The structure and size of sensory bursts encode stimulus information but only size affects behavior

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

Neural populations within macaque early vestibular pathways are adapted to encode natural self-motion

How the activities of large neural populations are integrated in the brain to ensure accurate perception and behavior remains a central problem in systems neuroscience. Here, we investigated population coding of naturalistic self-motion by neurons within early vestibular pathways in rhesus macaques (Macacca mulatta). While vestibular neurons displayed similar dynamic tuning to self-motion, inspection of their spike trains revealed significant heterogeneity. Further analysis revealed that, during natural but not artificial stimulation, heterogeneity resulted primarily from variability across neurons as opposed to trial-to-trial variability. Interestingly, vestibular neurons displayed different correlation structures during naturalistic and artificial self-motion. Specifically, while correlations due to the stimulus (i.e., signal correlations) did not differ, correlations between the trial-to-trial variabilities of neural responses (i.e., noise correlations) were instead significantly positive during naturalistic but not artificial stimulation. Using computational modeling, we show that positive noise correlations during naturalistic stimulation benefits information transmission by heterogeneous vestibular neural populations. Taken together, our results provide evidence that neurons within early vestibular pathways are adapted to the statistics of natural self-motion stimuli at the population level. We suggest that similar adaptations will be found in other systems and species.

Silences, spikes and bursts: Three-part knot of the neural code

When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labelling action potentials emitted at a particularly high frequency with a metonym - bursts. Is there more to bursts than a figure of speech? After all, sudden bouts of high-frequency firing are expected to occur whenever inputs surge. The burst coding hypothesis advances that the neural code has three syllables: silences, spikes and bursts. We review evidence supporting this ternary code in terms of devoted mechanisms for burst generation, synaptic transmission and synaptic plasticity. We also review the learning and attention theories for which such a triad is beneficial.

Exploring retinal ganglion cells encoding to multi-modal stimulation using 3D microelectrodes arrays

Microelectrode arrays (MEA) are extensively utilized in encoding studies of retinal ganglion cells (RGCs) due to their capacity for simultaneous recording of neural activity across multiple channels. However, conventional planar MEAs face limitations in studying RGCs due to poor coupling between electrodes and RGCs, resulting in low signal-to-noise ratio (SNR) and limited recording sensitivity. To overcome these challenges, we employed photolithography, electroplating, and other processes to fabricate a 3D MEA based on the planar MEA platform. The 3D MEA exhibited several improvements compared to planar MEA, including lower impedance (8.73 ± 1.66 kΩ) and phase delay (-15.11° ± 1.27°), as well as higher charge storage capacity (CSC = 10.16 ± 0.81 mC/cm2), cathodic charge storage capacity (CSCc = 7.10 ± 0.55 mC/cm2), and SNR (SNR = 8.91 ± 0.57). Leveraging the advanced 3D MEA, we investigated the encoding characteristics of RGCs under multi-modal stimulation. Optical, electrical, and chemical stimulation were applied as sensory inputs, and distinct response patterns and response times of RGCs were detected, as well as variations in rate encoding and temporal encoding. Specifically, electrical stimulation elicited more effective RGC firing, while optical stimulation enhanced RGC synchrony. These findings hold promise for advancing the field of neural encoding.

Sound Intensity-dependent Multiple Tonotopic Organizations and Complex Sub-threshold Alterations of Auditory Response Across Sound Frequencies in the Thalamic Reticular Nucleus

The thalamic reticular nucleus (TRN), a cluster of GABAergic cells, modulates sensory attention and perception through its inhibitory projections to thalamic nuclei. Cortical and thalamic topographic projections to the auditory TRN are thought to compose tonotopic organizations for modulation of thalamic auditory processing. The present study determined tonotopies in the TRN and examined interactions between probe and masker sounds to obtain insights into temporal processing associated with tonotopies. Experiments were performed on anesthetized rats, using juxta-cellular recording and labeling techniques. Following determination of tonotopies, effects of sub-threshold masker sound stimuli on onset and late responses evoked by a probe sound were examined. The main findings are as follows. Tonotopic organizations were recognized in cell location and axonal projection. Tonotopic gradients and their clarities were diverse, depending on sound intensity, response type and the tiers of the TRN. Robust alterations in response magnitude, latency and/or burst spiking took place following masker sounds in either a broad or narrow range of frequencies that were close or far away from the probe sound frequency. The majority of alterations were suppression recognizable up to 600 ms in the interval between masker and probe sounds, and directions of alteration differed depending on the interval. Finally, masker sound effects were associated with tonotopic organizations. These findings suggest that the auditory TRN is comprised of sound intensity-dependent multiple tonotopic organizations, which could configure temporal interactions of auditory information across sound frequencies and impose complex but spatiotemporally structured influences on thalamic auditory processing.

