A review of the methods for neuronal response latency estimation

Supra-orbital whiskers act as wind-sensing antennae in rats

We know little about mammalian anemotaxis or wind sensing. Recently, however, Hartmann and colleagues showed whisker-based anemotaxis in rats. To investigate how whiskers sense airflow, we first tracked whisker tips in anesthetized rats under low (0.5 m/s) and high (1.5 m/s) airflow. Whisker tips showed increasing movement from low to high airflow conditions, with all whisker tips moving during high airflow. Low airflow conditions-most similar to naturally occurring wind stimuli-engaged whisker tips differentially. Most whiskers moved little, but the long supra-orbital (lSO) whisker showed maximal displacement, followed by the α, β, and A1 whiskers. The lSO whisker differs from other whiskers in its exposed dorsal position, upward bending, length and thin diameter. Ex vivo extracted lSO whiskers also showed exceptional airflow displacement, suggesting whisker-intrinsic biomechanics mediate the unique airflow-sensitivity. Micro computed tomography (micro-CT) revealed that the ring-wulst-the follicle structure receiving the most sensitive afferents-was more complete/closed in the lSO, and other wind-sensitive whiskers, than in non-wind-sensitive whiskers, suggesting specialization of the supra-orbital for omni-directional sensing. We localized and targeted the cortical supra-orbital whisker representation in simultaneous Neuropixels recordings with D/E-row whisker barrels. Responses to wind-stimuli were stronger in the supra-orbital whisker representation than in D/E-row barrel cortex. We assessed the behavioral significance of whiskers in an airflow-sensing paradigm. We observed that rats spontaneously turn towards airflow stimuli in complete darkness. Selective trimming of wind-responsive whiskers diminished airflow turning responses more than trimming of non-wind-responsive whiskers. Lidocaine injections targeted to supra-orbital whisker follicles also diminished airflow turning responses compared to control injections. We conclude that supra-orbital whiskers act as wind antennae.

Generalised exponential-Gaussian distribution: a method for neural reaction time analysis

Reaction times (RTs) are an essential metric used for understanding the link between brain and behaviour. As research is reaffirming the tight coupling between neuronal and behavioural RTs, thorough statistical modelling of RT data is thus essential to enrich current theories and motivate novel findings. A statistical distribution is proposed herein that is able to model the complete RT's distribution, including location, scale and shape: the generalised-exponential-Gaussian (GEG) distribution. The GEG distribution enables shifting the attention from traditional means and standard deviations to the entire RT distribution. The mathematical properties of the GEG distribution are presented and investigated via simulations. Additionally, the GEG distribution is featured via four real-life data sets. Finally, we discuss how the proposed distribution can be used for regression analyses via generalised additive models for location, scale and shape (GAMLSS).

Precise Spiking Motifs in Neurobiological and Neuromorphic Data

Why do neurons communicate through spikes? By definition, spikes are all-or-none neural events which occur at continuous times. In other words, spikes are on one side binary, existing or not without further details, and on the other, can occur at any asynchronous time, without the need for a centralized clock. This stands in stark contrast to the analog representation of values and the discretized timing classically used in digital processing and at the base of modern-day neural networks. As neural systems almost systematically use this so-called event-based representation in the living world, a better understanding of this phenomenon remains a fundamental challenge in neurobiology in order to better interpret the profusion of recorded data. With the growing need for intelligent embedded systems, it also emerges as a new computing paradigm to enable the efficient operation of a new class of sensors and event-based computers, called neuromorphic, which could enable significant gains in computation time and energy consumption-a major societal issue in the era of the digital economy and global warming. In this review paper, we provide evidence from biology, theory and engineering that the precise timing of spikes plays a crucial role in our understanding of the efficiency of neural networks.

Variance of filtered signals: Characterization for linear reaction networks and application to neurotransmission dynamics

Neurotransmission at chemical synapses relies on the calcium-induced fusion of synaptic vesicles with the presynaptic membrane. The distance of the synaptic vesicle to the calcium channels determines the release probability and consequently the postsynaptic signal. Suitable models of the process need to capture both the mean and the variance observed in electrophysiological measurements of the postsynaptic current. In this work, we propose a method to directly compute the exact first- and second-order moments for signals generated by a linear reaction network under convolution with an impulse response function, rendering computationally expensive numerical simulations of the underlying stochastic counting process obsolete. We show that the autocorrelation of the process is central for the calculation of the filtered signal's second-order moments, and derive a system of PDEs for the cross-correlation functions (including the autocorrelations) of linear reaction networks with time-dependent rates. Finally, we employ our method to efficiently compare different spatial coarse graining approaches for a specific model of synaptic vesicle fusion. Beyond the application to neurotransmission processes, the developed theory can be applied to any linear reaction system that produces a filtered stochastic signal.

