Reliable Spike Research Articles

Spike Reliability is Cell-Type Specific and Shapes Excitation and Inhibition in the Cortex.

Neurons encode information in the highly variable spiking activity of neuronal populations, so that different repetitions of the same stimulus can generate action potentials that vary significantly in terms of the count and timing. How does spiking variability originate, and does it have a functional purpose? Leveraging the Allen Institute cell types dataset, we relate the spiking reliability of cortical neurons in-vitro during the intracellular injection of current resembling synaptic inputs to their morphologic, electrophysiologic, and transcriptomic classes. Our findings demonstrate that parvalbumin+ (PV) interneurons, a subclass of inhibitory neurons, show high reliability compared to other neuronal subclasses, particularly excitatory neurons. Through computational modeling, we predict that the high reliability of PV interneurons allows for strong and precise inhibition in downstream neurons, while the lower reliability of excitatory neurons allows for integrating multiple synaptic inputs leading to a spiking rate code. These findings illuminate how spiking variability in different neuronal classes affect information propagation in the brain, leading to precise inhibition and spiking rate codes.

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.

Resting-potential-adjustable soft-reset integrate-and-fire neuron model for highly reliable and energy-efficient hardware-based spiking neural networks

In this study, the "resting-potential-adjustable soft-reset (RSR)" integrate-and-fire neuron model is proposed for higher reliability of hardware-based spiking neural networks (SNNs). Recently, researchers have attempted to implement neuronal models using hardware. However, previously proposed hard-reset (HR) and soft-reset (SR) neuron models are not suitable for hardware implementation for the following reasons. First, it is difficult to compensate for the neuron-threshold variation, which generally occurs in the fabrication processes. Second, they require a negative voltage supply that consumes substantial energy to implement the negative membrane potential (NMP). Third, for the HR and SR neuron models, maintaining the residual membrane potential (RMP) is challenging. The proposed RSR neuron model solves these challenges without any additional systems and algorithms and provides the following advantages: easy compensation for the neuron-threshold variation, significant energy reduction without using a negative voltage supply for the NMP, and precise preservation of the RMP. We discuss the behavior of the RSR neuron model and evaluate performance by comparing the inference results. Even with the neuron-threshold variation within the six-sigma range, the RSR neuron model restores the inference accuracy to the values of artificial neural networks (ANNs) for various datasets (CIFAR-10: 92.74%, SVHN: 95.00%, MNIST: 98.30%). In the case of the CIFAR-10, the RSR neuron model consumes 32.80% lower energy than the SR neuron model.

Trace arsenic quantification by hydride generation-flame atomic absorption spectrometry using durian husk-derived nanocellulose as a solid-phase extraction biosorbent

<p>In the present study, nanocellulose was successfully extracted from durian husk via chemical treatment and then characterized by several analytical techniques, involving X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), energy-dispersive X-ray spectroscopy (EDX) and transmission electron microscopy (TEM). Nanocellulose was obtained with a crystallinity index of 77% and in the form of cellulose nanofibrils (30 nm in width and micrometers in length). Using nanocellulose as a biosorbent in solid-phase extraction technique, the inorganic arsenic ions were eluted using 0.1 M HNO3 and its content in the eluate solution was quantified by hydride generation-flame atomic absorption spectrometry (HG-FAAS), in which the recovery of arsenic ions using nanocellulose were assessed via contact time, pH and dosage of sorbent, reaching the equilibrium after 30 minutes. With the limit of detection (LOD) of 0.41 ng mL-1, the limit of quantification (LOQ) of 1.38 ng mL-1 and the relative standard deviation of repeatability (%RSD) of 3.44% (with ten replicates) for arsenic determination, the present method was practiced with the trace inorganic arsenic quantification in real samples (fish sauce with various protein levels) with reliable spike recoveries.</p>

Heterogeneity of voltage gated sodium current density between neurons decorrelates spiking and suppresses network synchronization in Scn1b null mouse models