Interspike intervals within retinal spike bursts combinatorially encode multiple stimulus features

Neurons in various regions of the brain generate spike bursts. While the number of spikes within a burst has been shown to carry information, information coding by interspike intervals (ISIs) is less well understood. In particular, a burst with k spikes has k−1 intraburst ISIs, and these k−1 ISIs could theoretically encode k−1 independent values. In this study, we demonstrate that such combinatorial coding occurs for retinal bursts. By recording ganglion cell spikes from isolated salamander retinae, we found that intraburst ISIs encode oscillatory light sequences that are much faster than the light intensity modulation encoded by the number of spikes. When a burst has three spikes, the two intraburst ISIs combinatorially encode the amplitude and phase of the oscillatory sequence. Analysis of trial-to-trial variability suggested that intraburst ISIs are regulated by two independent mechanisms responding to orthogonal oscillatory components, one of which is common to bursts with a different number of spikes. Therefore, the retina encodes multiple stimulus features by exploiting all degrees of freedom of burst spike patterns, i.e., the spike number and multiple intraburst ISIs.

Neurons in various regions of the brain generate spike bursts. Bursts are typically composed of a few spikes generated within dozens of milliseconds, and individual bursts are separated by much longer periods of silence (~hundreds of milliseconds). Recent evidence indicates that the number of spikes in a burst, the interspike intervals (ISIs), and the overall duration of a burst, as well as the timing of burst onset, encode information. However, it remains unknown whether multiple ISIs within a single burst encode multiple input features. Here we demonstrate that such combinatorial ISI coding occurs for spike bursts in the retina. We recorded ganglion cell spikes from isolated salamander retinae stimulated with computer-generated movies. Visual response analyses indicated that multiple ISIs within a single burst combinatorially encode the phase and amplitude of oscillatory light sequences, which are different from the stimulus feature encoded by the spike number. The result demonstrates that the retina encodes multiple stimulus features by exploiting all degrees of freedom of burst spike patterns, i.e., the spike number and multiple intraburst ISIs. Because synaptic transmission in the visual system is highly sensitive to ISIs, the combinatorial ISI coding must have a major impact on visual information processing.

Differences in Sodium Channel Densities in the Apical Dendrites of Pyramidal Cells of the Electrosensory Lateral Line Lobe

Heterogeneity of neural properties within a given neural class is ubiquitous in the nervous system and permits different sub-classes of neurons to specialize for specific purposes. This principle has been thoroughly investigated in the hindbrain of the weakly electric fish A. leptorhynchus in the primary electrosensory area, the Electrosensory Lateral Line lobe (ELL). The pyramidal cells (PCs) that receive inputs from tuberous electroreceptors are organized in three maps in distinct segments of the ELL. The properties of these cells vary greatly across maps due to differences in connectivity, receptor expression, and ion channel composition. These cells are a seminal example of bursting neurons and their bursting dynamic relies on the presence of voltage-gated Na⁺ channels in the extensive apical dendrites of the superficial PCs. Other ion channels can affect burst generation and their expression varies across ELL neurons and segments. For example, SK channels cause hyperpolarizing after-potentials decreasing the likelihood of bursting, yet bursting propensity is similar across segments. We question whether the depolarizing mechanism that generates the bursts presents quantitative differences across segments that could counterbalance other differences having the opposite effect. Although their presence and role are established, the distribution and density of the apical dendrites' Na⁺ channels have not been quantified and compared across ELL maps. Therefore, we test the hypothesis that Na⁺ channel density varies across segment by quantifying their distribution in the apical dendrites of immunolabeled ELL sections. We found the Na⁺ channels to be two-fold denser in the lateral segment (LS) than in the centro-medial segment (CMS), the centro-lateral segment (CLS) being intermediate. Our results imply that this differential expression of voltage-gated Na⁺ channels could counterbalance or interact with other aspects of neuronal physiology that vary across segments (e.g., SK channels). We argue that burst coding of sensory signals, and the way the network regulates bursting, should be influenced by these variations in Na⁺ channel density.

