Reliable Spike Research Articles

Heterogeneity of intrinsic biophysical properties among cochlear nucleus neurons improves the population coding of temporal information

Reliable representation of the spectrotemporal features of an acoustic stimulus is critical for sound recognition. However, if all neurons respond with identical firing to the same stimulus, redundancy in the activity patterns would reduce the information capacity of the population. We thus investigated spike reliability and temporal fluctuation coding in an ensemble of neurons recorded in vitro from the avian auditory brain stem. Sequential patch-clamp recordings were made from neurons of the cochlear nucleus angularis while injecting identical filtered Gaussian white noise currents, simulating synaptic drive. The spiking activity in neurons receiving these identically fluctuating stimuli was highly correlated, measured pairwise across neurons and as a pseudo-population. Two distinct uncorrelated noise stimuli could be discriminated using the temporal patterning, but not firing rate, of the spike trains in the neural ensemble, with best discrimination using information at time scales of 5-20 ms. Despite high cross-correlation values, the spike patterns observed in individual neurons were idiosyncratic, with notable heterogeneity across neurons. To investigate how temporal information is being encoded, we used optimal linear reconstruction to produce an estimate of the original current stimulus from the spike trains. Ensembles of trains sampled across the neural population could be used to predict >50% of the stimulus variation using optimal linear decoding, compared with ∼20% using the same number of spike trains recorded from single neurons. We conclude that heterogeneity in the intrinsic biophysical properties of cochlear nucleus neurons reduces firing pattern redundancy while enhancing representation of temporal information.

Reliability of Noise-Induced Spikes for Two Types of Threshold Dynamics

Spike time reliability (STR) refers to the phenomenon in which repetitive applications of a frozen copy of one stochastic signal to a neuron trigger spikes with reliable timing while a constant signal fails to do so. Focusing on the situation in which the unstimulated neuron is quiescent but close to a switching point for oscillations, we numerically and dynamically analyze STR treating each noise-driven spike occurrence as a time localized event in a model neuron. We study both the averaged properties as well as individual features of spike-evoking epochs. The effects of interactions between spikes is minimized by selecting signals that generate spikes with relatively long interspike intervals. We study two distinct cases characterized by either a smooth or discontinuous dependence of frequency on the applied current. In the setting of noise-induced spiking the frequency content of the input signal has little impact on STR. Rather, increased STR is observed for a certain increase in the average amount of current delivered during a fixed time interval in combination with a favorable time profile. These computational results are complemented by an analytical approximation for the density of the phase difference of two coupled stochastically forced oscillators, considering different relative sizes of the intrinsic and extrinsic noises. When common noise forcing dominates, there is a stronger probability of synchronization related to STR. In contrast, if the intrinsic noises are not of identical strength, there is a synchronized lag between oscillations.

Characterization of a Highly Efficient Blue-shifted Channelrhodopsin from the Marine Alga Platymonas subcordiformis

Rhodopsin photosensors of phototactic algae act as light-gated cation channels when expressed in animal cells. These proteins (channelrhodopsins) are extensively used for millisecond scale photocontrol of cellular functions (optogenetics). We report characterization of PsChR, one of the phototaxis receptors in the alga Platymonas (Tetraselmis) subcordiformis. PsChR exhibited ∼3-fold higher unitary conductance and greater relative permeability for Na(+) ions, as compared with the most frequently used channelrhodopsin-2 from Chlamydomonas reinhardtii (CrChR2). Photocurrents generated by PsChR in HEK293 cells showed lesser inactivation and faster peak recovery than those by CrChR2. Their maximal spectral sensitivity was at 445 nm, making PsChR the most blue-shifted channelrhodopsin so far identified. The λmax of detergent-purified PsChR was 437 nm at neutral pH and exhibited red shifts (pKa values at 6.6 and 3.8) upon acidification. The purified pigment undergoes a photocycle with a prominent red-shifted intermediate whose formation and decay kinetics match the kinetics of channel opening and closing. The rise and decay of an M-like intermediate prior to formation of this putative conductive state were faster than in CrChR2. PsChR mediated sufficient light-induced membrane depolarization in cultured hippocampal neurons to trigger reliable repetitive spiking at the upper threshold frequency of the neurons. At low frequencies spiking probability decreases less with PsChR than with CrChR2 because of the faster recovery of the former. Its blue-shifted absorption enables optogenetics at wavelengths even below 400 nm. A combination of characteristics makes PsChR important for further research on structure-function relationships in ChRs and potentially useful for optogenetics, especially for combinatorial applications when short wavelength excitation is required.