Voltage gated sodium channels (VGSCs) are required for action potential initiation and propagation in mammalian neurons. As with other ion channel families, VGSC density varies between neurons. Importantly, sodium current (INa) density variability is reduced in pyramidal neurons of Scn1b null mice. Scn1b encodes the VGSC β1/ β1B subunits, which regulate channel expression, trafficking, and voltage dependent properties. Here, we investigate how variable INa density in cortical layer 6 and subicular pyramidal neurons affects spike patterning and network synchronization. Constitutive or inducible Scn1b deletion enhances spike timing correlations between pyramidal neurons in response to fluctuating stimuli and impairs spike-triggered average current pattern diversity while preserving spike reliability. Inhibiting INa with a low concentration of tetrodotoxin similarly alters patterning without impairing reliability, with modest effects on firing rate. Computational modeling shows that broad INa density ranges confer a similarly broad spectrum of spike patterning in response to fluctuating synaptic conductances. Network coupling of neurons with high INa density variability displaces the coupling requirements for synchronization and broadens the dynamic range of activity when varying synaptic strength and network topology. Our results show that INa heterogeneity between neurons potently regulates spike pattern diversity and network synchronization, expanding VGSC roles in the nervous system.

Loss of \u03b24-spectrin impairs Nav channel clustering at the heminode and temporal fidelity of presynaptic spikes in developing auditory brain

Beta-4 (β4)-spectrin, encoded by the gene Sptbn4, is a cytoskeleton protein found at nodes and the axon initial segments (AIS). Sptbn4 mutations are associated with myopathy, neuropathy, and auditory deficits in humans. Related to auditory dysfunction, however, the expression and roles of β4-spectrin at axon segments along the myelinated axon in the developing auditory brain are not well explored. We found during postnatal development, β4-spectrin is critical for voltage-gated sodium channel (Nav) clustering at the heminode along the nerve terminal, but not for the formation of nodal and AIS structures in the auditory brainstem. Presynaptic terminal recordings in Sptbn4geo mice, β4-spectrin null mice, showed an elevated threshold of action potential and increased failures during action potential train at high-frequency. Sptbn4geo mice exhibited a slower central conduction and showed no startle responses, but had normal cochlear function. Taken together, the lack of β4-spectrin impairs Nav clustering at the heminode along the nerve terminal and the temporal fidelity and reliability of presynaptic spikes, leading to central auditory processing deficits during postnatal development.

KCNQ Channels Enable Reliable Presynaptic Spiking and Synaptic Transmission at High Frequency.

The presynaptic action potential (AP) is required to drive calcium influx into nerve terminals, resulting in neurotransmitter release. Accordingly, the AP waveform is crucial in determining the timing and strength of synaptic transmission. The calyx of Held nerve terminals of rats of either sex showed minimum changes in AP waveform during high-frequency AP firing. We found that the stability of the calyceal AP waveform requires KCNQ (KV7) K+ channel activation during high-frequency spiking activity. High-frequency presynaptic spikes gradually led to accumulation of KCNQ channels in open states which kept interspike membrane potential sufficiently negative to maintain Na+ channel availability. Blocking KCNQ channels during stimulus trains led to inactivation of presynaptic Na+, and to a lesser extent KV1 channels, thereby reducing the AP amplitude and broadening AP duration. Moreover, blocking KCNQ channels disrupted the stable calcium influx and glutamate release required for reliable synaptic transmission at high frequency. Thus, while KCNQ channels are generally thought to prevent hyperactivity of neurons, we find that in axon terminals these channels function to facilitate reliable high-frequency synaptic signaling needed for sensory information processing.SIGNIFICANCE STATEMENT The presynaptic spike results in calcium influx required for neurotransmitter release. For this reason, the spike waveform is crucial in determining the timing and strength of synaptic transmission. Auditory information is encoded by spikes phase locked to sound frequency at high rates. The calyx of Held nerve terminals in the auditory brainstem show minimum changes in spike waveform during high-frequency spike firing. We found that activation of KCNQ K+ channel builds up during high-frequency firing and its activation helps to maintain a stable spike waveform and reliable synaptic transmission. While KCNQ channels are generally thought to prevent hyperexcitability of neurons, we find that in axon terminals these channels function to facilitate high-frequency synaptic signaling during auditory information processing.

Disturbed Balance of Inhibitory Signaling Links Hearing Loss and Cognition.

