirectional air currents were applied repeatedly from eight different directions, and the optically recorded responses from each direction were averaged. The dispersion of the optical signals by the ganglion limited the spatial resolution with which these ensemble afferent activity patterns could be observed. However, resolution was adequate to demonstrate that different directional stimuli induced different spatial patterns of Ca2+ elevation in the terminal arbors of afferents within the TAG. These coarsely- resolved, optically-recorded patterns were consistent with the anatomy-based predictions.

Dejittered spike-conditioned stimulus waveforms yield improved estimates of neuronal feature selectivity and spike-timing precision of sensory interneurons

What is the meaning associated with a single action potential in a neural spike train? The answer depends on the way the question is formulated. One general approach toward formulating this question involves estimating the average stimulus waveform preceding spikes in a spike train. Many different algorithms have been used to obtain such estimates, ranging from spike-triggered averaging of stimuli to correlation-based extraction of "stimulus-reconstruction" kernels or spatiotemporal receptive fields. We demonstrate that all of these approaches miscalculate the stimulus feature selectivity of a neuron. Their errors arise from the manner in which the stimulus waveforms are aligned to one another during the calculations. Specifically, the waveform segments are locked to the precise time of spike occurrence, ignoring the intrinsic "jitter" in the stimulus-to-spike latency. We present an algorithm that takes this jitter into account. "Dejittered" estimates of the feature selectivity of a neuron are more accurate (i.e., provide a better estimate of the mean waveform eliciting a spike) and more precise (i.e., have smaller variance around that waveform) than estimates obtained using standard techniques. Moreover, this approach yields an explicit measure of spike-timing precision. We applied this technique to study feature selectivity and spike-timing precision in two types of sensory interneurons in the cricket cercal system. The dejittered estimates of the mean stimulus waveforms preceding spikes were up to three times larger than estimates based on the standard techniques used in previous studies and had power that extended into higher-frequency ranges. Spike timing precision was approximately 5 ms.

Effect of the temporal pattern of contralateral inhibition on sound localization cues

We studied the temporal coding properties of identified interneurons in the auditory system of crickets, using information theory as an analytical tool. The ascending neuron 1 (AN1), which is tuned to the dominant carrier frequency (CF) of cricket songs, selectively codes the limited range of amplitude modulation (AM) frequencies that occur in these signals. AN2, which is most sensitive to the ultrasonic frequencies that occur in echolocation calls of insectivorous bats, codes a broader range of AM frequencies, as occur in bat calls. A third neuron, omega neuron 1 (ON1), which is dually tuned to both ranges of carrier frequency, was shown previously to have CF-specific coding properties, allowing it to represent accurately the differing temporal structures of both cricket songs and bat calls. ON1 is a source of contralateral inhibition to AN1 and AN2, enhancing binaural contrast and facilitating sound localization. We used dichotic stimulation to examine the importance of the temporal structure of contralateral inhibition for enhancing binaural contrast. Contralateral inhibition degrades the coding of temporal pattern by AN1 and AN2, but only if the temporal pattern of inhibitory input matches that of excitation. Firing rate is also decreased most strongly by temporally matched contralateral inhibition. This is apparent for AN1 in its mean firing rate; for AN2, high-frequency firing is selectively suppressed. Our results show that the CF-specific coding properties of ON1 allow this single neuron to enhance effectively localization cues for both cricket-like and bat-like acoustic signals.

Approaches to Information-Theoretic Analysis of Neural Activity

Understanding how neurons represent, process, and manipulate information is one of the main goals of neuroscience. These issues are fundamentally abstract, and information theory plays a key role in formalizing and addressing them. However, application of information theory to experimental data is fraught with many challenges. Meeting these challenges has led to a variety of innovative analytical techniques, with complementary domains of applicability, assumptions, and goals.

Neuronal control of leech behavior

The medicinal leech has served as an important experimental preparation for neuroscience research since the late 19th century. Initial anatomical and developmental studies dating back more than 100 years ago were followed by behavioral and electrophysiological investigations in the first half of the 20th century. More recently, intense studies of the neuronal mechanisms underlying leech movements have resulted in detailed descriptions of six behaviors described in this review; namely, heartbeat, local bending, shortening, swimming, crawling, and feeding. Neuroethological studies in leeches are particularly tractable because the CNS is distributed and metameric, with only 400 identifiable, mostly paired neurons in segmental ganglia. An interesting, yet limited, set of discrete movements allows students of leech behavior not only to describe the underlying neuronal circuits, but also interactions among circuits and behaviors. This review provides descriptions of six behaviors including their origins within neuronal circuits, their modification by feedback loops and neuromodulators, and interactions between circuits underlying with these behaviors.