Stochastic Properties of Neurotransmitter Release Expand the Dynamic Range of Synapses

Release of neurotransmitter is an inherently random process, which could degrade the reliability of postsynaptic spiking, even at relatively large synapses. This is particularly important at auditory synapses, where the rate and precise timing of spikes carry information about sounds. However, the functional consequences of the stochastic properties of release are unknown. We addressed this issue at the mouse endbulb of Held synapse, which is formed by auditory nerve fibers onto bushy cells (BCs) in the anteroventral cochlear nucleus. We used voltage clamp to characterize synaptic variability. Dynamic clamp was used to compare BC spiking with stochastic or deterministic synaptic input. The stochastic component increased the responsiveness of the BC to conductances that were on average subthreshold, thereby increasing the dynamic range of the synapse. This had the benefit that BCs relayed auditory nerve activity even when synapses showed significant depression during rapid activity. However, the precision of spike timing decreased with stochastic conductances, suggesting a trade-off between encoding information in spike timing versus probability. These effects were confirmed in fiber stimulation experiments, indicating that they are physiologically relevant, and that synaptic randomness, dynamic range, and jitter are causally related.

A Computational Study of Spike Time Reliability in Two Types of Threshold Dynamics

Spike time reliability (STR) refers to the phenomenon in which repetitive applications of a frozen copy of one stochastic signal to a neuron trigger spikes with reliable timing while a constant signal fails to do so. Observed and explored in numerous experimental and theoretical studies, STR is a complex dynamic phenomenon depending on the nature of external inputs as well as intrinsic properties of a neuron. The neuron under consideration could be either quiescent or spontaneously spiking in the absence of the external stimulus. Focusing on the situation in which the unstimulated neuron is quiescent but close to a switching point to oscillations, we numerically analyze STR treating each spike occurrence as a time localized event in a model neuron. We study both the averaged properties as well as individual features of spike-evoking epochs (SEEs). The effects of interactions between spikes is minimized by selecting signals that generate spikes with relatively long interspike intervals (ISIs). Under these conditions, the frequency content of the input signal has little impact on STR. We study two distinct cases, Type I in which the f–I relation (f for frequency, I for applied current) is continuous and Type II where the f–I relation exhibits a jump. STR in the two types shows a number of similar features and differ in some others. SEEs that are capable of triggering spikes show great variety in amplitude and time profile. On average, reliable spike timing is associated with an accelerated increase in the “action” of the signal as a threshold for spike generation is approached. Here, “action” is defined as the average amount of current delivered during a fixed time interval. When individual SEEs are studied, however, their time profiles are found important for triggering more precisely timed spikes. The SEEs that have a more favorable time profile are capable of triggering spikes with higher precision even at lower action levels.

Self-tuning of inhibition by endocannabinoids shapes spike-time precision in CA1 pyramidal neurons