Neuronal hyperexcitability in the central auditory pathway linked to reduced inhibitory activity is associated with numerous forms of hearing loss, including noise damage, age-dependent hearing loss, and deafness, as well as tinnitus or auditory processing deficits in autism spectrum disorder (ASD). In most cases, the reduced central inhibitory activity and the accompanying hyperexcitability are interpreted as an active compensatory response to the absence of synaptic activity, linked to increased central neural gain control (increased output activity relative to reduced input). We here suggest that hyperexcitability also could be related to an immaturity or impairment of tonic inhibitory strength that typically develops in an activity-dependent process in the ascending auditory pathway with auditory experience. In these cases, high-SR auditory nerve fibers, which are critical for the shortest latencies and lowest sound thresholds, may have either not matured (possibly in congenital deafness or autism) or are dysfunctional (possibly after sudden, stressful auditory trauma or age-dependent hearing loss linked with cognitive decline). Fast auditory processing deficits can occur despite maintained basal hearing. In that case, tonic inhibitory strength is reduced in ascending auditory nuclei, and fast inhibitory parvalbumin positive interneuron (PV-IN) dendrites are diminished in auditory and frontal brain regions. This leads to deficits in central neural gain control linked to hippocampal LTP/LTD deficiencies, cognitive deficits, and unbalanced extra-hypothalamic stress control. Under these conditions, a diminished inhibitory strength may weaken local neuronal coupling to homeostatic vascular responses required for the metabolic support of auditory adjustment processes. We emphasize the need to distinguish these two states of excitatory/inhibitory imbalance in hearing disorders: (i) Under conditions of preserved fast auditory processing and sustained tonic inhibitory strength, an excitatory/inhibitory imbalance following auditory deprivation can maintain precise hearing through a memory linked, transient disinhibition that leads to enhanced spiking fidelity (central neural gain⇑) (ii) Under conditions of critically diminished fast auditory processing and reduced tonic inhibitory strength, hyperexcitability can be part of an increased synchronization over a broader frequency range, linked to reduced spiking reliability (central neural gain⇓). This latter stage mutually reinforces diminished metabolic support for auditory adjustment processes, increasing the risks for canonical dementia syndromes.

BiPOLES is an optogenetic tool developed for bidirectional dual-color control of neurons

Optogenetic manipulation of neuronal activity through excitatory and inhibitory opsins has become an indispensable experimental strategy in neuroscience research. For many applications bidirectional control of neuronal activity allowing both excitation and inhibition of the same neurons in a single experiment is desired. This requires low spectral overlap between the excitatory and inhibitory opsin, matched photocurrent amplitudes and a fixed expression ratio. Moreover, independent activation of two distinct neuronal populations with different optogenetic actuators is still challenging due to blue-light sensitivity of all opsins. Here we report BiPOLES, an optogenetic tool for potent neuronal excitation and inhibition with light of two different wavelengths. BiPOLES enables sensitive, reliable dual-color neuronal spiking and silencing with single- or two-photon excitation, optical tuning of the membrane voltage, and independent optogenetic control of two neuronal populations using a second, blue-light sensitive opsin. The utility of BiPOLES is demonstrated in worms, flies, mice and ferrets.

Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network

We investigate the constructive role of an external noise signal, in the form of a low-rate Poisson sequence of pulses supplied to all inputs of a spiking neural network, consisting in maintaining for a long time or even recovering a memory trace (engram) of the image without its direct renewal (or rewriting). In particular, this unique dynamic property is demonstrated in a single-layer spiking neural network consisting of simple integrate-and-fire neurons and memristive synaptic weights. This is carried out by preserving and even fine-tuning the conductance values of memristors in terms of dynamic plasticity, specifically spike-timing-dependent plasticity-type, driven by overlapping pre- and postsynaptic voltage spikes. It has been shown that the weights can be to a certain extent unreliable, due to such characteristics as the limited retention time of resistive state or the variation of switching voltages. Such a noise-assisted persistence of memory, on one hand, could be a prototypical mechanism in a biological nervous system and, on the other hand, brings one step closer to the possibility of building reliable spiking neural networks composed of unreliable analog elements.

Asynchronous Glutamate Release at Autapses Regulates Spike Reliability and Precision in Mouse Neocortical Pyramidal Cells.

Autapses are self-synapses of a neuron. Inhibitory autapses in the neocortex release GABA in 2 modes, synchronous release and asynchronous release (AR), providing precise and prolonged self-inhibition, respectively. A subpopulation of neocortical pyramidal cells (PCs) also forms functional autapses, activation of which promotes burst firing by strong unitary autaptic response that reflects synchronous glutamate release. However, it remains unclear whether AR occurs at PC autapses and plays a role in neuronal signaling. We performed whole-cell recordings from layer-5 PCs in slices of mouse prefrontal cortex (PFC). In response to action potential (AP) burst, 63% of PCs showed robust long-lasting autaptic AR, much stronger than synaptic AR between neighboring PCs. The autaptic AR is mediated predominantly by P/Q-type Ca2+ channels, and its strength depends on the intensity of PC activity and the level of residual Ca2+. Further experiments revealed that autaptic AR enhances spiking activities but reduces the temporal precision of post-burst APs. Together, the results show the occurrence of AR at PC autapses, the delayed and persistent glutamate AR causes self-excitation in individual PCs but may desynchronize the autaptic PC population. Thus, glutamatergic autapses should be essential elements in PFC and contribute to cortical information processing.

Source imaging of deep-brain activity using the regional spatiotemporal Kalman filter

Background and objective: The human brain displays rich and complex patterns of interaction within and among brain networks that involve both cortical and subcortical brain regions. Due to the limited spatial resolution of surface electroencephalography (EEG), EEG source imaging is used to reconstruct brain sources and investigate their spatial and temporal dynamics. The majority of EEG source imaging methods fail to detect activity from subcortical brain structures. The reconstruction of subcortical sources is a challenging task because the signal from these sources is weakened and mixed with artifacts and other signals from cortical sources. In this proof-of-principle study we present a novel EEG source imaging method, the regional spatiotemporal Kalman filter (RSTKF), that can detect deep brain activity.Methods: The regional spatiotemporal Kalman filter (RSTKF) is a generalization of the spatiotemporal Kalman filter (STKF), which allows for the characterization of different regional dynamics in the brain. It is based on state-space modeling with spatially heterogeneous dynamical noise variances, since models with spatial and temporal homogeneity fail to describe the dynamical complexity of brain activity. First, RSTKF is tested using simulated EEG data from sources in the frontal lobe, putamen, and thalamus. After that, it is applied to non-averaged interictal epileptic spikes from a presurgical epilepsy patient with focal epileptic activity in the amygdalo-hippocampal complex. The results of RSTKF are compared to those of low-resolution brain electromagnetic tomography (LORETA) and of standard STKF.Results: Only RSTKF is successful in consistently and accurately localizing the sources in deep brain regions. Additionally, RSTKF shows improved spatial resolution compared to LORETA and STKF.Conclusions: RSTKF is a generalization of STKF that allows for accurate, focal, and consistent localization of sources, especially in the deeper brain areas. In contrast to standard source imaging methods, RSTKF may find application in the localization of the epileptogenic zone in deeper brain structures, such as mesial frontal and temporal lobe epilepsies, especially in EEG recordings for which no reliable averaged spike shape can be obtained due to lack of the necessary number of spikes required to reach a certain signal-to-noise ratio level after averaging.

A Layer 3→5 Circuit in Auditory Cortex That Contributes to Pre-pulse Inhibition of the Acoustic Startle Response.

While connectivity within sensory cortical circuits has been studied extensively, how these connections contribute to perception and behavior is not well understood. Here we tested the role of a circuit between layers 3 and 5 of auditory cortex in sound detection. We measured sound detection using a common variant of pre-pulse inhibition of the acoustic startle response, in which a silent gap in background noise acts as a cue that attenuates startle. We used the Nr5a-Cre driver line, which we found drove expression in the auditory cortex restricted predominantly to layer 3. Photoactivation of these cells evoked short-latency, highly reliable spiking in downstream layer 5 neurons, and attenuated startle responses similarly to gaps in noise. Photosuppression of these cells did not affect behavioral gap detection. Our data provide the first demonstration that direct activation of auditory cortical neurons is sufficient to attenuate the acoustic startle response, similar to the detection of a sound.

Excitatory-Inhibitory Synaptic Coupling in Avian Nucleus Magnocellularis.

The activity of neurons is determined by the balance between their excitatory and inhibitory synaptic inputs. Neurons in the avian nucleus magnocellularis (NM) integrate monosynaptic excitatory and polysynaptic inhibitory inputs from the auditory nerve, and transmit phase-locked output to higher auditory centers. The excitatory input is graded tonotopically, such that neurons tuned to higher frequency receive fewer, but larger, axon terminals. However, it remains unknown how the balance between excitatory and inhibitory inputs is determined in NM. We here examined synaptic and spike responses of NM neurons during stimulation of the auditory nerve in thick brain slices of chicken of both sexes, and found that the excitatory-inhibitory balance varied according to tonotopic region, ensuring reliable spike output across frequencies. Auditory nerve stimulation elicited IPSCs in NM neurons regardless of tonotopic region, but the dependence of IPSCs on intensity varied in a systematic way. In neurons tuned to low frequency, IPSCs appeared and increased in parallel with EPSCs with elevation of intensity, which expanded dynamic range by preventing saturation of spike generation. On the other hand, in neurons tuned to higher frequency, IPSCs were smaller than EPSCs and had higher thresholds for activation, thus facilitating high-fidelity transmission. Computer simulation confirmed that these differences in inhibitory input were optimally matched to the patterns of excitatory input, and enabled appropriate level of neuronal output for wide intensity and frequency ranges of sound in the auditory system.SIGNIFICANCE STATEMENT Neurons in nucleus magnocellularis encode timing information of sound across wide intensity ranges by integrating excitatory and inhibitory synaptic inputs from the auditory nerve, but underlying synaptic mechanisms of this integration are not fully understood. We here show that the excitatory-inhibitory relationship was expressed differentially at each tonotopic region; the relationship was linear in neurons tuned to low-frequency, expanding dynamic range by preventing saturation of spike generation; by contrast inhibitory input remained much smaller than excitatory input in neurons tuned to higher frequency, thus ensuring high-fidelity transmission. The tonotopic regulation of excitatory and inhibitory input optimized the output across frequencies and intensities, playing a fundamental role in the timing coding pathway in the auditory system.

Impaired Reliability and Precision of Spiking in Adults But Not Juveniles in a Mouse Model of Fragile X Syndrome.

Fragile X syndrome (FXS) is the most common source of intellectual disability and autism. Extensive studies have been performed on the network and behavioral correlates of the syndrome, but our knowledge about intrinsic conductance changes is still limited. In this study, we show a differential effect of FMRP knockout in different subsections of hippocampus using whole-cell patch clamp in mouse hippocampal slices. We observed no significant change in spike numbers in the CA1 region of hippocampus, but a significant increase in CA3, in juvenile mice. However, in adult mice we see a reduction in spike number in the CA1 with no significant difference in CA3. In addition, we see increased variability in spike numbers in CA1 cells following a variety of steady and modulated current step protocols. This effect emerges in adult mice (8 weeks) but not juvenile mice (4 weeks). This increased spiking variability was correlated with reduced spike number and with elevated AHP. The increased AHP arose from elevated SK currents (small conductance calcium-activated potassium channels), but other currents involved in medium AHP, such as Ih and M, were not significantly different. We obtained a partial rescue of the cellular variability phenotype when we blocked SK current using the specific blocker apamin. Our observations provide a single-cell correlate of the network observations of response variability and loss of synchronization, and suggest that the elevation of SK currents in FXS may provide a partial mechanistic explanation for this difference.

Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights

Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than traditional hardware with the Von-Neumann architecture. However, the memristive weight adjustment in the formal neuromorphic networks by the standard back-propagation techniques suffers from poor device-to-device reproducibility. One of the most promising approaches to overcome this problem is to use local learning rules for spiking neuromorphic architectures which potentially could be adaptive to the variability issue mentioned above. Different kinds of local rules for learning spiking systems are mostly realized on a bio-inspired spike-time-dependent plasticity (STDP) mechanism, which is an improved type of classical Hebbian learning. Whereas the STDP-like mechanism has already been shown to emerge naturally in memristive devices, the demonstration of its self-adaptive learning property, potentially overcoming the variability problem, is more challenging and has yet to be reported. Here we experimentally demonstrate an STDP-based learning protocol that ensures self-adaptation of the memristor resistive states, after only a very few spikes, and makes the plasticity sensitive only to the input signal configuration, but neither to the initial state of the devices nor their device-to-device variability. Then, it is shown that the self-adaptive learning of a spiking neuron with memristive weights on rate-coded patterns could also be realized with hardware-based STDP rules. The experiments have been carried out with nanocomposite-based (Co40Fe40B20)х(LiNbO3−y)100−х memristive structures, but their results are believed to be applicable to a wide range of memristive devices. All the experimental data were supported and extended by numerical simulations. There is a hope that the obtained results pave the way for building up reliable spiking neuromorphic systems composed of partially unreliable analog memristive elements, with a more complex architecture and the capability of unsupervised learning.

