Spike Synchronization Research Articles

Microsaccades enable efficient synchrony-based coding in the retina: a simulation study.

It is now reasonably well established that microsaccades (MS) enhance visual perception, although the underlying neuronal mechanisms are unclear. Here, using numerical simulations, we show that MSs enable efficient synchrony-based coding among the primate retinal ganglion cells (RGC). First, using a jerking contrast edge as stimulus, we demonstrate a qualitative change in the RGC responses: synchronous firing, with a precision in the 10 ms range, only occurs at high speed and high contrast. MSs appear to be sufficiently fast to be able reach the synchronous regime. Conversely, the other kinds of fixational eye movements known as tremor and drift both hardly synchronize RGCs because of a too weak amplitude and a too slow speed respectively. Then, under natural image stimulation, we find that each MS causes certain RGCs to fire synchronously, namely those whose receptive fields contain contrast edges after the MS. The emitted synchronous spike volley thus rapidly transmits the most salient edges of the stimulus, which often constitute the most crucial information. We demonstrate that the readout could be done rapidly by simple coincidence-detector neurons without knowledge of the MS landing time, and that the required connectivity could emerge spontaneously with spike timing-dependent plasticity.

Post-Inhibitory Rebound Spikes in Rat Medial Entorhinal Layer II/III Principal Cells: In Vivo, In Vitro, and Computational Modeling Characterization.

Medial entorhinal cortex Layer-II stellate cells (mEC-LII-SCs) primarily interact via inhibitory interneurons. This suggests the presence of alternative mechanisms other than excitatory synaptic inputs for triggering action potentials (APs) in stellate cells during spatial navigation. Our intracellular recordings show that the hyperpolarization-activated cation current (Ih) allows post-inhibitory-rebound spikes (PIRS) in mEC-LII-SCs. In vivo, strong inhibitory-post-synaptic potentials immediately preceded most APs shortening their delay and enhancing excitability. In vitro experiments showed that inhibition initiated spikes more effectively than excitation and that more dorsal mEC-LII-SCs produced faster and more synchronous spikes. In contrast, PIRS in Layer-II/III pyramidal cells were harder to evoke, voltage-independent, and slower in dorsal mEC. In computational simulations, mEC-LII-SCs morphology and Ih homeostatically regulated the dorso-ventral differences in PIRS timing and most dendrites generated PIRS with a narrow range of stimulus amplitudes. These results suggest inhibitory inputs could mediate the emergence of grid cell firing in a neuronal network.

Phase Locking of Multiple Single Neurons to the Local Field Potential in Cat V1.

The local field potential (LFP) is thought to reflect a temporal reference for neuronal spiking, which may facilitate information coding and orchestrate the communication between neural populations. To explore this proposed role, we recorded the LFP and simultaneously the spike activity of one to three nearby neurons in V1 of anesthetized cats during the presentation of drifting sinusoidal gratings, binary dense noise stimuli, and natural movies. In all stimulus conditions and during spontaneous activity, the average LFP power at frequencies >20 Hz was higher when neurons were spiking versus not spiking. The spikes were weakly but significantly phase locked to all frequencies of the LFP. The average spike phase of the LFP was stable across high and low levels of LFP power, but the strength of phase locking at low frequencies (≤10 Hz) increased with increasing LFP power. In a next step, we studied how strong stimulus responses of single neurons are reflected in the LFP and the LFP-spike relationship. We found that LFP power was slightly increased and phase locking was slightly stronger during strong compared with weak stimulus-locked responses. In summary, the coupling strength between high frequencies of the LFP and spikes was not strongly modulated by LFP power, which is thought to reflect spiking synchrony, nor was it strongly influenced by how strongly the neuron was driven by the stimulus. Furthermore, a comparison between neighboring neurons showed no clustering of preferred LFP phase. We argue that hypotheses on the relevance of phase locking in their current form are inconsistent with our findings.

Feature-Based Attention by Lateral Spike Synchronization.

We introduce a neural model capable of feature selectiveness by spike-mediated synchronization through lateral synaptic couplings. For a stimulus containing two features, the attended one elicits a higher response. In the case of sequential single-feature stimuli, repetition of the attended feature also results in an enhanced response, exhibited by greater synchrony and higher spiking rates.