In the hippocampus, activity-dependent changes of synaptic transmission and spike-timing coordination are thought to mediate information processing for the purpose of memory formation. Here, we investigated the self-tuning of intrinsic excitability and spiking reliability by CA1 hippocampal pyramidal cells via changes of their GABAergic inhibitory inputs and endocannabinoid (eCB) signaling. Firing patterns of CA1 place cells, when replayed in vitro, induced an eCB-dependent transient reduction of spontaneous GABAergic activity, sharing the main features of depolarization-induced suppression of inhibition (DSI), and conditioned a transient improvement of spike-time precision during consecutive burst discharges. When evaluating the consequences of DSI on excitatory postsynaptic potential (EPSP)-spike coupling, we found that transient reductions of uncorrelated (spontaneous) or correlated (feedforward) inhibition improved EPSP-spike coupling probability. The relationship between EPSP-spike-timing reliability and inhibition was, however, more complex: transient reduction of correlated (feedforward) inhibition disrupted or improved spike-timing reliability according to the initial spike-coupling probability. Thus eCB-mediated tuning of pyramidal cell spike-time precision is governed not only by the initial level of global inhibition, but also by the ratio between spontaneous and feedforward GABAergic activities. These results reveal that eCB-mediated self-tuning of spike timing by the discharge of pyramidal cells can constitute an important contribution to place-cell assemblies and memory formation in the hippocampus.

The role of inhibition in the generation of reliable spike sequences

Coordinated spiking of neuronal populations underlies the perception of sensory stimuli, the planning of movement, and the acquisition of new memories. In the hippocampus and the entorhinal cortex, groups of neurons are transiently activated as an animal traverses its environment. The sequential patterns generated by these neurons depend, not only on environmental cues, but can also be generated reliably in the absence of these cues. Such patterning depends on the temporal and the spatial distribution of inhibition. It has been hypothesized that inhibitory interneurons corral principal neurons into transiently synchronous ensembles that encode sensory information and sub-serve behavior. The goal of this study is to demonstrate a link between the topology of the inhibitory sub-network and the dynamics of its constituent neurons that, in turn, support the generation of reliable sequences of spiking activity. An understanding of structure-dynamics relationships in complex networks is often challenging because of the formidable dimensionality of the system and the inherent non-linear properties of neurons and synapses that constitute the network. In an earlier study [1] we established a relationship between a structural property of the network, its colorings (a proper vertex coloring of a network is one that assigns different colors to nodes that are directly connected to each other), and the dynamics it constrains. Competitive inhibitory interactions ensure that neurons associated with the same color generate spikes in approximate synchrony. Further we showed that the description of network dynamics based on its coloring comes with a fortuitous benefit. We used the coloring of the inhibitory sub-network as a tool to construct a space in which the distance between excitatory neurons is defined not by the length of the synaptic path connecting those neurons but by the similarity of the inhibitory input they receive. In this reconfigured space, the seemingly high-dimensional dynamics of networks of randomly connected excitatory and inhibitory neurons reliably forms a series of orthogonally propagating waves, a predictable and simple pattern where synchronous ensembles of excitatory neurons are successively recruited. The coloring constrains the identity of inhibitory neurons that spike synchronously. However, it does not impose any constraints on the order in which synchronous groups are recruited. The propensity of a particular group of neurons to spike can be attributed to slow temporal processes such as adaptive changes in the calcium concentration within the cell and slow oscillatory drive over time scales comparable to that of theta oscillations that are observed in the hippocampus and the entorhinal cortex. In this study we systematically vary the topology of the network (exemplified by it's coloring) and the timescale of slow temporal processes to understand how the two interact to construct reliable sequences of activity.

Chaos and reliability in balanced spiking networks with temporal drive.

Biological information processing is often carried out by complex networks of interconnected dynamical units. A basic question about such networks is that of reliability: If the same signal is presented many times with the network in different initial states, will the system entrain to the signal in a repeatable way? Reliability is of particular interest in neuroscience, where large, complex networks of excitatory and inhibitory cells are ubiquitous. These networks are known to autonomously produce strongly chaotic dynamics-an obvious threat to reliability. Here, we show that such chaos persists in the presence of weak and strong stimuli, but that even in the presence of chaos, intermittent periods of highly reliable spiking often coexist with unreliable activity. We elucidate the local dynamical mechanisms involved in this intermittent reliability, and investigate the relationship between this phenomenon and certain time-dependent attractors arising from the dynamics. A conclusion is that chaotic dynamics do not have to be an obstacle to precise spike responses, a fact with implications for signal coding in large networks.

