Behaviorally relevant burst coding in primary sensory neurons

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.

Detection of Activation Sequences in Spiking-Bursting Neurons by means of the Recognition of Intraburst Neural Signatures

Bursting activity is present in many cells of different nervous systems playing important roles in neural information processing. Multiple assemblies of bursting neurons act cooperatively to produce coordinated spatio-temporal patterns of sequential activity. A major goal in neuroscience is unveiling the mechanisms underlying neural information processing based on this sequential dynamics. Experimental findings have revealed the presence of precise cell-type-specific intraburst firing patterns in the activity of some bursting neurons. This characteristic neural signature coexists with the information encoded in other aspects of the spiking-bursting signals, and its functional meaning is still unknown. We investigate the ability of a neuron conductance-based model to detect specific presynaptic activation sequences taking advantage of intraburst fingerprints identifying the source of the signals building up a sequential pattern of activity. Our simulations point out that a reader neuron could use this information to contextualize incoming signals and accordingly compute a characteristic response by relying on precise phase relationships among the activity of different emitters. This would provide individual neurons enhanced capabilities to control and negotiate sequential dynamics. In this regard, we discuss the possible implications of the proposed contextualization mechanism for neural information processing.

Adaptive coding for dynamic sensory inference

Behavior relies on the ability of sensory systems to infer properties of the environment from incoming stimuli. The accuracy of inference depends on the fidelity with which behaviorally relevant properties of stimuli are encoded in neural responses. High-fidelity encodings can be metabolically costly, but low-fidelity encodings can cause errors in inference. Here, we discuss general principles that underlie the tradeoff between encoding cost and inference error. We then derive adaptive encoding schemes that dynamically navigate this tradeoff. These optimal encodings tend to increase the fidelity of the neural representation following a change in the stimulus distribution, and reduce fidelity for stimuli that originate from a known distribution. We predict dynamical signatures of such encoding schemes and demonstrate how known phenomena, such as burst coding and firing rate adaptation, can be understood as hallmarks of optimal coding for accurate inference.

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.

Neuronal processing of noxious thermal stimuli mediated by dendritic Ca(2+) influx in Drosophila somatosensory neurons

Adequate responses to noxious stimuli causing tissue damages are essential for organismal survival. Class IV neurons in Drosophila larvae are polymodal nociceptors responsible for thermal, mechanical, and light sensation. Importantly, activation of Class IV provoked distinct avoidance behaviors, depending on the inputs. We found that noxious thermal stimuli, but not blue light stimulation, caused a unique pattern of Class IV, which were composed of pauses after high-frequency spike trains and a large Ca²⁺ rise in the dendrite (the Ca²⁺ transient). Both these responses depended on two TRPA channels and the L-type voltage-gated calcium channel (L-VGCC), showing that the thermosensation provokes Ca²⁺ influx. The precipitous fluctuation of firing rate in Class IV neurons enhanced the robust heat avoidance. We hypothesize that the Ca²⁺ influx can be a key signal encoding a specific modality.

DOI:http://dx.doi.org/10.7554/eLife.12959.001

Animals often need to get away quickly from dangers in their environment, such as temperatures that are hot enough to damage their tissues. As such, an animal’s brain often encodes automatic ‘avoidance responses’ to signs of danger, which help the animal move away from harm.

The nervous system of a fruit fly larva, for example, contains a distinct class of neurons (known as class IV neurons) that respond specifically to high temperatures and ultraviolet or blue light. Both of these stimuli are potentially harmful, but the larvae escape from heat by rolling with a corkscrew-like motion, yet they turn their heads away from a source of ultraviolet or blue light. So, how does the same set of neurons orchestrate these two different types of behavior?

To answer this question, Terada, Matsubara, Onodera et al. measured the activity in the class IV neurons in two different ways. First, the levels of calcium ions in the neurons, which play a key role in neurons’ activity, were imaged using a calcium-sensitive biosensor. Second, electrodes were used to directly on the class IV neurons to record changes in their electrical activity.

The experiments showed that class IV neurons responded to heat by producing a characteristic burst of electrical activity followed by a pause, and that this pattern of electrical activity was accompanied by a large rise in the calcium signal. In contrast, the same neurons did not show this ‘burst and pause’ pattern of activity when the fruit fly larvae were exposed to ultraviolet/blue light. Instead, these conditions triggered much smaller changes in electrical activity. Further experiments then confirmed that the characteristic ‘burst and pause’ pattern of electrical activity was linked to the rolling motion observed when the larvae try to escape from heat.

These findings show how differing patterns of activity in the same neurons can be used to differentiate between different types of stimuli. Further work is now needed to explain how these two different patterns of activity in one set of neurons is translated by the fruit fly’s brain into different patterns of behavior.

DOI:http://dx.doi.org/10.7554/eLife.12959.002

Burst Firing in a Motion-Sensitive Neural Pathway Correlates with Expansion Properties of Looming Objects that Evoke Avoidance Behaviors

The locust visual system contains a well-defined motion-sensitive pathway that transfers visual input to motor centers involved in predator evasion and collision avoidance. One interneuron in this pathway, the descending contralateral movement detector (DCMD), is typically described as using rate coding; edge expansion of approaching objects causes an increased rate of neuronal firing that peaks after a certain retinal threshold angle is exceeded. However, evidence of intrinsic DCMD bursting properties combined with observable oscillations in mean firing rates and tight clustering of spikes in raw traces, suggest that bursting may be important for motion detection. Sensory neuron bursting provides important timing information about dynamic stimuli in many model systems, yet no studies have rigorously investigated if bursting occurs in the locust DCMD during object approach. We presented repetitions of 30 looming stimuli known to generate behavioral responses to each of 20 locusts in order to identify and quantify putative bursting activity in the DCMD. Overall, we found a bimodal distribution of inter-spike intervals (ISI) with peaks of more frequent and shorter ISIs occurring from 1–8 ms and longer less frequent ISIs occurring from 40–50 ms. Subsequent analysis identified bursts and isolated single spikes from the responses. Bursting frequency increased in the latter phase of an approach and peaked at the time of collision, while isolated spiking was predominant during the beginning of stimulus approach. We also found that the majority of inter-burst intervals (IBIs) occurred at 40–50 ms (or 20–25 bursts/s). Bursting also occurred across varied stimulus parameters and suggests that burst timing may be a key component of looming detection. Our findings suggest that the DCMD uses two modes of coding to transmit information about looming stimuli and that these modes change dynamically with a changing stimulus at a behaviorally-relevant time.

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.

Single synapse information coding in intraburst spike patterns of central pattern generator motor neurons

Burst firing is ubiquitous in nervous systems and has been intensively studied in central pattern generators (CPGs). Previous works have described subtle intraburst spike patterns (IBSPs) that, despite being traditionally neglected for their lack of relation to CPG motor function, were shown to be cell-type specific and sensitive to CPG connectivity. Here we address this matter by investigating how a bursting motor neuron expresses information about other neurons in the network. We performed experiments on the crustacean stomatogastric pyloric CPG, both in control conditions and interacting in real-time with computer model neurons. The sensitivity of postsynaptic to presynaptic IBSPs was inferred by computing their average mutual information along each neuron burst. We found that details of input patterns are nonlinearly and inhomogeneously coded through a single synapse into the fine IBSPs structure of the postsynaptic neuron following burst. In this way, motor neurons are able to use different time scales to convey two types of information simultaneously: muscle contraction (related to bursting rhythm) and the behavior of other CPG neurons (at a much shorter timescale by using IBSPs as information carriers). Moreover, the analysis revealed that the coding mechanism described takes part in a previously unsuspected information pathway from a CPG motor neuron to a nerve that projects to sensory brain areas, thus providing evidence of the general physiological role of information coding through IBSPs in the regulation of neuronal firing patterns in remote circuits by the CNS.

Pharmacology of currents underlying the different firing patterns of spinal sensory neurons and interneurons identified in vivo using multivariate analysis

The operation of neuronal networks depends on the firing patterns of the network's neurons. When sustained current is injected, some neurons in the central nervous system fire a single action potential and others fire repetitively. For example, in Xenopus laevis tadpoles, primary-sensory Rohon-Beard (RB) neurons fired a single action potential in response to 300-ms rheobase current injections, whereas dorsolateral (DL) interneurons fired repetitively at 10-20 Hz. To investigate the basis for these differences in vivo, we examined drug-induced changes in the firing patterns of Xenopus spinal neurons using whole cell current-clamp recordings. Neuron types were initially separated through cluster analysis, and we compared results produced using different clustering algorithms. We used these results to develop a predictive function to classify subsequently recorded neurons. The potassium channel blocker tetraethylammonium (TEA) converted single-firing RB neurons to low-frequency repetitive firing but reduced the firing frequency of repetitive-firing DL interneurons. Firing frequency in DL interneurons was also reduced by the potassium channel blockers 4-aminopyridine (4-AP), catechol, and margatoxin; 4-AP had the greatest effect. The calcium channel blockers amiloride and nimodipine had few effects on firing in either neuron type but reduced action potential duration in DL interneurons. Muscarine, which blocks M-currents, did not affect RB neurons but reduced firing frequency in DL interneurons. These results suggest that potassium currents may control neuron firing patterns: a TEA-sensitive current prevents repetitive firing in RB neurons, whereas a 4-AP-sensitive current underlies repetitive firing in DL interneurons. The cluster and discriminant analysis described could help to classify neurons in other systems.

Time and category information in pattern-based codes

Sensory stimuli are usually composed of different features (the what) appearing at irregular times (the when). Neural responses often use spike patterns to represent sensory information. The what is hypothesized to be encoded in the identity of the elicited patterns (the pattern categories), and the when, in the time positions of patterns (the pattern timing). However, this standard view is oversimplified. In the real world, the what and the when might not be separable concepts, for instance, if they are correlated in the stimulus. In addition, neuronal dynamics can condition the pattern timing to be correlated with the pattern categories. Hence, timing and categories of patterns may not constitute independent channels of information. In this paper, we assess the role of spike patterns in the neural code, irrespective of the nature of the patterns. We first define information-theoretical quantities that allow us to quantify the information encoded by different aspects of the neural response. We also introduce the notion of synergy/redundancy between time positions and categories of patterns. We subsequently establish the relation between the what and the when in the stimulus with the timing and the categories of patterns. To that aim, we quantify the mutual information between different aspects of the stimulus and different aspects of the response. This formal framework allows us to determine the precise conditions under which the standard view holds, as well as the departures from this simple case. Finally, we study the capability of different response aspects to represent the what and the when in the neural response.

Neural heterogeneities and stimulus properties affect burst coding in vivo

Many neurons tend to fire clusters of action potentials called bursts followed by quiescence in response to sensory input. While the mechanisms that underlie burst firing are generally well understood in vitro, the functional role of these bursts in generating behavioral responses to sensory input in vivo are less clear. Pyramidal cells within the electrosensory lateral line lobe (ELL) of weakly electric fish offer an attractive model system for studying the coding properties of burst firing, because the anatomy and physiology of the electrosensory circuitry are well understood, and the burst mechanism of ELL pyramidal cells has been thoroughly characterized in vitro. We investigated the coding properties of bursts generated by these cells in vivo in response to mimics of behaviorally relevant sensory input. We found that heterogeneities within the pyramidal cell population had quantitative but not qualitative effects on burst coding for the low frequency components of broadband time varying input. Moreover, spatially localized stimuli mimicking, for example, prey tended to elicit more bursts than spatially global stimuli mimicking conspecific-related stimuli. We also found small but significant correlations between burst attributes such as the number of spikes per burst or the interspike interval during the burst and stimulus attributes such as stimulus amplitude or slope. These correlations were much weaker in magnitude than those observed in vitro. More surprisingly, our results show that correlations between burst and stimulus attributes actually decreased in magnitude when we used low frequency stimuli that are expected to promote burst firing. We propose that this discrepancy is attributable to differences between ELL pyramidal cell burst firing under in vivo and in vitro conditions.

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.