Functional congruity in local auditory cortical microcircuits

Functional columns of primary auditory cortex (AI) are arranged in layers, each composed of highly connected fine-scale networks. The basic response properties and interactions within these local subnetworks have only begun to be assessed. We examined the functional diversity of neurons within the laminar microarchitecture of cat AI to determine the relationship of spectrotemporal processing between neighboring neurons. Neuronal activity was recorded across the cortical layers while presenting a dynamically modulated broadband noise. Spectrotemporal receptive fields (STRFs) and their nonlinear input/output functions (nonlinearities) were constructed for each neuron and compared for pairs of neurons simultaneously recorded at the same contact site. Properties of these local neuron pairs showed greater similarity than non-paired neurons within the same column for all considered parameters including firing rate, envelope-phase precision, preferred spectral and temporal modulation frequency, as well as for the threshold and transition of the response nonlinearity. This higher functional similarity of paired versus non-paired neurons was most apparent in infragranular neuron pairs, and less for local supragranular and granular pairs. The functional similarity of local paired neurons for firing rate, best temporal modulation frequency and two nonlinearity aspects was laminar dependent, with infragranular local pair-wise differences larger than for granular or supragranular layers.Synchronous spiking events between pairs of neurons revealed that simultaneous ‘Bicellular’ spikes, in addition to carrying higher stimulus information than non-synchronized spikes, encoded faster modulation frequencies. Bicellular functional differences to the best matched of the paired neurons could be substantial. Bicellular nonlinearities showed that synchronous spikes act to transmit stimulus information with higher fidelity and precision than non-synchronous spikes of the individual neurons, thus, likely enhancing stimulus feature selectivity in their target neurons. Overall, the well-correlated and temporally precise processing within local subnetworks of cat AI showed laminar-dependent functional diversity in spectrotemporal processing, despite high intra-columnar congruity in frequency preference.

PATH MULTIMODALITY IN A FEEDFORWARD SNN MODULE, USING LIF WITH LATENCY MODEL

In this paper, the network transmission properties of a feedforward Spiking Neural Network (SNN) affected by synchronous stimuli are investigated with respect to the connection probability and the synaptic strengths. By means of an event-driven method, all simulations are conducted using the Leaky Integrateand-Fire with Latency (LIFL) model. Typical cases are taken into consideration, in which a network section (module) is able to process the input information, introducing a particular behavior, that we have called path multimodality. Simulation results are discussed. Through this phenomenon, the output layer of the network can generate a number of temporally spaced groups of synchronous spikes. The multimodality effect could be applied for various purposes, for instance in coding or else transmission issues

On the Spatial Dynamics of a Network Spike in Neuronal Cultures

Event Abstract Back to Event On the Spatial Dynamics of a Network Spike in Neuronal Cultures Dmitry Zendrikov1* and Alexander Paraskevov2 1 Moscow Institute of Physics and Technology (State University), Russia 2 National Research Centre "Kurchatov Institute", Russia There exists a short-term (~100 ms), repetitive, spontaneous synchronization of network spiking activity in planar neuronal networks grown in vitro from initially dissociated cortical or hippocampal neurons [1]. Such networks are called neuronal cultures while a single synchronization event is called a network spike (NS) or a population burst. The stationary regime of a NS activity in neuronal cultures is thought to be related to epilepsy therefore both the origin and the properties of such a regime is a subject of intensive studies. In a recent article [2] it was shown experimentally that spatial dynamics of a typical NS is inhomogeneous: there exist a few occasional local sources, or nucleation centers, from which the synchronous spiking activity propagates in the neuronal culture as traveling waves. The number and spatial locations of such sources are unique and steady for the specified culture of excitatory neurons, and these quantities vary over the different cultures. By generalizing the NS simulation model [3] to the case of a spatially-dependent network topology, we show that nucleation centers of a NS occur in simulations at certain conditions. In particular, it is shown that (i) spatial nucleation centers of a network spike appear if the majority of connections between neurons are the local ones, (ii) nucleation centers are not nested in fluctuations of spatial density of neurons, (iii) inhibitory neurons (a) decrease the average frequency of NS occurrence, (b) increase the NS amplitude variability, the NS duration and the number of nucleation centers, (c) do not affect essentially the degree (i.e., coefficient of variation) of periodicity of NSs and the level of spiking activity between the subsequent NSs.