Intrinsic firing properties in the avian auditory brain stem allow both integration and encoding of temporally modulated noisy inputs in vitro

The intrinsic properties of tonically firing neurons in the cochlear nucleus contribute to representing average sound intensity by favoring synaptic integration across auditory nerve inputs, reducing phase locking to fine temporal acoustic structure and enhancing envelope locking. To determine whether tonically firing neurons of the avian cochlear nucleus angularis (NA) resemble ideal integrators, we investigated their firing responses to noisy current injections during whole cell patch-clamp recordings in brain slices. One subclass of neurons (36% of tonically firing neurons, mainly subtype tonic III) showed no significant changes in firing rate with noise fluctuations, acting like pure integrators. In contrast, many tonically firing neurons (>60%, mainly subtype tonic I or II) showed a robust sensitivity to noisy current fluctuations, increasing their firing rates with increased fluctuation amplitudes. For noise-sensitive tonic neurons, the firing rate vs. average current curves with noise had larger maximal firing rates, lower gains, and wider dynamic ranges compared with FI curves for current steps without noise. All NA neurons showed fluctuation-driven patterning of spikes with a high degree of temporal reliability and millisecond spike time precision. Single-spiking neurons in NA also responded to noisy currents with higher firing rates and reliable spike trains, although less precisely than nucleus magnocellularis neurons. Thus some NA neurons function as integrators by encoding average input levels over wide dynamic ranges regardless of current fluctuations, others detect the degree of coherence in the inputs, and most encode the temporal patterns contained in their inputs with a high degree of precision.

Predicting spike occurrence and neuronal responsiveness from LFPs in primary somatosensory cortex.

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role.

Fluctuating Inhibitory Inputs Promote Reliable Spiking at Theta Frequencies in Hippocampal Interneurons

Theta-frequency (4–12 Hz) rhythms in the hippocampus play important roles in learning and memory. CA1 interneurons located at the stratum lacunosum-moleculare and radiatum junction (LM/RAD) are thought to contribute to hippocampal theta population activities by rhythmically pacing pyramidal cells with inhibitory postsynaptic potentials. This implies that LM/RAD cells need to fire reliably at theta frequencies in vivo. To determine whether this could occur, we use biophysically based LM/RAD model cells and apply different cholinergic and synaptic inputs to simulate in vivo-like network environments. We assess spike reliabilities and spiking frequencies, identifying biophysical properties and network conditions that best promote reliable theta spiking. We find that synaptic background activities that feature large inhibitory, but not excitatory, fluctuations are essential. This suggests that strong inhibitory input to these cells is vital for them to be able to contribute to population theta activities. Furthermore, we find that Type I-like oscillator models produced by augmented persistent sodium currents (INaP) or diminished A-type potassium currents (IA) enhance reliable spiking at lower theta frequencies. These Type I-like models are also the most responsive to large inhibitory fluctuations and can fire more reliably under such conditions. In previous work, we showed that INaP and IA are largely responsible for establishing LM/RAD cells’ subthreshold activities. Taken together with this study, we see that while both these currents are important for subthreshold theta fluctuations and reliable theta spiking, they contribute in different ways – INaP to reliable theta spiking and subthreshold activity generation, and IA to subthreshold activities at theta frequencies. This suggests that linking subthreshold and suprathreshold activities should be done with consideration of both in vivo contexts and biophysical specifics.