Multisensory enhancement of burst activity in an insect auditory neuron

Detecting predators is crucial for survival. In insects, a few sensory interneurons receiving sensory input from a distinct receptive organ extract specific features informing the animal about approaching predators and mediate avoidance behaviors. Although integration of multiple sensory cues relevant to the predator enhances sensitivity and precision, it has not been established whether the sensory interneurons that act as predator detectors integrate multiple modalities of sensory inputs elicited by predators. Using intracellular recording techniques, we found that the cricket auditory neuron AN2, which is sensitive to the ultrasound-like echolocation calls of bats, responds to airflow stimuli transduced by the cercal organ, a mechanoreceptor in the abdomen. AN2 enhanced spike outputs in response to cross-modal stimuli combining sound with airflow, and the linearity of the summation of multisensory integration depended on the magnitude of the evoked response. The enhanced AN2 activity contained bursts, triggering avoidance behavior. Moreover, cross-modal stimuli elicited larger and longer lasting excitatory postsynaptic potentials (EPSP) than unimodal stimuli, which would result from a sublinear summation of EPSPs evoked respectively by sound or airflow. The persistence of EPSPs was correlated with the occurrence and structure of burst activity. Our findings indicate that AN2 integrates bimodal signals and that multisensory integration rather than unimodal stimulation alone more reliably generates bursting activity. NEW & NOTEWORTHY Crickets detect ultrasound with their tympanum and airflow with their cercal organ and process them as alert signals of predators. These sensory signals are integrated by auditory neuron AN2 in the early stages of sensory processing. Multisensory inputs from different sensory channels enhanced excitatory postsynaptic potentials to facilitate burst firing, which could trigger avoidance steering in flying crickets. Our results highlight the cellular basis of multisensory integration in AN2 and possible effects on escape behavior.

Task-specific sensory coding strategies are matched to detection and discrimination performance

The acquisition of sensory information is limited by the neural encoding method used, constraining perceptual abilities. The most relevant aspects of stimuli may change as behavioral context changes, making efficient encoding of information more challenging. Sensory systems must balance rapid detection of a stimulus with perception of fine details that enable discrimination between similar stimuli. Here, we show that in a species of weakly electric fish, Apteronotus leptorhynchus, two coding strategies are employed for these separate behavioral tasks. Using communication signals, we demonstrate a strong correlation between neural coding strategies and behavioral performance on a discrimination task. Extracellular recordings of pyramidal cells within the electrosensory lateral line lobe of alert fish show two distinct response patterns, either burst discharges with little variation between different signals of the same category, or a graded, heterogeneous response that contains sufficient information to discriminate between signals with slight variations. When faced with a discrimination-based task, the behavioral performance of the fish closely matches predictions based on coding strategy. Comparisons of these results with neural and behavioral responses observed in other model systems suggest that our study highlights a general principle in the way sensory systems utilize different neural codes.