Linking Essential Tremor to the Cerebellum-Animal Model Evidence.

In this review, we hope to stimulate interest in animal models as opportunities to understand tremor mechanisms within the cerebellar system. We begin by considering the harmaline model of essential tremor (ET), which has ET-like anatomy and pharmacology. Harmaline induces the inferior olive (IO) to burst fire rhythmically, recruiting rhythmic activity in Purkinje cells (PCs) and deep cerebellar nuclei (DCN). This model has fostered the IO hypothesis of ET, which postulates that factors that promote excess IO, and hence PC complex spike synchrony, also promote tremor. In contrast, the PC hypothesis postulates that partial PC cell loss underlies tremor of ET. We describe models in which chronic partial PC loss is associated with tremor, such as the Weaver mouse, and others with PC loss that do not show tremor, such as the Purkinje cell degeneration mouse. We postulate that partial PC loss with tremor is associated with terminal axonal sprouting. We then discuss tremor that occurs with large lesions of the cerebellum in primates. This tremor has variable frequency and is an ataxic tremor not related to ET. Another tremor type that is not likely related to ET is tremor in mice with mutations that cause prolonged synaptic GABA action. This tremor is probably due to mistiming within cerebellar circuitry. In the final section, we catalog tremor models involving neurotransmitter and ion channel perturbations. Some appear to be related to the IO hypothesis of ET, while in others tremor may be ataxic or due to mistiming. In summary, we offer a tentative framework for classifying animal action tremor, such that various models may be considered potentially relevant to ET, subscribing to IO or PC hypotheses, or not likely relevant, as with mistiming or ataxic tremor. Considerable further research is needed to elucidate the mechanisms of tremor in animal models.

Multiplexed coding through synchronous and asynchronous spiking

The prodigious capacity of our brain to process information relies on efficient neural coding strategies. In engineered systems, bandwidth is often increased through multiplexing multiple signals are simultaneously, yet independently, transmitted through a single communication channel. We have proposed previously that neural systems might implement the same sort of solution [1]. Here, we tested if/how multiplexed coding could be achieved through combined rate and temporal coding. We hypothesized that a set of neurons could independently encode two signals by using asynchronous spike rate to encode one signal and synchronous spike timing to encode the other. To test our hypothesis, we built a feed-forward neural network comprising Morris-Lecar (ML) model neurons. All neurons received a common input constructed from two distinct signals, slow and fast, plus uncorrelated fast noise. According to our hypothesis, slow and fast signals are independently encoded by different types of spikes; in other words, differentially correlated output spikes, namely, asynchronous (Async) and synchronous (Sync), enable encoding of slow and fast signals, respectively. To assess the feasibility of the multiplexed coding scheme, recorded spikes were classified into two independent classes based on the peristimulus time histogram (PSTH) calculated from the entire set of neurons. Spikes whose instantaneous rates exceeded a threshold were designated “Sync” and all others were designated “Async”. The spike triggered average (STA) was calculated for slow and fast signals using Sync and Async spikes, resulting in four different STAs. The Async-slow and Sync-fast STAs were clearly structured whereas the other two were not (Figure 1A). Using

Robust estimation of millisecond timescale synchrony under nonstationary conditions and its physiological interpretation