Separability of Adjacent Neurons Recorded with a CMOS-Multi-Transistor-Array

Event Abstract Back to Event Separability of Adjacent Neurons Recorded with a CMOS-Multi-Transistor-Array Christian Leibig1, 2, 3*, Armin Lambacher4, Thomas Wachtler1, 5 and Günther Zeck2 1 Ludwig-Maximilians-Universität München, Department Biology II, Germany 2 Natural and Medical Sciences Institute at the University of Tübingen, Germany 3 University of Tübingen, Graduate School for Neural Information Processing, Germany 4 Max-Planck-Institute of Biochemistry, Membrane- and Neurophysics, Germany 5 Bernstein Center for Computational Neuroscience Munich, Germany Background/Aims In order to understand the functionality of neural networks, reliable parallel spike trains must be recorded from neuronal ensembles [1, 2]. Silicon-based multi-transistor arrays ('Neurochips') are large enough to map the activity of hundreds to thousands of neurons within an area of 1 mm^2. In order to retrieve reliable parallel spike trains, action potentials (APs) have to be properly assigned to their corresponding units. The signal separation of adjacent neurons in neural tissue or cell culture represents a serious challenge, as their electrical coupling areas on the sensor array overlap. In this work we aim to determine whether this problem can be solved based on extracellular action potential waveforms together with their positional information. Methods A potential algorithm for assigning APs to corresponding units was presented in [3]. Here, we test its capabilities by using synthetic data with known positions, signal shapes and amplitudes of the extracellular signal. The synthetic data were generated from recordings of four retinal ganglion cells (RGCs). Measurements were done with a CMOS-multi-transistor array (size 1 mm^2, pitch 7.4 µm, sampling rate 78 kHz). For each RGC several hundred threshold crossings were averaged and down sampled to either 11.5 or 23 kHz respectively. Gaussian noise was added with appropriate amplitude to obtain a signal-to-noise ratio of 11 [4]. Thus a template was obtained for each RGC. Finally, synthetic data sets were generated by combining template pairs at different distances from each other (0, 7.4, 14.8 or 22.2 µm) and fed to the algorithm described in [3]. The quality of spike sorting was quantified by an error rate defined as the sum of false positive and false negative assignments with respect to the known ground truth. This procedure was carried out for two sampling rates (11.5 and 23 kHz). Results We compared spikes from pairs of different neuronal templates with centres of electrical coupling areas either aligned on each other or separated by multiples of 7.4 µm. For both temporal sampling rates, spikes could be reliably separated down to the smallest separation distance (0 µm). Conclusion/Summary For given conditions (signal-to-noise ratio, noise distribution and templates) our results provide parameter settings for which spiking activity can be reliably assigned to the corresponding source. Acknowledgements Supported by BMBF grant 0312038.

Morphological and Functional Continuum Underlying Heterogeneity in the Spiking Fidelity at the Calyx of Held SynapseIn Vitro

Reliable neuronal spiking is critical for a myriad of computations performed by neural circuits. This is particularly evident for sound localization cues in the auditory brainstem circuits that detect timing and intensity differences of sounds arriving at two ears. The calyx of Held-principal neuron synapse in the medial nucleus of the trapezoid body (MNTB) in this circuit is traditionally viewed as a reliable relay, which converts contralateral excitatory inputs to inhibitory outputs to ipsilateral superior olive neurons that code interaural timing and intensity differences. However, recent studies demonstrated large variability in the incidence of postsynaptic spike failures at this synapse, challenging the view that this synapse is a fail-safe relay. Using combined imaging and paired recordings in mature (P16-P19) mouse brainstem slices, we show that spike failure rates of MNTB neurons are strongly correlated with differences in gross morphology of the calyx terminal and quantal properties under standard in vitro- and in vivo-like conditions. MNTB neurons innervated by calyces with simple morphologies (mainly digits) express strong short-term synaptic depression and a high incidence of spike failures after high-frequency stimulation. Conversely, MNTB neurons innervated by structurally complex calyces (digits and numerous bouton-like swellings) exhibit initial facilitation followed by slow depression and very few spike failures. Our results indicate that the calyx of Held-MNTB synapse is likely organized as a structural and functional continuum, in that correlated heterogeneities in calyx morphology and short-term plasticity serve as a filter for regulating the inhibition delivered to superior olive neurons during sound localization.

Relative spike time coding and STDP-based orientation selectivity in the early visual system in natural continuous and saccadic vision: a computational model

We have built a phenomenological spiking model of the cat early visual system comprising the retina, the Lateral Geniculate Nucleus (LGN) and V1's layer 4, and established four main results (1) When exposed to videos that reproduce with high fidelity what a cat experiences under natural conditions, adjacent Retinal Ganglion Cells (RGCs) have spike-time correlations at a short timescale (~30 ms), despite neuronal noise and possible jitter accumulation. (2) In accordance with recent experimental findings, the LGN filters out some noise. It thus increases the spike reliability and temporal precision, the sparsity, and, importantly, further decreases down to ~15 ms adjacent cells' correlation timescale. (3) Downstream simple cells in V1's layer 4, if equipped with Spike Timing-Dependent Plasticity (STDP), may detect these fine-scale cross-correlations, and thus connect principally to ON- and OFF-centre cells with Receptive Fields (RF) aligned in the visual space, and thereby become orientation selective, in accordance with Hubel and Wiesel (Journal of Physiology 160:106-154, 1962) classic model. Up to this point we dealt with continuous vision, and there was no absolute time reference such as a stimulus onset, yet information was encoded and decoded in the relative spike times. (4) We then simulated saccades to a static image and benchmarked relative spike time coding and time-to-first spike coding w.r.t. to saccade landing in the context of orientation representation. In both the retina and the LGN, relative spike times are more precise, less affected by pre-landing history and global contrast than absolute ones, and lead to robust contrast invariant orientation representations in V1.

Striatal cellular properties conserved from lampreys to mammals

The striatum of the lamprey, the first vertebrate group to appear in evolution, shows striking similarities to that of mammals with respect to histochemical markers, afferent and efferent projections and the effect of dopamine depletion, which leads to hypokinetic motor symptoms. The cellular properties of lamprey striatal neurons were studied here using patch-clamp recordings in acute striatal slices. Sixty-five per cent of recorded neurons were characterised by a prominent inward rectification due to a K+ conductance of the Kir type. They had a ramping response with a long delay to the first action potential due to activation of a low-voltage-activated A-type K+ current. Many such inwardly rectifying neurons (IRNs) had a hyperpolarised resting membrane potential and some had spiny dendrites. The remaining 35% of the neurons (non-IRNs) represent a heterogeneous group, including some with characteristics similar to the fast-spiking interneuron of the mammalian striatum. They showed short-lasting, large after hyperpolarisations (AHPs) and discharged action potentials at high frequency. None of the recorded neurons were spontaneously active but they received GABAergic and glutamatergic synaptic input. The fact that most lamprey striatal neurons display inward rectification indicates that this is a conserved characteristic of striatal neurons throughout vertebrate phylogeny. This is a cellular property of critical importance for the operations of the striatum in mammals.

Head direction cells in the postsubiculum do not show replay of prior waking sequences during sleep

During slow-wave sleep (SWS) and rapid eye movement (REM) sleep, hippocampal place cells in the rat show replay of sequences previously observed during waking. We tested the hypothesis from computational modeling that the temporal structure of REM sleep replay could arise from an interplay of place cells with head direction cells in the postsubiculum. Physiological single-unit recording was performed simultaneously from five or more head direction or place by head direction cells in the postsubiculum during running on a circular track allowing sampling of a full range of head directions, and during sleep periods before and after running on the circular track. Data analysis compared the spiking activity during individual REM periods with waking as in previous analysis procedures for REM sleep. We also used a new procedure comparing groups of similar runs during waking with REM sleep periods. There was no consistent evidence for a statistically significant correlation of the temporal structure of spiking during REM sleep with spiking during waking running periods. Thus, the spiking activity of head direction cells during REM sleep does not show replay of head direction cell activity occurring during a previous waking period of running on the task. In addition, we compared the spiking of postsubiculum neurons during hippocampal sharp wave ripple events. We show that head direction cells are not activated during sharp wave ripples, whereas neurons responsive to place in the postsubiculum show reliable spiking at ripple events.