Spatially displaced excitation contributes to the encoding of interrupted motion by a retinal direction-selective circuit

Spatially distributed excitation and inhibition collectively shape a visual neuron's receptive field (RF) properties. In the direction-selective circuit of the mammalian retina, the role of strong null-direction inhibition of On-Off direction-selective ganglion cells (On-Off DSGCs) on their direction selectivity is well-studied. However, how excitatory inputs influence the On-Off DSGC's visual response is underexplored. Here, we report that On-Off DSGCs have a spatially displaced glutamatergic receptive field along their horizontal preferred-null motion axes. This displaced receptive field contributes to DSGC null-direction spiking during interrupted motion trajectories. Theoretical analyses indicate that population responses during interrupted motion may help populations of On-Off DSGCs signal the spatial location of moving objects in complex, naturalistic visual environments. Our study highlights that the direction-selective circuit exploits separate sets of mechanisms under different stimulus conditions, and these mechanisms may help encode multiple visual features.

Widespread Decoding of Tactile Input Patterns Among Thalamic Neurons

Whereas, there is data to support that cuneothalamic projections predominantly reach a topographically confined volume of the rat thalamus, the ventroposterior lateral (VPL) nucleus, recent findings show that cortical neurons that process tactile inputs are widely distributed across the neocortex. Since cortical neurons project back to the thalamus, the latter observation would suggest that thalamic neurons could contain information about tactile inputs, in principle regardless of where in the thalamus they are located. Here we use a previously introduced electrotactile interface for producing sets of highly reproducible tactile afferent spatiotemporal activation patterns from the tip of digit 2 and record neurons throughout widespread parts of the thalamus of the anesthetized rat. We find that a majority of thalamic neurons, regardless of location, respond to single pulse tactile inputs and generate spike responses to such tactile stimulation patterns that can be used to identify which of the inputs that was provided, at above-chance decoding performance levels. Thalamic neurons with short response latency times, compatible with a direct tactile afferent input via the cuneate nucleus, were typically among the best decoders. Thalamic neurons with longer response latency times as a rule were also found to be able to decode the digit 2 inputs, though typically at a lower decoding performance than the thalamic neurons with presumed direct cuneate inputs. These findings provide support for that tactile information arising from any specific skin area is widely available in the thalamocortical circuitry.

Age-Dependent Auditory Processing Deficits after Cochlear Synaptopathy Depend on Auditory Nerve Latency and the Ability of the Brain to Recruit LTP/BDNF

Age-related decoupling of auditory nerve fibers from hair cells (cochlear synaptopathy) has been linked to temporal processing deficits and impaired speech recognition performance. The link between both is elusive. We have previously demonstrated that cochlear synaptopathy, if centrally compensated through enhanced input/output function (neural gain), can prevent age-dependent temporal discrimination loss. It was also found that central neural gain after acoustic trauma was linked to hippocampal long-term potentiation (LTP) and upregulation of brain-derived neurotrophic factor (BDNF). Using middle-aged and old BDNF-live-exon-visualization (BLEV) reporter mice we analyzed the specific recruitment of LTP and the activity-dependent usage of Bdnf exon-IV and -VI promoters relative to cochlear synaptopathy and central (temporal) processing. For both groups, specimens with higher or lower ability to centrally compensate diminished auditory nerve activity were found. Strikingly, low compensating mouse groups differed from high compensators by prolonged auditory nerve latency. Moreover, low compensators exhibited attenuated responses to amplitude-modulated tones, and a reduction of hippocampal LTP and Bdnf transcript levels in comparison to high compensators. These results suggest that latency of auditory nerve processing, recruitment of hippocampal LTP, and Bdnf transcription, are key factors for age-dependent auditory processing deficits, rather than cochlear synaptopathy or aging per se.