Cortical reliability amid noise and chaos

Typical responses of cortical neurons to identical sensory stimuli appear highly variable. It has thus been proposed that the cortex primarily uses a rate code. However, other studies have argued for spike-time coding under certain conditions. The potential role of spike-time coding is directly limited by the internally generated variability of cortical circuits, which remains largely unexplored. Here, we quantify this internally generated variability using a biophysical model of rat neocortical microcircuitry with biologically realistic noise sources. We find that stochastic neurotransmitter release is a critical component of internally generated variability, causing rapidly diverging, chaotic recurrent network dynamics. Surprisingly, the same nonlinear recurrent network dynamics can transiently overcome the chaos in response to weak feed-forward thalamocortical inputs, and support reliable spike times with millisecond precision. Our model shows that the noisy and chaotic network dynamics of recurrent cortical microcircuitry are compatible with stimulus-evoked, millisecond spike-time reliability, resolving a long-standing debate.

Scaling Spike Detection and Sorting for Next-Generation Electrophysiology.

Reliable spike detection and sorting, the process of assigning each detected spike to its originating neuron, are essential steps in the analysis of extracellular electrical recordings from neurons. The volume and complexity of the data from recently developed large-scale, high-density microelectrode arrays and probes, which allow recording from thousands of channels simultaneously, substantially complicate this task conceptually and computationally. This chapter provides a summary and discussion of recently developed methods to tackle these challenges and discusses the important aspect of algorithm validation, and assessment of detection and sorting quality.

Fine-scale mapping of cortical laminar activity during sleep slow oscillations using high-density linear silicon probes

BackgroundThe cortical slow (∼1 Hz) oscillation (SO), which is thought to play an active role in the consolidation of memories, is a brain rhythm characteristic of slow-wave sleep, with alternating periods of neuronal activity and silence. Although the laminar distribution of cortical activity during SO is well-studied by using linear neural probes, traditional devices have a relatively low (20–100 μm) spatial resolution along cortical layers. New methodIn this work, we demonstrate a high-density linear silicon probe fabricated to record the SO with very high spatial resolution (∼6 μm), simultaneously from multiple cortical layers. Ketamine/xylazine-induced SO was acquired acutely from the neocortex of rats, followed by the examination of the high-resolution laminar structure of cortical activity. ResultsThe probe provided high-quality extracellular recordings, and the obtained cortical laminar profiles of the SO were in good agreement with the literature data. Furthermore, we could record the simultaneous activity of 30–50 cortical single units. Spiking activity of these neurons showed layer-specific differences. Comparison with existing methodsThe developed silicon probe measures neuronal activity with at least a three-fold higher spatial resolution compared with traditional linear probes. By exploiting this feature, we could determine the site of up-state initiation with a higher precision than before. Additionally, increased spatial resolution may provide more reliable spike sorting results, as well as a higher single unit yield. ConclusionsThe high spatial resolution provided by the electrodes allows to examine the fine structure of local population activity during sleep SO in greater detail.

M current regulates firing mode and spike reliability in a collision-detecting neuron.

All animals must detect impending collisions to escape and reliably discriminate them from nonthreatening stimuli, thus preventing false alarms. Therefore, it is no surprise that animals have evolved highly selective and sensitive neurons dedicated to such tasks. We examined a well-studied collision-detection neuron in the grasshopper ( Schistocerca americana) using in vivo electrophysiology, pharmacology, and computational modeling. This lobula giant movement detector (LGMD) neuron is excitable by inputs originating from each ommatidia of the compound eye. It possesses many intrinsic properties that increase its selectivity to objects approaching on a collision course, including switching between burst and nonburst firing. In this study, we demonstrate that the LGMD neuron exhibits a large M current, generated by noninactivating K+ channels, that shortens the temporal window of dendritic integration, regulates a firing mode switch between burst and isolated spiking, increases the precision of spike timing, and increases the reliability of spike propagation to downstream motor centers. By revealing how the M current increases the LGMD's ability to detect impending collisions, our results suggest that similar channels may play an analogous role in other collision detection circuits. NEW & NOTEWORTHY The ability to reliably detect impending collisions is a critical survival skill. The nervous systems of many animals have developed dedicated neurons for accomplishing this task. We used a mix of in vivo electrophysiology and computational modeling to investigate the role of M potassium channels within one such collision-detecting neuron and show that through regulation of burst firing and enhancement of spiking reliability, the M current increases the ability to detect impending collisions.