Bursting Neurons in the Hippocampal Formation Encode Features of LFP Rhythms

Burst spike patterns are common in regions of the hippocampal formation such as the subiculum and medial entorhinal cortex (MEC). Neurons in these areas are immersed in extracellular electrical potential fluctuations often recorded as the local field potential (LFP). LFP rhythms within different frequency bands are linked to different behavioral states. For example, delta rhythms are often associated with slow-wave sleep, inactivity and anesthesia; whereas theta rhythms are prominent during awake exploratory behavior and REM sleep. Recent evidence suggests that bursting neurons in the hippocampal formation can encode LFP features. We explored this hypothesis using a two-compartment model of a bursting pyramidal neuron driven by time-varying input signals containing spectral peaks at either delta or theta rhythms. The model predicted a neural code in which bursts represented the instantaneous value, phase, slope and amplitude of the driving signal both in their timing and size (spike number). To verify whether this code is employed in vivo, we examined electrophysiological recordings from the subiculum of anesthetized rats and the MEC of a behaving rat containing prevalent delta or theta rhythms, respectively. In both areas, we found bursting cells that encoded information about the instantaneous voltage, phase, slope and/or amplitude of the dominant LFP rhythm with essentially the same neural code as the simulated neurons. A fraction of the cells encoded part of the information in burst size, in agreement with model predictions. These results provide in-vivo evidence that the output of bursting neurons in the mammalian brain is tuned to features of the LFP.

Thalamic neuron models encode stimulus information by burst-size modulation

Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.

What is the password? Female bark beetles (Scolytinae) grant males access to their galleries based on courtship song

Acoustic signals are commonly used by insects in the context of mating, and signals can vary depending on the stage of interaction between a male and female. While calling songs have been studied extensively, particularly in the Orthoptera, much less is known about courtship songs. One outstanding question is how potential mates are differentiated by their courtship signal characteristics. We examined acoustic courtship signals in a new system, bark beetles (Scolytinae). In the red turpentine beetle (Dendroctonus valens) males produce chirp trains upon approaching the entrance of a female's gallery. We tested the hypotheses that acoustic signals are honest indicators of male condition and that females choose males based on signal characteristics. Males generated two distinct chirp types (simple and interrupted), and variability in their prevalence correlated with an indicator of male quality, body size, with larger males producing significantly more interrupted chirps. Females showed a significant preference for males who produced interrupted chirps, suggesting that females distinguish between males on the basis of their chirp performances. We suggest that interrupted chirps during courtship advertise a male's size and/or motor skills, and function as the proverbial 'passwords' that allow him entry to a female's gallery.

Computational themes of peripheral processing in the auditory pathway of insects

Hearing in insects serves to gain information in the context of mate finding, predator avoidance or host localization. For these goals, the auditory pathways of insects represent the computational substrate for object recognition and localization. Before these higher level computations can be executed in more central parts of the nervous system, the signals need to be preprocessed in the auditory periphery. Here, we review peripheral preprocessing along four computational themes rather than discussing specific physiological mechanisms: (1) control of sensitivity by adaptation, (2) recoding of amplitude modulations of an acoustic signal into a labeled-line code (3) frequency processing and (4) conditioning for binaural processing. Along these lines, we review evidence for canonical computations carried out in the peripheral auditory pathway and show that despite the vast diversity of insect hearing, signal processing is governed by common computational motifs and principles.

Linking dynamical and functional properties of intrinsically bursting neurons

Several studies have shown that bursting neurons can encode information in the number of spikes per burst: As the stimulus varies, so does the length of individual bursts. There presented stimuli, however, vary substantially among different sensory modalities and different neurons.The goal of this paper is to determine which kind of stimulus features can be encoded in burst length, and how those features depend on the mathematical properties of the underlying dynamical system.We show that the initiation and termination of each burst is triggered by specific stimulus features whose temporal characteristsics are determined by the types of bifurcations that initiate and terminate firing in each burst. As only a few bifurcations are possible, only a restricted number of encoded features exists. Here we focus specifically on describing parabolic, square-wave and elliptic bursters. We find that parabolic bursters, whose firing is initiated and terminated by saddle-node bifurcations, behave as prototypical integrators: Firing is triggered by depolarizing stimuli, and lasts for as long as excitation is prolonged. Elliptic bursters, contrastingly, constitute prototypical resonators, since both the initiating and terminating bifurcations possess well-defined oscillation time scales. Firing is therefore triggered by stimulus stretches of matching frequency and terminated by a phase-inversion in the oscillation. The behavior of square-wave bursters is somewhat intermediate, since they are triggered by a fold bifurcation of cycles of well-defined frequency but are terminated by a homoclinic bifurcation lacking an oscillating time scale. These correspondences show that stimulus selectivity is determined by the type of bifurcations. By testing several neuron models, we also demonstrate that additional biological properties that do not modify the bifurcation structure play a minor role in stimulus encoding. Moreover, we show that burst-length variability (and thereby, the capacity to transmit information) depends on a trade-off between the variance of the external signal driving the cell and the strength of the slow internal currents modulating bursts. Thus, our work explicitly links the computational properties of bursting neurons to the mathematical properties of the underlying dynamical systems.