Motivated by the observation of millisecond-timescale synchrony among hippocampal neurons in high-density extracellular recordings of freely moving rats [1], we developed and applied a model for detecting synchrony under such conditions. We calibrated the model using data of a putative monosynaptic connection, obtained in paired extracellular-intracellular recordings in anesthetized rats [2]. There has been a great deal of interest in detecting synchrony, and more generally fine-temporal correlation, in the firing activities of pairs of neurons. In certain settings, a common question is whether this fine-temporal structure is supposed to reflect either a direct synaptic connection between these cells (the monosynaptic connection hypothesis), or a larger network organization in which these cells are embedded (the common modulation hypothesis). Here, we studied a specific related problem: what is a proper estimate of the amount of synchrony between two spike trains? We modeled the problem based on a technique we call separation of time scale model. Among synchronous spikes, a spike can belong either to a fast timescale process, or to a slow timescale process, called background activity; non-synchronous spikes belong to background activity. The definition of timescale for these processes is based on conditional modeling tools, as developed in previous studies [3], and is designed to accommodate even extreme forms of nonstationarity in the underlying spike processes. Under this framework, we can either test the hypothesis that the activities of these two cells are uncorrelated on a fine-timescale, or estimate the number of synchronous spikes due to fine-timescale processes. It was argued however that, in the context of related models, resulting statistical hypothesis tests have low power and estimators of synchrony are biased [4]. An alternative method was proposed and observed to offer a high power test and unbiased estimation in numerical simulations. We studied this alternative approach and framed it within our separation-of-timescale model. We develop hypothesis tests and unbiased estimators and interpret and clarify previous results in this context. We applied the developed method to large-scale recordings of neuronal populations in the cornu ammonis 1 (CA1) and CA3 regions of the hippocampus of freely moving rats [1]. Finally, we also simulated a simple biophysical model of a monosynaptic connection, and demonstrated the relevance of our method in this mechanistic context. Nevertheless, this method is based solely on an analysis of spike trains, which give a restricted representation of a cell's `true' activity and result from numerous steps of statistical inference (e.g. spike sorting). Any conclusion based on such analysis should be therefore trusted with caution. We examined an experimental dataset where a fine-temporal structure was identified using paired extracellular-intracellular in vivo recordings, in the enthorinal cortex of the anaesthetized rat [2]. This dataset allowed us to calibrate our spike train analysis method (i.e. supervised learning), and to advance existing methodologies for identifying functional connectivity from in vivo recording.

Mechanisms for synchronized burst firing in pyramidal cells using oscillatory inhibition: a model for attentional control

Pyramidal cells have two different action potential initiation sites; one at the soma, which receives inputs from synapses contacting the basal dendrites or the soma, and one at the distal apical dendrite and tuft [1]. At the latter, calcium spikes can be elicited resulting in a plateau potential due to the influx of calcium that causes the pyramidal cell to fire bursts of action potentials [2]. This mechanism can be used to enhance the effect of top-down synaptic inputs that are received on the distal apical dendrite and tuft on activating its downstream targets. Calcium spikes can be elicited either by convergence of forward-propagating inputs at the tuft or by simultaneous depolarization from back-propagating action potentials and excitation at the tuft [3], referred to as FAC and BAC, respectively. We investigated whether these two firing patterns can be synchronized by oscillatory inhibition in order to transmit top-down or combined top-down and bottom-up information to different brain areas. We hypothesize this mechanism as an explanation for the synchronization of burst spiking to distant local field potentials at beta frequencies (12-20 Hz) that was recorded in the prefrontal cortex and the anterior cingulate cortex during attentional control [4]. We simulated the behavior of pyramidal cells stimulated by oscillatory inhibition at the tuft and noisy excitatory inputs at the basal dendrite and tuft. First, we investigated the occurrence and phase-locking of FAC and BAC firing in one cell as function of the ratio of excitatory basal and tuft inputs. Second, we examined the synchronization of a population of pyramidal cells downstream to oscillatory inhibition with frequencies in the beta range. The pyramidal cells were modeled using four compartments and including calcium dynamics in the apical dendrite and tuft [5]. For single pyramidal cells FACs predominantly occur when the tuft receives stronger excitation than the basal dendrites, while BACs occur predominantly when the tuft and basal dendrites are excited equally strong. Both mechanisms have in common that they dominate the behavior of the pyramidal cell only when oscillatory inhibition is in the lower frequency range (2-20 Hz). For a 1:2 ratio between excitatory basal and tuft input FACs accounted for up to 85% of all spike events, whereas for a ratio of 1:1 BACs constituted up to 68%. Bursts activated by FACs and BACs were strongly phase-locked to the inhibitory drive, while bursts activated by other mechanisms had a lower coherence. The synchrony between pairs of pyramidal cells was quantified using the Schreiber measure [6] with the smoothing parameter set to σ=5 ms. When stimulated in the beta frequency range the synchrony between pairs of pyramidal cells was on average 0.78. Taken together, the results show that FAC and BAC firing can be used in conjunction with oscillatory inhibition to produce synchronized burst firing in pyramidal cells. This provides an effective means to transmit top-down or combined top-down and bottom-up information downstream.