Designing optimal stimuli to control neuronal spike timing

Recent advances in experimental stimulation methods have raised the following important computational question: how can we choose a stimulus that will drive a neuron to output a target spike train with optimal precision, given physiological constraints? Here we adopt an approach based on models that describe how a stimulating agent (such as an injected electrical current or a laser light interacting with caged neurotransmitters or photosensitive ion channels) affects the spiking activity of neurons. Based on these models, we solve the reverse problem of finding the best time-dependent modulation of the input, subject to hardware limitations as well as physiologically inspired safety measures, that causes the neuron to emit a spike train that with highest probability will be close to a target spike train. We adopt fast convex constrained optimization methods to solve this problem. Our methods can potentially be implemented in real time and may also be generalized to the case of many cells, suitable for neural prosthesis applications. With the use of biologically sensible parameters and constraints, our method finds stimulation patterns that generate very precise spike trains in simulated experiments. We also tested the intracellular current injection method on pyramidal cells in mouse cortical slices, quantifying the dependence of spiking reliability and timing precision on constraints imposed on the applied currents.

How reliable is spike train reconstruction from Calcium imaging data?

How reliable is spike train reconstruction from Calcium imaging data?

A Robust and Biologically Plausible Spike Pattern Recognition Network

The neural mechanisms that enable recognition of spiking patterns in the brain are currently unknown. This is especially relevant in sensory systems, in which the brain has to detect such patterns and recognize relevant stimuli by processing peripheral inputs; in particular, it is unclear how sensory systems can recognize time-varying stimuli by processing spiking activity. Because auditory stimuli are represented by time-varying fluctuations in frequency content, it is useful to consider how such stimuli can be recognized by neural processing. Previous models for sound recognition have used preprocessed or low-level auditory signals as input, but complex natural sounds such as speech are thought to be processed in auditory cortex, and brain regions involved in object recognition in general must deal with the natural variability present in spike trains. Thus, we used neural recordings to investigate how a spike pattern recognition system could deal with the intrinsic variability and diverse response properties of cortical spike trains. We propose a biologically plausible computational spike pattern recognition model that uses an excitatory chain of neurons to spatially preserve the temporal representation of the spike pattern. Using a single neural recording as input, the model can be trained using a spike-timing-dependent plasticity-based learning rule to recognize neural responses to 20 different bird songs with >98% accuracy and can be stimulated to evoke reverse spike pattern playback. Although we test spike train recognition performance in an auditory task, this model can be applied to recognize sufficiently reliable spike patterns from any neuronal system.

Desynchronization of Neocortical Networks by Asynchronous Release of GABA at Autaptic and Synaptic Contacts from Fast-Spiking Interneurons

Author SummaryIn the cerebral cortex (neocortex) of the brain, fast-spiking (FS) inhibitory cells contact many principal pyramidal (P) neurons on their cell bodies, which allows the FS cells to control the generation of action potentials (neuronal output). FS-cell-mediated rhythmic and synchronous inhibition drives coherent network oscillations of large ensembles of P neurons, indicating that FS interneurons are needed for the precise timing of cortical circuits. Interestingly, FS cells are self-innervated by GABAergic autaptic contacts, whose synchronous activation regulates FS-cell precise firing. Here we report that high-frequency firing in FS interneurons results in a massive (>10-fold), delayed, and prolonged (for seconds) increase in inhibitory events, occurring at both autaptic (FS–FS) and synaptic (FS–P) sites. This increased inhibition is due to asynchronous release of GABA from presynaptic FS cells. Delayed and disorganized asynchronous inhibitory responses significantly affected the input–output properties of both FS and P neurons, suggesting that asynchronous release of GABA might promote network desynchronization. FS interneurons can fire at high frequency (>100 Hz) in vitro and in vivo, and are known for their reliable and precise signaling. Our results show an unprecedented action of these cells, by which their tight temporal control of cortical circuits can be broken when they are driven to fire above certain frequencies.