Investigating the computational power of spiking neurons with non-standard behaviors

Spiking neural networks have been called the third generation of neural networks. Their main difference with respect to the previous two generations is the use of realistic neuron models. Their computational power has been well studied with respect to threshold gates and sigmoidal neurons. However, biologically realistic models of spiking neurons can produce behaviors that can be computationally relevant, but their power has not been assessed in the same way. This paper studies the computational power of neurons with different behaviors based on the previous analyses conducted by Maass and Schmitt. The studied behaviors are rebound spiking, resonance and bursting. The results of the analysis are presented. A theoretical motivation for this study is presented and a discussion is done on the possible implications of the findings for using networks of spiking neurons for performing computations.

Bursting neurons and ultrasound avoidance in crickets

Decision making in invertebrates often relies on simple neural circuits composed of only a few identified neurons. The relative simplicity of these circuits makes it possible to identify the key computation and neural properties underlying decisions. In this review, we summarize recent research on the neural basis of ultrasound avoidance in crickets, a response that allows escape from echolocating bats. The key neural property shaping behavioral output is high-frequency bursting of an identified interneuron, AN2, which carries information about ultrasound stimuli from receptor neurons to the brain. AN2's spike train consists of clusters of spikes - bursts - that may be interspersed with isolated, non-burst spikes. AN2 firing is necessary and sufficient to trigger avoidance steering but only high-rate firing, such as occurs in bursts, evokes this response. AN2 bursts are therefore at the core of the computation involved in deciding whether or not to steer away from ultrasound. Bursts in AN2 are triggered by synaptic input from nearly synchronous bursts in ultrasound receptors. Thus the population response at the very first stage of sensory processing - the auditory receptor - already differentiates the features of the stimulus that will trigger a behavioral response from those that will not. Adaptation, both intrinsic to AN2 and within ultrasound receptors, scales the burst-generating features according to the stimulus statistics, thus filtering out background noise and ensuring that bursts occur selectively in response to salient peaks in ultrasound intensity. Furthermore AN2's sensitivity to ultrasound varies adaptively with predation pressure, through both developmental and evolutionary mechanisms. We discuss how this key relationship between bursting and the triggering of avoidance behavior is also observed in other invertebrate systems such as the avoidance of looming visual stimuli in locusts or heat avoidance in beetles.

Bursts and isolated spikes code for opposite movement directions in midbrain electrosensory neurons

Directional selectivity, in which neurons respond strongly to an object moving in a given direction but weakly or not at all to the same object moving in the opposite direction, is a crucial computation that is thought to provide a neural correlate of motion perception. However, directional selectivity has been traditionally quantified by using the full spike train, which does not take into account particular action potential patterns. We investigated how different action potential patterns, namely bursts (i.e. packets of action potentials followed by quiescence) and isolated spikes, contribute to movement direction coding in a mathematical model of midbrain electrosensory neurons. We found that bursts and isolated spikes could be selectively elicited when the same object moved in opposite directions. In particular, it was possible to find parameter values for which our model neuron did not display directional selectivity when the full spike train was considered but displayed strong directional selectivity when bursts or isolated spikes were instead considered. Further analysis of our model revealed that an intrinsic burst mechanism based on subthreshold T-type calcium channels was not required to observe parameter regimes for which bursts and isolated spikes code for opposite movement directions. However, this burst mechanism enhanced the range of parameter values for which such regimes were observed. Experimental recordings from midbrain neurons confirmed our modeling prediction that bursts and isolated spikes can indeed code for opposite movement directions. Finally, we quantified the performance of a plausible neural circuit and found that it could respond more or less selectively to isolated spikes for a wide range of parameter values when compared with an interspike interval threshold. Our results thus show for the first time that different action potential patterns can differentially encode movement and that traditional measures of directional selectivity need to be revised in such cases.

Cellular and circuit properties supporting different sensory coding strategies in electric fish and other systems

Neural codes often seem tailored to the type of information they must carry. Here we contrast the encoding strategies for two different communication signals in electric fish and describe the underlying cellular and network properties that implement them. We compare an aggressive signal that needs to be quickly detected, to a courtship signal whose quality needs to be evaluated. The aggressive signal is encoded by synchronized bursts and a predictive feedback input is crucial in separating background noise from the communication signal. The courtship signal is accurately encoded through a heterogenous population response allowing the discrimination of signal differences. Most importantly we show that the same strategies are used in other systems arguing that they evolved similar solutions because they faced similar tasks.

Coding movement direction by burst firing in electrosensory neurons

Directional selectivity, in which neurons respond strongly to an object moving in a given direction ("preferred") but respond weakly or not at all to an object moving in the opposite direction ("null"), is a critical computation achieved in brain circuits. Previous measures of direction selectivity have compared the numbers of action potentials elicited by each direction of movement, but most sensory neurons display patterning, such as bursting, in their spike trains. To examine the contribution of patterned responses to direction selectivity, we recorded from midbrain neurons in weakly electric fish and found that most neurons responded with a combination of both bursts and isolated spikes to moving object stimuli. In these neurons, we separated bursts and isolated spikes using an interspike interval (ISI) threshold. The directional bias of bursts was significantly higher than that of either the full spike train or the isolated spike train. To examine the encoding and decoding of bursts, we built biologically plausible models that examine 1) the upstream mechanisms that generate these spiking patterns and 2) downstream decoders of bursts. Our model of upstream mechanisms uses an interaction between afferent input and subthreshold calcium channels to give rise to burst firing that occurs preferentially for one direction of movement. We tested this model in vivo by application of calcium antagonists, which reduced burst firing and eliminated the differences in direction selectivity between bursts, isolated spikes, and the full spike train. Our model of downstream decoders used strong synaptic facilitation to achieve qualitatively similar results to those obtained using the ISI threshold criterion. This model shows that direction selective information carried by bursts can be decoded by downstream neurons using biophysically plausible mechanisms.

In vivo conditions influence the coding of stimulus features by bursts of action potentials

The functional role of burst firing (i.e. the firing of packets of action potentials followed by quiescence) in sensory processing is still under debate. Should bursts be considered as unitary events that signal the presence of a particular feature in the sensory environment or is information about stimulus attributes contained within their temporal structure? We compared the coding of stimulus attributes by bursts in vivo and in vitro of electrosensory pyramidal neurons in weakly electric fish by computing correlations between burst and stimulus attributes. Our results show that, while these correlations were strong in magnitude and significant in vitro, they were actually much weaker in magnitude if at all significant in vivo. We used a mathematical model of pyramidal neuron activity in vivo and showed that such a model could reproduce the correlations seen in vitro, thereby suggesting that differences in burst coding were not due to differences in bursting seen in vivo and in vitro. We next tested whether variability in the baseline (i.e. without stimulation) activity of ELL pyramidal neurons could account for these differences. To do so, we injected noise into our model whose intensity was calibrated to mimic baseline activity variability as quantified by the coefficient of variation. We found that this noise caused significant decreases in the magnitude of correlations between burst and stimulus attributes and could account for differences between in vitro and in vivo conditions. We then tested this prediction experimentally by directly injecting noise in vitro through the recording electrode. Our results show that this caused a lowering in magnitude of the correlations between burst and stimulus attributes in vitro and gave rise to values that were quantitatively similar to those seen under in vivo conditions. While it is expected that noise in the form of baseline activity variability will lower correlations between burst and stimulus attributes, our results show that such variability can account for differences seen in vivo. Thus, the high variability seen under in vivo conditions has profound consequences on the coding of information by bursts in ELL pyramidal neurons. In particular, our results support the viewpoint that bursts serve as a detector of particular stimulus features but do not carry detailed information about such features in their structure.