Timing of response onset and offset in macaque V4: stimulus and task dependence

In the primate visual cortex, both the magnitude of the neuronal response and its timing can carry important information about the visual world, but studies typically focus only on response magnitude. Here, we examine the onset and offset latency of the responses of neurons in area V4 of awake, behaving macaques across several experiments in the context of a variety of stimuli and task paradigms. Our results highlight distinct contributions of stimuli and tasks to V4 response latency. We found that response onset latencies are shorter than typically cited (median = 75.5 ms), supporting a role for V4 neurons in rapid object and scene recognition functions. Moreover, onset latencies are longer for smaller stimuli and stimulus outlines, consistent with the hypothesis that longer latencies are associated with higher spatial frequency content. Strikingly, we found that onset latencies showed no significant dependence on stimulus occlusion, unlike in inferotemporal cortex, nor on task demands. Across the V4 population, onset latencies had a broad distribution, reflecting the diversity of feedforward, recurrent, and feedback connections that inform the responses of individual neurons. Response offset latencies, on the other hand, displayed the opposite tendency in their relationship to stimulus and task attributes: they are less influenced by stimulus appearance but are shorter in guided saccade tasks compared with fixation tasks. The observation that response latency is influenced by stimulus- and task-associated factors emphasizes a need to examine response timing alongside firing rate in determining the functional role of area V4.NEW & NOTEWORTHY Onset and offset timing of neuronal responses can provide information about visual environment and neuron's role in visual processing and its anatomical connectivity. In the first comprehensive examination of onset and offset latencies in the intermediate visual cortical area V4, we find neurons respond faster than previously reported, making them ideally suited to contribute to rapid object and scene recognition. While response onset reflects stimulus characteristics, timing of response offset is influenced more by behavioral task.

Fronto-Temporal Coupling Dynamics During Spontaneous Activity and Auditory Processing in the Bat Carollia perspicillata

Most mammals rely on the extraction of acoustic information from the environment in order to survive. However, the mechanisms that support sound representation in auditory neural networks involving sensory and association brain areas remain underexplored. In this study, we address the functional connectivity between an auditory region in frontal cortex (the frontal auditory field, FAF) and the auditory cortex (AC) in the bat Carollia perspicillata. The AC is a classic sensory area central for the processing of acoustic information. On the other hand, the FAF belongs to the frontal lobe, a brain region involved in the integration of sensory inputs, modulation of cognitive states, and in the coordination of behavioral outputs. The FAF-AC network was examined in terms of oscillatory coherence (local-field potentials, LFPs), and within an information theoretical framework linking FAF and AC spiking activity. We show that in the absence of acoustic stimulation, simultaneously recorded LFPs from FAF and AC are coherent in low frequencies (1-12 Hz). This "default" coupling was strongest in deep AC layers and was unaltered by acoustic stimulation. However, presenting auditory stimuli did trigger the emergence of coherent auditory-evoked gamma-band activity (>25 Hz) between the FAF and AC. In terms of spiking, our results suggest that FAF and AC engage in distinct coding strategies for representing artificial and natural sounds. Taken together, our findings shed light onto the neuronal coding strategies and functional coupling mechanisms that enable sound representation at the network level in the mammalian brain.

Bimodal modulation of background activity in an identified descending interneuron

In the absence of any obvious input, sensory neurons and interneurons can display resting or spontaneous activity. This is often regarded as noise and removed through trial averaging, although it may reflect history-dependent modulation of tuning or fidelity and, thus, be of functional relevance to downstream interneurons. We investigated the history dependence of spontaneous activity in a pair of identified, bimodal descending interneurons of the stick insect, called contralateral ON-type velocity-sensitive interneurons (cONv). The bilateral pair of cONv conveys antennal mechanosensory information to the thoracic ganglia, where it arborizes in regions containing locomotor networks. Each cONv encodes the movement velocity of the contralateral antenna, but also substrate vibration as induced by discrete tapping events. Moreover, cONv display highly fluctuating spontaneous activity that can reach rates similar to those during antennal movement at moderate velocities. Hence, cONv offer a unique opportunity to study history-dependent effects on spontaneous activity and, thus, encoding fidelity in two modalities. In this work, we studied unimodal and cross-modal effects as well as unilateral and bilateral effects, using bilateral recordings of both cONv neurons, while moving one antenna and/or delivering taps to induce substrate vibration. Tapping could reduce spontaneous activity of both neurons, whereas antennal movement reduced spontaneous activity of the contralateral cONv neuron only. Combination of both modalities showed a cooperative effect for some parameter constellations, suggesting bimodal enhancement. Since both stimulus modalities could cause a reduction of spontaneous activity at stimulus intensities occurring during natural locomotion, we conclude that this should enhance neuronal response fidelity during locomotion.

NEW & NOTEWORTHY The spontaneous activity in a pair of identified, descending insect interneurons is reduced depending on stimulus history. At rest, spontaneous activity levels are correlated in both interneurons, indicating a common drive from background activity. Whereas taps on the substrate affect both interneurons, antennal movement affects the contralateral interneuron only. Cross-modal interaction occurs, too. Since spontaneous activity is reduced at stimulus intensities encountered during natural locomotion, the mechanism could enhance neuronal response fidelity during locomotion.