Curvature of dendritic nonlinearities modulates higher-order spiking correlations

Characterizing neural spiking covariability is essential for understanding the collective activity of neural populations. Recent experiments have provided evidence of statistical dependencies among groups of neurons beyond that expected by the firing rates and pairwise alone [1-3]. These correlations (HOCs) can be generated by common input [4] or motifs within the network architecture [5], yet a complete mechanistic understanding is lacking. We explore a novel mechanism through which higher-order can be modulated: dendritic nonlinearities. We simulated the spiking activity of a simple exponential integrate-and-fire model neuron in response to two correlated presynaptic spike trains and background noise. The synaptic conductances were either summed linearly at the soma or were filtered through a nonlinear dendritic transfer function (see Figure ​Figure1),1), which was chosen to have a similar magnitude of effect on nonlinear EPSP summation as observed by active dendritic properties in pyramidal cells [6]. Using maximum entropy techniques, correlations in the circuit were quantified as the probability of synchronous triplet spiking between the postsynaptic cell and its two presynaptic inputs beyond what could be captured by the firing rates and pairwise alone, normalized by the product of the standard deviations. We found that superlinear dendrites significantly increased the level of triplet. On the other hand, saturating dendrites decorrelated triplets. These results reveal that HOCs in spiking activity are modulated by the curvature of the dendritic transfer function. Finally, this study demonstrates how intrinsic single-cell properties can tune spiking covariability in neural populations. Figure 1 A. Transfer functions depicting the summation of synaptic inputs for linear dendrites. B. Transfer function for saturating dendritic nonlinearities (solid line), as well as the identity (dotted line). C. Same as B for superlinear dendrites. D-F. Triplet ...

The role of horizontal connections for the modulation of border-ownership selective neurons in visual cortex

Border-ownership selectivity (BOS) codes for the direction of a foreground object relative to the background [1]. Attention modulates the responses of BOS neurons in cortical areas V2, both for the firing rate [2] and in terms of spike synchrony which can occur over long cortical distances [3]. Here, we develop a network model of spiking neurons based on dis-inhibitory feedback [4]. Specifically, we consider the potential influence of intra-areal (horizontal) connections for attention-induced modulation of BOS neurons in visual cortex. In our model (Figure ​(Figure1),1), horizontal connections (oblique lines) connect excitatory BOS (EBO) neurons representing one part of a figure to feed-forward inhibitory (FFI) neurons representing another figure part. Since horizontal connections are weakly myelinated or non-myelinated, signal propagation along them is slow and interactions through them are subject to substantial delays, roughly proportional to the distance between the neurons they connect. We therefore investigated the influence of communication delays on spike-spike synchrony between BOS neurons. Our simulation results of the network with short delays are in agreement with experimental data, showing attentional enhancement of firing rates of EBO neurons and, at the same time, reduction of spike-spike synchrony. In contrast, simulations with more realistic (longer) delays could not reproduce experimental results. Our results suggest that horizontal connections in early cortical areas cannot explain the observed synchrony structure between BOS neurons and are unlikely to form the substrate of figure-ground segregation. Figure 1 Circuit of the dis-inhibition model. The network was organized into 2 identical microcircuit units and consisted of 4 types of neurons [4]: excitatory BOS neurons (triangles, EBO), excitatory grouping neurons (hexagon, G), feedforward inhibitory neurons ...

Sharp Wave Ripples during Visual Exploration in the Primate Hippocampus.

Sharp-wave ripples (SWRs) are high-frequency oscillations that generate highly synchronized activity in neural populations. Their prevalence in sleep and quiet wakefulness, and the memory deficits that result from their interruption, suggest that SWRs contribute to memory consolidation during rest. Here, we report that SWRs from the monkey hippocampus occur not only during behavioral inactivity but also during successful visual exploration. SWRs were associated with attentive, focal search and appeared to enhance perception of locations viewed around the time of their occurrence. SWRs occurring in rest are noteworthy for their relation to heightened neural population activity, temporally precise and widespread synchronization, and memory consolidation; therefore, the SWRs reported here may have a similar effect on neural populations, even as experiences unfold.

Trajectory events across hippocampal place cells require previous experience.

