Spike Timing Precision Research Articles

Neuropeptide-dependent spike time precision and plasticity in circadian output neurons.

Circadian rhythms influence various physiological and behavioral processes such as sleep-wake cycles, hormone secretion, and metabolism. Circadian output neurons are a group of neurons that receive input from the central circadian clock located in the suprachiasmatic nucleus of the mammalian brain and transmit timing information to different regions of the brain and body, coordinating the circadian rhythms of various physiological processes. In Drosophila, an important set of circadian output neurons are called pars intercerebralis (PI) neurons, which receive input from specific clock neurons called DN1. These neurons can further be subdivided into functionally and anatomically distinctive anterior (DN1a) and posterior (DN1p) clusters. The neuropeptide diuretic hormones 31 (Dh31) and 44 (Dh44) are the insect neuropeptides known to activate PI neurons to control activity rhythms. However, the neurophysiological basis of how Dh31 and Dh44 affect circadian clock neural coding mechanisms underlying sleep in Drosophila is not well understood. Here, we identify Dh31/Dh44-dependent spike time precision and plasticity in PI neurons. We find that the application of synthesized Dh31 and Dh44 affects membrane potential dynamics of PI neurons in the precise timing of the neuronal firing through their synergistic interaction, possibly mediated by calcium-activated potassium channel conductance. Further, we characterize that Dh31/Dh44 enhances postsynaptic potentials in PI neurons. Together, these results suggest multiplexed neuropeptide-dependent spike time precision and plasticity as circadian clock neural coding mechanisms underlying sleep in Drosophila.

Models optimized for real-world tasks reveal the task-dependent necessity of precise temporal coding in hearing.

Neurons encode information in the timing of their spikes in addition to their firing rates. Spike timing is particularly precise in the auditory nerve, where action potentials phase lock to sound with sub-millisecond precision, but its behavioral relevance remains uncertain. We optimized machine learning models to perform real-world hearing tasks with simulated cochlear input, assessing the precision of auditory nerve spike timing needed to reproduce human behavior. Models with high-fidelity phase locking exhibited more human-like sound localization and speech perception than models without, consistent with an essential role in human hearing. However, the temporal precision needed to reproduce human-like behavior varied across tasks, as did the precision that benefited real-world task performance. These effects suggest that perceptual domains incorporate phase locking to different extents depending on the demands of real-world hearing. The results illustrate how optimizing models for realistic tasks can clarify the role of candidate neural codes in perception.

The impact of spike timing precision and spike emission reliability on decoding accuracy

Precisely timed and reliably emitted spikes are hypothesized to serve multiple functions, including improving the accuracy and reproducibility of encoding stimuli, memories, or behaviours across trials. When these spikes occur as a repeating sequence, they can be used to encode and decode a potential time series. Here, we show both analytically and in simulations that the error incurred in approximating a time series with precisely timed and reliably emitted spikes decreases linearly with the number of neurons or spikes used in the decoding. This was verified numerically with synthetically generated patterns of spikes. Further, we found that if spikes were imprecise in their timing, or unreliable in their emission, the error incurred in decoding with these spikes would be sub-linear. However, if the spike precision or spike reliability increased with network size, the error incurred in decoding a time-series with sequences of spikes would maintain a linear decrease with network size. The spike precision had to increase linearly with network size, while the probability of spike failure had to decrease with the square-root of the network size. Finally, we identified a candidate circuit to test this scaling relationship: the repeating sequences of spikes with sub-millisecond precision in area HVC (proper name) of the zebra finch. This scaling relationship can be tested using both neural data and song-spectrogram-based recordings while taking advantage of the natural fluctuation in HVC network size due to neurogenesis.

Bifurcation and negative self-feedback mechanisms for enhanced spike-timing precision of inhibitory interneurons

<abstract> <p>A high spike-timing precision characterized by a small variation in interspike intervals of neurons is important for information processing in various brain functions. An experimental study on fast-spiking interneurons has shown that inhibitory autapses functioning as negative self-feedback can enhance spike-timing precision. In the present paper, bifurcation and negative self-feedback mechanisms for the enhanced spike-timing precision to stochastic modulations are obtained in two theoretical models, presenting theoretical explanations to the experimental finding. For stochastic spikes near both the saddle-node bifurcation on an invariant cycle (SNIC) and the subcritical Hopf (SubH) bifurcation with classes 1 and 2 excitabilities, respectively, enhanced spike-timing precision appears in large ranges of the conductance and the decaying rate of inhibitory autapses, closely matching the experimental observation. The inhibitory autaptic current reduces the membrane potential after a spike to a level lower than that in the absence of inhibitory autapses and the threshold to evoke the next spike, making it more difficult for stochastic modulations to affect spike timings, and thereby enhancing spike-timing precision. In addition, firing frequency near the SubH bifurcation is more robust than that near the SNIC bifurcation, resulting in a higher spike-timing precision for the SubH bifurcation. The bifurcation and negative self-feedback mechanisms for the enhanced spike-timing precision present potential measures to modulate the neuronal dynamics or the autaptic parameters to adjust the spike-timing precision.</p> </abstract>

Retinal ganglion cells encode differently in the myopic mouse retina?

The etiology of myopia remains unclear. This study investigated whether retinal ganglion cells (RGCs) in the myopic retina encode visual information differently from the normal retina and to determine the role of Connexin (Cx) 36 in this process. Generalized linear models (GLMs), which can capture stimulus-dependent changes in real neurons with spike timing precision and reliability, were used to predict RGCs responses to focused and defocused images in the retinas of wild-type (normal) and Lens-Induced Myopia (LIM) mice. As the predominant subunit of gap junctions in the mouse retina and a plausible modulator in myopia development, Cx36 knockout (KO) mice were used as a control for an intact retinal circuit. The kinetics of excitatory postsynaptic currents (EPSCs) of a single αRGC could reflect projection of both focused and defocused images in the retinas of normal and LIM, but not in the Cx36 knockout mice. Poisson GLMs revealed that RGC encoding of visual stimuli in the LIM retina was similar to that of the normal retina. In the LIM retinas, the linear-Gaussian GLM model with offset was a better fit for predicting the spike count under a focused image than the defocused image. Akaike information criterion (AIC) indicated that nonparametric GLM (np-GLM) model predicted focused/defocused images better in both LIM and normal retinas. However, the spike counts in 33% of αRGCs in LIM retinas were better fitted by exponential GLM (exp-GLM) under defocus, compared to only 13% αRGCs in normal retinas. The differences in encoding performance between LIM and normal retinas indicated the possible amendment and plasticity of the retinal circuit in myopic retinas. The absence of a similar response between Cx36 KO mice and normal/LIM mice might suggest that Cx36, which is associated with myopia development, plays a role in encoding focused and defocused images.

An information theoretic method to resolve millisecond-scale spike timing precision in a comprehensive motor program.

Sensory inputs in nervous systems are often encoded at the millisecond scale in a precise spike timing code. There is now growing evidence in behaviors ranging from slow breathing to rapid flight for the prevalence of precise timing encoding in motor systems. Despite this, we largely do not know at what scale timing matters in these circuits due to the difficulty of recording a complete set of spike-resolved motor signals and assessing spike timing precision for encoding continuous motor signals. We also do not know if the precision scale varies depending on the functional role of different motor units. We introduce a method to estimate spike timing precision in motor circuits using continuous MI estimation at increasing levels of added uniform noise. This method can assess spike timing precision at fine scales for encoding rich motor output variation. We demonstrate the advantages of this approach compared to a previously established discrete information theoretic method of assessing spike timing precision. We use this method to analyze the precision in a nearly complete, spike resolved recording of the 10 primary wing muscles control flight in an agile hawk moth, Manduca sexta. Tethered moths visually tracked a robotic flower producing a range of turning (yaw) torques. We know that all 10 muscles in this motor program encode the majority of information about yaw torque in spike timings, but we do not know whether individual muscles encode motor information at different levels of precision. We demonstrate that the scale of temporal precision in all motor units in this insect flight circuit is at the sub-millisecond or millisecond-scale, with variation in precision scale present between muscle types. This method can be applied broadly to estimate spike timing precision in sensory and motor circuits in both invertebrates and vertebrates.

Error correction and improved precision of spike timing in converging cortical networks.

SummaryThe brain propagates neuronal signals accurately and rapidly. Nevertheless, whether and how a pool of cortical neurons transmits an undistorted message to a target remains unclear. We apply optogenetic white noise signals to small assemblies of cortical pyramidal cells (PYRs) in freely moving mice. The directly activated PYRs exhibit a spike timing precision of several milliseconds. Instead of losing precision, interneurons driven via synaptic activation exhibit higher precision with respect to the white noise signal. Compared with directly activated PYRs, postsynaptic interneuron spike trains allow better signal reconstruction, demonstrating error correction. Data-driven modeling shows that nonlinear amplification of coincident spikes can generate error correction and improved precision. Over multiple applications of the same signal, postsynaptic interneuron spiking is most reliable at timescales ten times shorter than those of the presynaptic PYR, exhibiting temporal coding. Similar results are observed in hippocampal region CA1. Coincidence detection of convergent inputs enables messages to be precisely propagated between cortical PYRs and interneurons.

Connectivity and synaptic features of hilar mossy cells and their effects on granule cell activity along the hippocampal longitudinal axis.

The hippocampus is an elongated brain structure which runs along a ventral-to-dorsal axis in rodents, corresponding to the anterior-to-posterior axis in humans. A glutamatergic cell type in the dentate gyrus (DG), the mossy cells (MCs), establishes extensive excitatory collateral connections with the DG principal cells, the granule cells (GCs), and inhibitory interneurons in both hippocampal hemispheres along the longitudinal axis. Although coupling of two physically separated GC populations via long-axis projecting MCs is instrumental for information processing, the connectivity and synaptic features of MCs along the longitudinal axis are poorly defined. Here, using channelrhodopsin-2 assisted circuit mapping, we showed that MC excitation results in a low synaptic excitation-inhibition (E/I) balance in the intralamellar (local) GCs, but a high synaptic E/I balance in the translamellar (distant) ones. In agreement with the differential E/I balance along the ventrodorsal axis, activation of MCs either enhances or suppresses the local GC response to the cortical input, but primarily promotes the distant GC activation. Moreover, activation of MCs enhances the spike timing precision of the local GCs, but not that of the distant ones. Collectively, these findings suggest that MCs differentially regulate the local and distant GC activity through distinct synaptic mechanisms. KEY POINTS: Hippocampal mossy cell (MC) pathways differentially regulate granule cell (GC) activity along the longitudinal axis. MCs mediate a low excitation-inhibition balance in intralamellar (local) GCs, but a high excitation-inhibition balance in translamellar (distant) GCs. MCs enhance the spiking precision of local GCs, but not distant GCs. MCs either promote or suppress local GC activity, but primarily promote distant GC activation.

Effect of autaptic delay signal on spike-timing precision of single neuron

Experimental and theoretical studies have reported that the precise firing of neurons is crucial for sensory representation. Autapse serves as a special synapse connecting neuron and itself, which has also been found to improve the accuracy of neuronal response. In current work, the effect of autaptic delay signal on the spike-timing precision is investigated on a single autaptic Hodgkin–Huxley neuron in the present of noise. The simulation results show that both excitatory and inhibitory autaptic signals can effectively adjust the precise spike time of neurons with noise by choosing the appropriate coupling strength g and time delay of autaptic signal τ. The g–τ parameter space is divided into two regions: one is the region where the spike-timing precision is effectively regulated; the other is the region where the neuronal firing is almost not regulated. For the excitatory and inhibitory autapse, the range of parameters causing the accuracy of neuronal firing is different. Moreover, it is also found that the mechanisms of the spike-timing precision regulation are different for the two kinds of autaptic signals.

Nicotine Exposure during Adolescence Leads to Changes of Synaptic Plasticity and Intrinsic Excitability of Mice Insular Pyramidal Cells at Later Life.

To find satisfactory treatment for nicotine addiction, synaptic and cellular mechanisms should be investigated comprehensively. Synaptic transmission, plasticity and intrinsic excitability in various brain regions are known to be altered by acute nicotine exposure. However, it has not been addressed whether and how nicotine exposure during adolescence alters these synaptic events and intrinsic excitability in the insular cortex in adulthood. To address this question, we performed whole-cell patch-clamp recordings to examine the effects of adolescent nicotine exposure on synaptic transmission, plasticity and intrinsic excitability in layer V pyramidal neurons (PNs) of the mice insular cortex five weeks after the treatment. We found that excitatory synaptic transmission and potentiation were enhanced in these neurons. Following adolescent nicotine exposure, insular layer V PNs displayed enhanced intrinsic excitability, which was reflected in changes in relationship between current strength and spike number, inter-spike interval, spike current threshold and refractory period. In addition, spike-timing precision evaluated by standard deviation of spike timing was decreased following nicotine exposure. Our data indicate that adolescent nicotine exposure enhances synaptic transmission, plasticity and intrinsic excitability in layer V PNs of the mice insular cortex at later life, which might contribute to severe nicotine dependence in adulthood.

Faster Repetition Rate Sharpens the Cortical Representation of Echo Streams in Echolocating Bats.

There is consensus that primary auditory cortex (A1) utilizes a combination of rate codes and temporally precise population codes to represent discreet auditory objects. During the response to auditory streams, forward suppression constrains cortical rate coding strategies, but it may also be well positioned to enhance temporal coding strategies that rely on synchronized firing across neural ensembles. Here, we exploited the rapid temporal dynamics of bat echolocation to investigate how forward suppression modulates the cortical ensemble representation of complex acoustic signals embedded in echo streams. We recorded from auditory cortex of anesthetized free-tailed bats while stimulating the auditory system with naturalistic biosonar pulse-echo sequences covering a range of pulse emission rates. As expected, increasing pulse repetition rate significantly reduced the number of spikes per echo stimulus, but it also increased spike timing precision and doubled the information gain. This increased spike-timing precision translated into more robust inter-neuronal synchronization patterns with >10-dB higher signal-to-noise ratios (SNRs) at the ensemble level. We propose that forward suppression dynamically mediates a trade-off between the sensitive detection of isolated sounds versus precise spatiotemporal encoding of ongoing sound sequences in auditory cortex.

Membrane Resonance in Pyramidal and GABAergic Neurons of the Mouse Perirhinal Cortex.

The perirhinal cortex (PRC) is a polymodal associative region of the temporal lobe that works as a gateway between cortical areas and hippocampus. In recent years, an increasing interest arose in the role played by the PRC in learning and memory processes, such as object recognition memory, in contrast with certain forms of hippocampus-dependent spatial and episodic memory. The integrative properties of the PRC should provide all necessary resources to select and enhance the information to be propagated to and from the hippocampus. Among these properties, we explore in this paper the ability of the PRC neurons to amplify the output voltage to current input at selected frequencies, known as membrane resonance. Within cerebral circuits the resonance of a neuron operates as a filter toward inputs signals at certain frequencies to coordinate network activity in the brain by affecting the rate of neuronal firing and the precision of spike timing. Furthermore, the ability of the PRC neurons to resonate could have a fundamental role in generating subthreshold oscillations and in the selection of cortical inputs directed to the hippocampus. Here, performing whole-cell patch-clamp recordings from perirhinal pyramidal neurons and GABAergic interneurons of GAD67-GFP+ mice, we found, for the first time, that the majority of PRC neurons are resonant at their resting potential, with a resonance frequency of 0.5–1.5 Hz at 23°C and of 1.5–2.8 Hz at 36°C. In the presence of ZD7288 (blocker of HCN channels) resonance was abolished in both pyramidal neurons and interneurons, suggesting that Ih current is critically involved in resonance generation. Otherwise, application of TTx (voltage-dependent Na+ channel blocker) attenuates the resonance in pyramidal neurons but not in interneurons, suggesting that only in pyramidal neurons the persistent sodium current has an amplifying effect. These experimental results have also been confirmed by a computational model. From a functional point of view, the resonance in the PRC would affect the reverberating activity between neocortex and hippocampus, especially during slow wave sleep, and could be involved in the redistribution and strengthening of memory representation in cortical regions.

Differential contributions of voltage-gated potassium channel subunits in enhancing temporal coding in the bushy cells of the ventral cochlear nucleus.

Temporal coding precision of bushy cells in the ventral cochlear nucleus (VCN), critical for sound localization and communication, depends on the generation of rapid and temporally precise action potentials (APs). Voltage-gated potassium (Kv) channels are critically involved in this. The bushy cells in rat VCN express Kv1.1, 1.2, 1.3, 1.6, 3.1, 4.2, and 4.3 subunits. The Kv1.1 subunit contributes to the generation of a temporally precise single AP. However, the understanding of the functions of other Kv subunits expressed in the bushy cells is limited. Here, we investigated the functional diversity of Kv subunits concerning their contributions to temporal coding. We characterized the electrophysiological properties of the Kv channels with different subunits using whole cell patch-clamp recording and pharmacological methods. The neuronal firing pattern changed from single to multiple APs only when the Kv1.1 subunit was blocked. The Kv subunits, including the Kv1.1, 1.2, 1.6, or 3.1, were involved in enhancing temporal coding by lowering membrane excitability, shortening AP latencies, reducing jitter, and regulating AP kinetics. Meanwhile, all the Kv subunits contributed to rapid repolarization and sharpening peaks by narrowing half-width and accelerating fall rate, and the Kv1.1 subunit also affected the depolarization of AP. The Kv1.1, 1.2, and 1.6 subunits endowed bushy cells with a rapid time constant and a low input resistance of membrane for enhancing spike timing precision. The present results indicate that the Kv channels differentially affect intrinsic membrane properties to optimize the generation of rapid and reliable APs for temporal coding.NEW & NOTEWORTHY This study investigates the roles of Kv channels in effecting precision using electrophysiological and pharmacological methods in bushy cells. Different Kv channels have varying electrophysiological characteristics, which contribute to the interplay between changes in the membrane properties and regulation of neuronal excitability which then improve temporal coding. We conclude that the Kv channels are specialized to promote the precise and rapid coding of acoustic input by optimizing the generation of reliable APs.

Encoding sound in the cochlea: from receptor potential to afferent discharge.

Ribbon-class synapses in the ear achieve analog to digital transformation of a continuously graded membrane potential to all-or-none spikes. In mammals, several auditory nerve fibres (ANFs) carry information from each inner hair cell (IHC) to the brain in parallel. Heterogeneity of transmission among synapses contributes to the diversity of ANF sound-response properties. In addition to the place code for sound frequency and the rate code for sound level, there is also a temporal code. In series with cochlear amplification and frequency tuning, neural representation of temporal cues over a broad range of sound levels enables auditory comprehension in noisy multi-speaker settings. The IHC membrane time constant introduces a low-pass filter that attenuates fluctuations of the receptor potential above 1-2kHz. The ANF spike generator adds a high-pass filter via its depolarization-rate threshold that rejects slow changes in the postsynaptic potential and its phasic response property that ensures one spike per depolarization. Synaptic transmission involves several stochastic subcellular processes between IHC depolarization and ANF spike generation, introducing delay and jitter that limits the speed and precision of spike timing. ANFs spike at a preferred phase of periodic sounds in a process called phase-locking that is limited to frequencies below a few kilohertz by both the IHC receptor potential and the jitter in synaptic transmission. During phase-locking to periodic sounds of increasing intensity, faster and facilitated activation of synaptic transmission and spike generation may be offset by presynaptic depletion of synaptic vesicles, resulting in relatively small changes in response phase. Here we review encoding of spike-timing at cochlear ribbon synapses.

Network Asynchrony Underlying Increased Broadband Gamma Power.

Synchronous activity of cortical inhibitory interneurons expressing parvalbumin (PV) underlies expression of cortical γ rhythms. Paradoxically, deficient PV inhibition is associated with increased broadband γ power in the local field potential. Increased baseline broadband γ is also a prominent characteristic in schizophrenia and a hallmark of network alterations induced by NMDAR antagonists, such as ketamine. Whether enhanced broadband γ is a true rhythm, and if so, whether rhythmic PV inhibition is involved or not, is debated. Asynchronous and increased firing activities are thought to contribute to broadband power increases spanning the γ band. Using male and female mice lacking NMDAR activity specifically in PV neurons to model deficient PV inhibition, we here show that neuronal activity with decreased synchronicity is associated with increased prefrontal broadband γ power. Specifically, reduced spike time precision and spectral leakage of spiking activity because of higher firing rates (spike "contamination") affect the broadband γ band. Desynchronization was evident at multiple time scales, with reduced spike entrainment to the local field potential, reduced cross-frequency coupling, and fragmentation of brain states. Local application of S(+)-ketamine in (control) mice with intact NMDAR activity in PV neurons triggered network desynchronization and enhanced broadband γ power. However, our investigations suggest that disparate mechanisms underlie increased broadband γ power caused by genetic alteration of PV interneurons and ketamine-induced power increases in broadband γ. Our study confirms that enhanced broadband γ power can arise from asynchronous activities and demonstrates that long-term deficiency of PV inhibition can be a contributor.SIGNIFICANCE STATEMENT Brain oscillations are fundamental to the coordination of neuronal activity across neurons and structures. γ oscillations (30-80 Hz) have received particular attention through their association with perceptual and cognitive processes. Synchronous activity of inhibitory parvalbumin (PV) interneurons generates cortical γ oscillation, but, paradoxically, PV neuron deficiency is associated with increases in γ oscillations. We here reconcile this conundrum and show how deficient PV inhibition can lead to increased and asynchronous excitatory firing, contaminating the local field potential and manifesting as increased γ power. Thus, increased γ power does not always reflect a genuine rhythm. Further, we show that ketamine-induced γ increases are caused by separate network mechanisms.

Study on propagation efficiency and fidelity of subthreshold signal in feed-forward hybrid neural network under electromagnetic radiation

The hybrid neural model provides a computationally effective and biophysics-based neuron behavior model, which maintains its simplicity by incorporating the dynamics characteristics of ion channels in two fast ion channels. In view of the issue of signal propagation in multilayer network composed of hybrid neurons is still unclear and the role of neural network is mainly used to generate biological consciousness and help creatures to think and act, it is necessary to explore neural network with more perfect function and better transmission efficiency. Considering a feed-forward neural network (FFN) which is composed of hybrid neurons in the presence of electromagnetic radiation, the effects of the Gaussian white noise, the strength of synaptic weight and inter-layer connection probability on subthreshold excitatory postsynaptic current (EPSC) propagation are investigated. In order to clarify the mechanism of signal transmission more clearly, the dot raster plot of spike and the corresponding post-spike time histogram of each layer are explored. Particularly, the propagation efficiency and fidelity of subthreshold EPSC signal in the FFN are investigated by calculating the power norm and the spike timing precision. Our results indicate that both the power norm and the spike timing precision fluctuate with the increase in the inter-layer connection probability, and the multi-periods bursting discharge mode can be detected in both the transmission layers and the output layer. Besides, there exists an optimal noise intensity to guarantee the best temporal coding and maximum transmission efficiency of subthreshold EPSC signal. Moreover, the optimal noise intensity makes the fidelity of the FFN the highest, which ensures that the weak signal and the excitation caused by noise can be distinguished. The conclusions obtained in this paper have potential value for discussing the encoding, decoding and propagation mechanism of information in real neural networks.

Astrocytes Control Sensory Acuity via Tonic Inhibition in the Thalamus

Sensory discrimination is essential for survival. However, how sensory information is finely controlled in the brain is not well defined. Here, we show that astrocytes control tactile acuity via tonic inhibition in the thalamus. Mechanistically, diamine oxidase (DAO) and the subsequent aldehyde dehydrogenase 1a1 (Aldh1a1) convert putrescine into GABA, which is released via Best1. The GABA from astrocytes inhibits synaptically evoked firing at the lemniscal synapses to fine-tune the dynamic range of the stimulation-response relationship, the precision of spike timing, and tactile discrimination. Our findings reveal a novel role of astrocytes in the control of sensory acuity through tonic GABA release.

Specific loss of neural sensitivity to interaural time difference of unmodulated noise stimuli following noise-induced hearing loss.

Sensorineural hearing loss (SNHL) causes an overall deficit in binaural hearing, including the abilities to localize sound sources, discriminate interaural time and level differences (ITDs and ILDs, respectively), and utilize binaural cues to aid signal detection and comprehension in noisy environments. Few studies have examined the effect of SNHL on binaural coding in the central auditory system, and those that have focused on age-related hearing loss. We induced hearing loss in male and female Dutch-belted rabbits via noise overexposure and compared unanesthetized single-unit responses of their inferior colliculi [hearing loss (HL) neurons] with those of unexposed rabbits. Sound-level thresholds of HL neurons to diotic noise were elevated by 75 dB, on average. Sensitivity of firing rates of HL neurons to the azimuth of a broadband noise stimulus was reduced, on average, but was confounded by differences in sound level with respect to detection threshold between groups. We independently manipulated ITD and ILD in virtual acoustic space and found directional sensitivity in binaurally sensitive HL neurons was entirely due to ILD sensitivity and no different than that for unexposed rabbits. However, ITD sensitivity was completely absent in binaurally sensitive HL neurons for noise stimuli both in virtual acoustic space and with ITDs extending to ±3 ms. HL neurons also had weaker spike-timing precision and slightly increased spontaneous rates. Overall, ILD sensitivity was uncompromised, whereas ITD sensitivity was completely lost, implying a specific inability to use information in the timing or correlation of acoustic noise waveforms between the two ears following severe SNHL.NEW & NOTEWORTHY Sensorineural hearing loss compromises perceptual abilities that arise from hearing with two ears, yet its effects on binaural aspects of neural responses are largely unknown. We found that, following severe hearing loss because of acoustic trauma, auditory midbrain neurons specifically lost the ability to encode time differences between the arrival of a broadband noise stimulus to the two ears, whereas the encoding of sound level differences between the two ears remained uncompromised.

Developmentally Regulated Rebound Depolarization Enhances Spike Timing Precision in Auditory Midbrain Neurons.

The inferior colliculus (IC) is an auditory midbrain structure involved in processing biologically important temporal features of sounds. The responses of IC neurons to these temporal features reflect an interaction of synaptic inputs and neuronal biophysical properties. One striking biophysical property of IC neurons is the rebound depolarization produced following membrane hyperpolarization. To understand how the rebound depolarization is involved in spike timing, we made whole-cell patch clamp recordings from IC neurons in brain slices of P9–21 rats. We found that the percentage of rebound neurons was developmentally regulated. The precision of the timing of the first spike on the rebound increased when the neuron was repetitively injected with a depolarizing current following membrane hyperpolarization. The average jitter of the first spikes was only 0.5 ms. The selective T-type Ca2+ channel antagonist, mibefradil, significantly increased the jitter of the first spike of neurons in response to repetitive depolarization following membrane hyperpolarization. Furthermore, the rebound was potentiated by one to two preceding rebounds within a few hundred milliseconds. The first spike generated on the potentiated rebound was more precise than that on the non-potentiated rebound. With the addition of a calcium chelator, BAPTA, into the cell, the rebound potentiation no longer occurred, and the precision of the first spike on the rebound was not improved. These results suggest that the postinhibitory rebound mediated by T-type Ca2+ channel promotes spike timing precision in IC neurons. The rebound potentiation and precise spikes may be induced by increases in intracellular calcium levels.

Neuromorphic approach to tactile edge orientation estimation using spatiotemporal similarity

Moving a finger around the boundary edge is one of the important strategies followed by humans for ascertaining the shape and size of an object by touch. Tactile response from thousands of mechanoreceptors in the human hand offers a high spatiotemporal resolution to perceive the edge orientation quickly (<50 ms) and accurately (acuity of <3°). Inspired by the computational efficiency of biological tactile system, we present a neuromorphic approach to artificial tactile sensing that mimics the spike-based spatiotemporal tactile response of Fast Adapting type I (FA-I) mechanoreceptors. We propose a novel, model-based spatiotemporal correlation matching method to estimate the orientation of the boundary edge while a piezoresistive tactile sensor array attached to robotic arm palpates over the object. Results highlight the ability of the proposed method to efficiently leverage spatial and temporal information, by obtaining very precise orientation estimates (±1.67° error for edges oriented from 10° to 90°, with a step of 5°) in spite of a low-resolution sensor (169 mm2, 4×4 resolution). A comparison with both spatial and spatiotemporal based classifiers indicates that the proposed method achieves 20% lower mean absolute error (MAE) than its closest counterparts, all of which required supervised training. Furthermore, we show that even with a ten-fold loss of spike time precision (1 ms–10 ms), MAE is maintained at 2°. This work highlights that even with a modest sensor size and resolution, spatiotemporal similarity metrics can be used to obtain very precise estimates of orientation. Such an approach has potential applications towards improving the tactile sensing capability in robotic/prosthetic hands where knowledge of spatial edge orientation information is paramount for object manipulation, and sensor contact area is often sparse and small.