Online Detection of Multiple Stimulus Changes Based on Single Neuron Interspike Intervals

Nervous systems need to detect stimulus changes based on their neuronal responses without using any additional information on the number, times, and types of stimulus changes. Here, two relatively simple, biologically realistic change point detection methods are compared with two common analysis methods. The four methods are applied to intra- and extracellularly recorded responses of a single cricket interneuron (AN2) to acoustic simulation. Solely based on these recorded responses, the methods should detect an unknown number of different types of sound intensity in- and decreases shortly after their occurrences. For this task, the methods rely on calculating an adjusting interspike interval (ISI). Both simple methods try to separate responses to intensity in- or decreases from activity during constant stimulation. The Pure-ISI method performs this task based on the distribution of the ISI, while the ISI-Ratio method uses the ratio of actual and previous ISI. These methods are compared to the frequently used Moving-Average method, which calculates mean and standard deviation of the instantaneous spike rate in a moving interval. Additionally, a classification method provides the upper limit of the change point detection performance that can be expected for the cricket interneuron responses. The classification learns the statistical properties of the actual and previous ISI during stimulus changes and constant stimulation from a training data set. The main results are: (1) The Moving-Average method requires a stable activity in a long interval to estimate the previous activity, which was not always given in our data set. (2) The Pure-ISI method can reliably detect stimulus intensity increases when the neuron bursts, but it fails to identify intensity decreases. (3) The ISI-Ratio method detects stimulus in- and decreases well, if the spike train is not too noisy. (4) The classification method shows good performance for the detection of stimulus in- and decreases. But due to the statistical learning, this method tends to confuse responses to constant stimulation with responses triggered by a stimulus change. Our results suggest that stimulus change detection does not require computationally costly mechanisms. Simple nervous systems like the cricket's could effectively apply ISI-Ratios to solve this fundamental task.

Enhanced Central Neural Gain Compensates Acoustic Trauma-induced Cochlear Impairment, but Unlikely Correlates with Tinnitus and Hyperacusis

For successful future therapeutic strategies for tinnitus and hyperacusis, a subcategorization of both conditions on the basis of differentiated neural correlates would be of invaluable advantage. In the present study, we used our refined operant conditioning animal model to divide equally noise-exposed rats into groups with either tinnitus or hyperacusis, with neither condition, or with both conditions co-occurring simultaneously. Using click stimulus and noise burst-evoked Auditory Brainstem Responses (ABR) and Distortion Product Otoacoustic Emissions, no hearing threshold difference was observed between any of the groups. However, animals with neither tinnitus nor hyperacusis responded to noise trauma with shortened ABR wave I and IV latencies and elevated central neuronal gain (increased ABR wave IV/I amplitude ratio), which was previously assumed in most of the literature to be a neural correlate for tinnitus. In contrast, animals with tinnitus had reduced neural response gain and delayed ABR wave I and IV latencies, while animals with hyperacusis showed none of these changes. Preliminary studies, aimed at establishing comparable non-invasive objective tools for identifying tinnitus in humans and animals, confirmed reduced central gain and delayed response latency in human and animals. Moreover, the first ever resting state functional Magnetic Resonance Imaging (rs-fMRI) analyses comparing humans and rats with and without tinnitus showed reduced rs-fMRI activities in the auditory cortex in both patients and animals with tinnitus. These findings encourage further efforts to establish non-invasive diagnostic tools that can be used in humans and animals alike and give hope for differentiated classification of tinnitus and hyperacusis.

Moth olfactory receptor neurons adjust their encoding efficiency to temporal statistics of pheromone fluctuations

The efficient coding hypothesis predicts that sensory neurons adjust their coding resources to optimally represent the stimulus statistics of their environment. To test this prediction in the moth olfactory system, we have developed a stimulation protocol that mimics the natural temporal structure within a turbulent pheromone plume. We report that responses of antennal olfactory receptor neurons to pheromone encounters follow the temporal fluctuations in such a way that the most frequent stimulus timescales are encoded with maximum accuracy. We also observe that the average coding precision of the neurons adjusted to the stimulus-timescale statistics at a given distance from the pheromone source is higher than if the same encoding model is applied at a shorter, non-matching, distance. Finally, the coding accuracy profile and the stimulus-timescale distribution are related in the manner predicted by the information theory for the many-to-one convergence scenario of the moth peripheral sensory system.

Methods for identification of spike patterns in massively parallel spike trains

Temporally, precise correlations between simultaneously recorded neurons have been interpreted as signatures of cell assemblies, i.e., groups of neurons that form processing units. Evidence for this hypothesis was found on the level of pairwise correlations in simultaneous recordings of few neurons. Increasing the number of simultaneously recorded neurons increases the chances to detect cell assembly activity due to the larger sample size. Recent technological advances have enabled the recording of 100 or more neurons in parallel. However, these massively parallel spike train data require novel statistical tools to be analyzed for correlations, because they raise considerable combinatorial and multiple testing issues. Recently, various of such methods have started to develop. First approaches were based on population or pairwise measures of synchronization, and later led to methods for the detection of various types of higher-order synchronization and of spatio-temporal patterns. The latest techniques combine data mining with analysis of statistical significance. Here, we give a comparative overview of these methods, of their assumptions and of the types of correlations they can detect.

Efficiency of rate and latency coding with respect to metabolic cost and time

Recent studies on the theoretical performance of latency and rate code in single neurons have revealed that the ultimate accuracy is affected in a nontrivial way by aspects such as the level of spontaneous activity of presynaptic neurons, amount of neuronal noise or the duration of the time window used to determine the firing rate. This study explores how the optimal decoding performance and the corresponding conditions change when the energy expenditure of a neuron in order to spike and maintain the resting membrane potential is accounted for. It is shown that a nonzero amount of spontaneous activity remains essential for both the latency and the rate coding. Moreover, the optimal level of spontaneous activity does not change so much with respect to the intensity of the applied stimulus. Furthermore, the efficiency of the temporal and the rate code converge to an identical finite value if the neuronal activity is observed for an unlimited period of time.

Presynaptic Spontaneous Activity Enhances the Accuracy of Latency Coding

The time to the first spike after stimulus onset typically varies with the stimulation intensity. Experimental evidence suggests that neural systems use such response latency to encode information about the stimulus. We investigate the decoding accuracy of the latency code in relation to the level of noise in the form of presynaptic spontaneous activity. Paradoxically, the optimal performance is achieved at a nonzero level of noise and suprathreshold stimulus intensities. We argue that this phenomenon results from the influence of the spontaneous activity on the stabilization of the membrane potential in the absence of stimulation. The reported decoding accuracy improvement represents a novel manifestation of the noise-aided signal enhancement.

Single and Multiple Change Point Detection in Spike Trains: Comparison of Different CUSUM Methods

In a natural environment, sensory systems are faced with ever-changing stimuli that can occur, disappear or change their properties at any time. For the animal to react adequately the sensory systems must be able to detect changes in external stimuli based on its neuronal responses. Since the nervous system has no prior knowledge of the stimulus timing, changes in stimulus need to be inferred from the changes in neuronal activity, in particular increase or decrease of the spike rate, its variability, and shifted response latencies. From a mathematical point of view, this problem can be rephrased as detecting changes of statistical properties in a time series. In neuroscience, the CUSUM (cumulative sum) method has been applied to recorded neuronal responses for detecting a single stimulus change. Here, we investigate the applicability of the CUSUM approach for detecting single as well as multiple stimulus changes that induce increases or decreases in neuronal activity. Like the nervous system, our algorithm relies exclusively on previous neuronal population activities, without using knowledge about the timing or number of external stimulus changes. We apply our change point detection methods to experimental data obtained by multi-electrode recordings from turtle retinal ganglion cells, which react to changes in light stimulation with a range of typical neuronal activity patterns. We systematically examine how variations of mathematical assumptions (Poisson, Gaussian, and Gamma distributions) used for the algorithms may affect the detection of an unknown number of stimulus changes in our data and compare these CUSUM methods with the standard Rate Change method. Our results suggest which versions of the CUSUM algorithm could be useful for different types of specific data sets.

Effect of spontaneous activity on stimulus detection in a simple neuronal model

It is studied what level of a continuous-valued signal is optimally estimable on the basis of first-spike latency neuronal data. When a spontaneous neuronal activity is present, the first spike after the stimulus onset may be caused either by the stimulus itself, or it may be a result of the prevailing spontaneous activity. Under certain regularity conditions, Fisher information is the inverse of the variance of the best estimator. It can be considered as a function of the signal intensity and then indicates accuracy of the estimation for each signal level. The Fisher information is normalized with respect to the time needed to obtain an observation. The accuracy of signal level estimation is investigated in basic discharge patterns modelled by a Poisson and a renewal process and the impact of the complex interaction between spontaneous activity and a delay of the response is shown.