SummaryReplay of hippocampal place cell sequences has been proposed as a fundamental mechanism of learning and memory. However, the standard interpretation of replay has been challenged by reports that similar activity is observed before experience (“preplay”). By this account, pre-existing temporal sequences are mapped onto new experiences, without learning sequential structure. Here we employed high density recording methods to monitor hundreds of place cells simultaneously while rats explored multiple novel environments. While we observed large numbers of synchronous spiking events prior to experience, they were not temporally correlated with subsequent experience. Multiple measures differentiated pre-experience and post-experience events, that taken together defined the latter but not the former as trajectory-depicting. The formation of events with these properties was prevented by administration of a NMDA-receptor antagonist during experience. These results suggest that the sequential structure of behavioral episodes is encoded during experience and re-expressed as trajectory events.

Establishing a Statistical Link between Network Oscillations and Neural Synchrony.

Pairs of active neurons frequently fire action potentials or “spikes” nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons’ fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron’s firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony.

Effects of channel noise on synchronization transitions in delayed scale-free network of stochastic Hodgkin–Huxley neurons**Project supported by the Natural Science Foundation of Shandong Province of China (Grant No. ZR2012AM013).

We numerically study the effect of the channel noise on the spiking synchronization of a scale-free Hodgkin–Huxley neuron network with time delays. It is found that the time delay can induce synchronization transitions at an intermediate and proper channel noise intensity, and the synchronization transitions become strongest when the channel noise intensity is optimal. The neurons can also exhibit synchronization transitions as the channel noise intensity is varied, and this phenomenon is enhanced at around the time delays that can induce the synchronization transitions. It is also found that the synchronization transitions induced by the channel noise are dependent on the coupling strength and the network average degree, and there is an optimal coupling strength or network average degree with which the synchronization transitions become strongest. These results show that by inducing synchronization transitions, the channel noise has a big regulation effect on the synchronization of the neuronal network. These findings could find potential implications for the information transmission in neural systems.

Neural Encoding of Odors during Active Sampling and in Turbulent Plumes

Sensory inputs are often fluctuating and intermittent, yet animals reliably utilize them to direct behavior. Here we ask how natural stimulus fluctuations influence the dynamic neural encoding of odors. Using the locust olfactory system, we isolated two main causes of odor intermittency: chaotic odor plumes and active sampling behaviors. Despite their irregularity, chaotic odor plumes still drove dynamic neural response features including the synchronization, temporal patterning, and short-term plasticity of spiking in projection neurons, enabling classifier-based stimulus identification and activating downstream decoders (Kenyon cells). Locusts can also impose odor intermittency through active sampling movements with their unrestrained antennae. Odors triggered immediate, spatially targeted antennal scanning that, paradoxically, weakened individual neural responses. However, these frequent but weaker responses were highly informative about stimulus location. Thus, not only are odor-elicited dynamic neural responses compatible with natural stimulus fluctuations and important for stimulus identification, but locusts actively increase intermittency, possibly to improve stimulus localization.

Functional differentiation of cholinergic and noradrenergic modulation in a biophysical model of olfactory bulb granule cells.

Olfactory bulb granule cells are modulated by both acetylcholine (ACh) and norepinephrine (NE), but the effects of these neuromodulators have not been clearly distinguished. We used detailed biophysical simulations of granule cells, both alone and embedded in a microcircuit with mitral cells, to measure and distinguish the effects of ACh and NE on cellular and microcircuit function. Cholinergic and noradrenergic modulatory effects on granule cells were based on data obtained from slice experiments; specifically, ACh reduced the conductance densities of the potassium M current and the calcium-dependent potassium current, whereas NE nonmonotonically regulated the conductance density of an ohmic potassium current. We report that the effects of ACh and NE on granule cell physiology are distinct and functionally complementary to one another. ACh strongly regulates granule cell firing rates and afterpotentials, whereas NE bidirectionally regulates subthreshold membrane potentials. When combined, NE can regulate the ACh-induced expression of afterdepolarizing potentials and persistent firing. In a microcircuit simulation developed to investigate the effects of granule cell neuromodulation on mitral cell firing properties, ACh increased spike synchronization among mitral cells, whereas NE modulated the signal-to-noise ratio. Coapplication of ACh and NE both functionally improved the signal-to-noise ratio and enhanced spike synchronization among mitral cells. In summary, our computational results support distinct and complementary roles for ACh and NE in modulating olfactory bulb circuitry and suggest that NE may play a role in the regulation of cholinergic function.

Spike sorting of synchronous spikes from local neuron ensembles.

Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved with a recently developed filter-based template matching procedure. Using tetrodes with a three-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of nonoverlapping spikes and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates, and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons.