Regular Spiking Neurons Research Articles

Short-Term Plasticity of Unitary Inhibitory-to-Inhibitory Synapses Depends on the Presynaptic Interneuron Subtype

Excitatory-to-inhibitory cortical synapses exhibit either short-term facilitation or depression, depending on the subtype identity of the postsynaptic interneuron, while the short-term plasticity (STP) of inhibitory-to-excitatory synapses depends on the presynaptic interneuron. However, the rules governing STP of inhibitory-to-inhibitory synapses have not yet been determined. We recorded 109 unitary connections made by the two major inhibitory interneuron subtypes in layer 4 of mouse somatosensory cortex, fast-spiking (FS) and somatostatin-containing (SOM) interneurons, on each other and on excitatory, regular-spiking (RS) neurons. In all pairs, we measured dynamic changes in the postsynaptic response to a 20 Hz train of presynaptic action potentials. In half of our dataset, we also measured kinetic properties of the unitary IPSC: latency, rise time, and decay time constant. We found a pronounced dependency of STP on the presynaptic, but not the postsynaptic, identity: FS interneurons made strongly depressing connections on FS, SOM, and RS targets, while in synapses made by SOM interneurons on FS and RS targets, weak early depression was followed by weak late facilitation. IPSC latency and rise time were also strongly dependent on the presynaptic interneuron subtype, being 1.5-2× slower in output synapses of SOM compared with FS interneurons. In contrast, the IPSC decay time constant depended only on the postsynaptic class, with 1.5× slower decay on excitatory compared with inhibitory targets. The properties of the inhibitory outputs of FS and SOM interneurons reciprocate the properties of their excitatory inputs and imply a dynamic spatiotemporal division of labor between these two major inhibitory subsystems.

Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep

Intracranial recording is an important diagnostic method routinely used in a number of neurological monitoring scenarios. In recent years, advancements in such recordings have been extended to include unit activity of an ensemble of neurons. However, a detailed functional characterization of excitatory and inhibitory cells has not been attempted in human neocortex, particularly during the sleep state. Here, we report that such feature discrimination is possible from high-density recordings in the neocortex by using 2D multielectrode arrays. Successful separation of regular-spiking neurons (or bursting cells) from fast-spiking cells resulted in well-defined clusters that each showed unique intrinsic firing properties. The high density of the array, which allowed recording from a large number of cells (up to 90), helped us to identify apparent monosynaptic connections, confirming the excitatory and inhibitory nature of regular-spiking and fast-spiking cells, thus categorized as putative pyramidal cells and interneurons, respectively. Finally, we investigated the dynamics of correlations within each class. A marked exponential decay with distance was observed in the case of excitatory but not for inhibitory cells. Although the amplitude of that decline depended on the timescale at which the correlations were computed, the spatial constant did not. Furthermore, this spatial constant is compatible with the typical size of human columnar organization. These findings provide a detailed characterization of neuronal activity, functional connectivity at the microcircuit level, and the interplay of excitation and inhibition in the human neocortex.

Propofol and Etomidate Depress Cortical, Thalamic, and Reticular Formation Neurons During Anesthetic-Induced Unconsciousness

The sites where anesthetics produce unconsciousness are not well understood. Likely sites include the cerebral cortex, thalamus, and reticular formation. We examined the effects of propofol and etomidate on neuronal function in the cortex, thalamus, and reticular formation in intact animals. Five cats had a recording well and electroencephalogram screws placed under anesthesia. After a 5-day recovery period, the cats were repeatedly studied 3 to 4 times per week. Neuronal (single-unit) activity in the cerebral cortex (areas 7, 18 and 19), thalamus (ventral posterolateral and ventral posteromedial nuclei and medial geniculate body), and reticular formation (mesencephalic reticular nucleus and central tegmental field) was recorded before, during, and after infusion of either propofol or etomidate. Cortical neuronal action potentials were analyzed separately as either regular spiking neurons or fast spiking neurons. Propofol and etomidate decreased the spontaneous firing rate of cortical neurons by 37% to 41%; fast spiking neurons and regular spiking neurons were similarly affected by the anesthetics. The neuronal firing rate in the thalamus and reticular formation decreased 30% to 49% by propofol and etomidate. The electroencephalogram shifted from a low-amplitude, high-frequency pattern to a high-amplitude, low-frequency pattern during drug infusion suggesting an anesthetic effect; peak power occurred at 12 to 13 Hz during propofol infusion. There were 2 major peaks during etomidate anesthesia: one at 12 to 14 Hz and another at 7 to 8 Hz. The cats were heavily sedated, with depressed corneal and whisker reflexes; withdrawal to noxious stimulation remained intact. These data show that neurons in the cortex, thalamus, and reticular formation are similarly depressed by propofol and etomidate. Although anesthetic depression of neuronal activity likely contributes to anesthetic-induced unconsciousness, further work is needed to determine how anesthetic effects at these sites interact to produce unconsciousness.

Loss of Visually Driven Synaptic Responses in Layer 4 Regular-Spiking Neurons of Rat Visual Cortex in Absence of Competing Inputs

Monocular deprivation (MD) during development shifts the ocular preference of primary visual cortex (V1) neurons by depressing closed-eye responses and potentiating open-eye responses. As these 2 processes are temporally and mechanistically distinct, we tested whether loss of responsiveness occurs also in absence of competing inputs. We thus compared the effects of long-term MD in layer 4 regular-spiking pyramidal neurons (L4Ns) of binocular and monocular V1 (bV1 and mV1) with whole-cell recordings. In bV1, input depression was larger than potentiation, and the ocular dominance shift was larger for spike outputs. MD-but not retinal inactivation with tetrodotoxin-caused a comparable loss of synaptic and spike responsiveness in mV1, which is innervated only by the deprived eye. Conversely, brief MD depressed synaptic responses only in bV1. MD-driven depression in mV1 was accompanied by a proportional reduction of visual thalamic inputs, as assessed upon pharmacological silencing of intracortical transmission. Finally, sub- and suprathreshold responsiveness was similarly degraded in L4Ns of bV1 upon complete deprivation of patterned vision through a binocular deprivation period of comparable length. Thus, loss of synaptic inputs from the deprived eye occurs also in absence of competition in the main thalamorecipient lamina, albeit at a slower pace.

LTS and FS Inhibitory Interneurons, Short-Term Synaptic Plasticity, and Cortical Circuit Dynamics

Somatostatin-expressing, low threshold-spiking (LTS) cells and fast-spiking (FS) cells are two common subtypes of inhibitory neocortical interneuron. Excitatory synapses from regular-spiking (RS) pyramidal neurons to LTS cells strongly facilitate when activated repetitively, whereas RS-to-FS synapses depress. This suggests that LTS neurons may be especially relevant at high rate regimes and protect cortical circuits against over-excitation and seizures. However, the inhibitory synapses from LTS cells usually depress, which may reduce their effectiveness at high rates. We ask: by which mechanisms and at what firing rates do LTS neurons control the activity of cortical circuits responding to thalamic input, and how is control by LTS neurons different from that of FS neurons? We study rate models of circuits that include RS cells and LTS and FS inhibitory cells with short-term synaptic plasticity. LTS neurons shift the RS firing-rate vs. current curve to the right at high rates and reduce its slope at low rates; the LTS effect is delayed and prolonged. FS neurons always shift the curve to the right and affect RS firing transiently. In an RS-LTS-FS network, FS neurons reach a quiescent state if they receive weak input, LTS neurons are quiescent if RS neurons receive weak input, and both FS and RS populations are active if they both receive large inputs. In general, FS neurons tend to follow the spiking of RS neurons much more closely than LTS neurons. A novel type of facilitation-induced slow oscillations is observed above the LTS firing threshold with a frequency determined by the time scale of recovery from facilitation. To conclude, contrary to earlier proposals, LTS neurons affect the transient and steady state responses of cortical circuits over a range of firing rates, not only during the high rate regime; LTS neurons protect against over-activation about as well as FS neurons.

Integration and transmission of distributed deterministic neural activity in feed-forward networks

A ten layer feed-forward network characterized by diverging/converging patterns of projection between successive layers of regular spiking (RS) neurons is activated by an external spatiotemporal input pattern fed to Layer 1 in presence of stochastic background activities fed to all layers. We used three dynamical systems to derive the external input spike trains including the temporal information, and three types of neuron models for the network, i.e. either a network formed either by neurons modeled by exponential integrate-and-fire dynamics (RS-EIF, Fourcaud-Trocmé et al., 2003), or by simple spiking neurons (RS-IZH, Izhikevich, 2004) or by multiple-timescale adaptive threshold neurons (RS-MAT, Kobayashi et al., 2009), given five intensities for the background activity. The assessment of the temporal structure embedded in the output spike trains was carried out by detecting the preferred firing sequences for the reconstruction of de-noised spike trains (Asai and Villa, 2008). We confirmed that the RS-MAT model is likely to be more efficient in integrating and transmitting the temporal structure embedded in the external input. We observed that this structure could be propagated not only up to the 10th layer but in some cases it was retained better beyond the 4th downstream layers. This study suggests that diverging/converging network structures, by the propagation of synfire activity, could play a key role in the transmission of complex temporal patterns of discharges associated to deterministic nonlinear activity.This article is part of a Special Issue entitled Neural Coding.

Comparison of different neuron models to conductance-based post-stimulus time histograms obtained in cortical pyramidal cells using dynamic-clamp in vitro

A wide diversity of models have been proposed to account for the spiking response of central neurons, from the integrate-and-fire (IF) model and its quadratic and exponential variants, to multiple-variable models such as the Izhikevich (IZ) model and the well-known Hodgkin-Huxley (HH) type models. Such models can capture different aspects of the spiking response of neurons, but there is few objective comparison of their performance. In this article, we provide such a comparison in the context of well-defined stimulation protocols, including, for each cell, DC stimulation, and a series of excitatory conductance injections, arising in the presence of synaptic background activity. We use the dynamic-clamp technique to characterize the response of regular-spiking neurons from guinea-pig visual cortex by computing families of post-stimulus time histograms (PSTH), for different stimulus intensities, and for two different background activities (low- and high-conductance states). The data obtained are then used to fit different classes of models such as the IF, IZ, or HH types, which are constrained by the whole data set. This analysis shows that HH models are generally more accurate to fit the series of experimental PSTH, but their performance is almost equaled by much simpler models, such as the exponential or pulse-based IF models. Similar conclusions were also reached by performing partial fitting of the data, and examining the ability of different models to predict responses that were not used for the fitting. Although such results must be qualified by using more sophisticated stimulation protocols, they suggest that nonlinear IF models can capture surprisingly well the response of cortical regular-spiking neurons and appear as useful candidates for network simulations with conductance-based synaptic interactions.

Cross-modular processing in a spiking neural network model

According to the classical view about cortical organization, primary sensory areas are exclusively dedicated to a single sensory modality. Recent studies have challenged this view by showing that primary sensory cortices may undertake cross-modal processing whenever necessary [1,2]. Despite the growing interest in cross-modal cortical processing, the neural basis of this phenomenon remains largely unknown. How to understand cross-modal cortical processing given the anatomical structure of the cortex and the dynamic features of cortical neurons? It is known that cortical architecture is modular, so that neurons within a module are more densely connected among themselves than with neurons in other modules [3], and that the specificities of cortical connections depend on their range, with excitatory connections reaching longer distances than inhibitory connections [4]. It is also known that cortical neurons belong to different electrophysiological classes [5]. In this work we used a modular network of spiking neurons to study cross-modular processing in terms of average spike rate and latency. The network was made of 1,000 neurons modeled according to the Izhikevich formalism [6] to simulate three electrophysiological classes: fast spiking (FS), regular spiking (RS) and bursting (BS) neurons. Synaptic connections were modeled by α-functions. To generate the modular network, we started with a random network with a 4:1 ratio of excitatory to inhibitory neurons and sparse connectivity. The network was randomly divided into four modules of equal size. Each existing inter-modular inhibitory connection was deleted and corresponding intra-modular inhibitory connections were randomly created in turn. A similar rewiring was made for the inter-modular excitatory connections but with probability p of deleting an excitatory inter-modular connection. Furthermore, two modules (let us call them modules 1 and 2) were randomly selected and all connections between them were deleted. Three experimental protocols were performed: (a) the entire network was stimulated by random noise for 1200 msec; (b) similar to (a) but module 1 also received a fixed input pattern from t = 200 msec to t = 250 msec; and (c) the only input given to the network was the fixed pattern to module 1 only for the same time interval as in (b). The raster plots of the four modules for protocol (a) show a similar frequency response for all of them but the spiking activity of module 2 cells is out of phase in comparison with activities in other modules. In the case of protocol (b), the presentation of the fixed input pattern to module 1 caused a slight and sustained increase in the average firing frequency of all modules. The start of this increase is delayed by 15 msec for modules 3 and 4, which are directly connected with module 1, and by 30 msec for module 2, which is only indirectly connected with module 1. These delays are not only due to synaptic delay (equal to 1 msec) but may also be due to intrinsic network properties. For protocol (c) the situation was similar to protocol (b) but with a lower average spike rate which decays toward zero after offset of the external stimulus. The simple network studied allow propagation of information between modules, with modules that do not receive direct external input responding stably (with delays) to an external and fixed input pattern presented to a single module. This suggests that more detailed analyses of modular networks may help understanding of cross-modal processing.

Rhythmic intrinsic bursting neurons in human neocortex obtained from pediatric patients with epilepsy

Neocortical oscillations result from synchronized activity of a synaptically coupled network and can be strongly influenced by the intrinsic firing properties of individual neurons. As such, the intrinsic electroresponsive properties of individual neurons may have important implications for overall network function. Rhythmic intrinsic bursting (rIB) neurons are of particular interest, as they are poised to initiate and/or strongly influence network oscillations. Although neocortical rIB neurons have been recognized in multiple species, the current study is the first to identify and characterize rIB neurons in the human neocortex. Using whole-cell current-clamp recordings, rIB neurons (n = 12) are identified in human neocortical tissue resected from pediatric patients with intractable epilepsy. In contrast to human regular spiking neurons (n = 12), human rIB neurons exhibit rhythmic bursts of action potentials at frequencies of 0.1-4 Hz. These bursts persist after blockade of fast excitatory neurotransmission and voltage-gated calcium channels. However, bursting is eliminated by subsequent application of the persistent sodium current (I(NaP)) blocker, riluzole. In the presence of riluzole (either 10 or 20 μm), human rIB neurons no longer burst, but fire tonically like regular spiking neurons. These data demonstrate that I(NaP) plays a critical role in intrinsic oscillatory activity observed in rIB neurons in the human neocortex. It is hypothesized that aberrant changes in I(NaP) expression and/or function may ultimately contribute to neurological diseases that are linked to abnormal network activity, such as epilepsy.

Somatosensory response properties of excitatory and inhibitory neurons in rat motor cortex

In sensory cortical networks, peripheral inputs differentially activate excitatory and inhibitory neurons. Inhibitory neurons typically have larger responses and broader receptive field tuning compared with excitatory neurons. These differences are thought to underlie the powerful feedforward inhibition that occurs in response to sensory input. In the motor cortex, as in the somatosensory cortex, cutaneous and proprioceptive somatosensory inputs, generated before and during movement, strongly and dynamically modulate the activity of motor neurons involved in a movement and ultimately shape cortical command. Human studies suggest that somatosensory inputs modulate motor cortical activity in a center excitation, surround inhibition manner such that input from the activated muscle excites motor cortical neurons that project to it, whereas somatosensory input from nearby, nonactivated muscles inhibit these neurons. A key prediction of this hypothesis is that inhibitory and excitatory motor cortical neurons respond differently to somatosensory inputs. We tested this prediction with the use of multisite extracellular recordings in anesthetized rats. We found that fast-spiking (presumably inhibitory) neurons respond to tactile and proprioceptive inputs at shorter latencies and larger response magnitudes compared with regular-spiking (presumably excitatory) neurons. In contrast, we found no differences in the receptive field size of these neuronal populations. Strikingly, all fast-spiking neuron pairs analyzed with cross-correlation analysis displayed common excitation, which was significantly more prevalent than common excitation for regular-spiking neuron pairs. These findings suggest that somatosensory inputs preferentially evoke feedforward inhibition in the motor cortex. We suggest that this provides a mechanism for dynamic selection of motor cortical modules during voluntary movements.

Aging redistributes medial prefrontal neuronal excitability and impedes extinction of trace fear conditioning

Cognitive flexibility is critical for survival and reflects the malleability of the central nervous system (CNS) in response to changing environmental demands. Normal aging results in difficulties modifying established behaviors, which may involve medial prefrontal cortex (mPFC) dysfunction. Using extinction of conditioned fear in rats to assay cognitive flexibility, we demonstrate that extinction deficits reminiscent of mPFC dysfunction first appear during middle age, in the absence of hippocampus-dependent context deficits. Emergence of aging-related extinction deficits paralleled a redistribution of neuronal excitability across two critical mPFC regions via two distinct mechanisms. First, excitability decreased in regular spiking neurons of infralimbic-mPFC (IL), a region whose activity is required for extinction. Second, excitability increased in burst spiking neurons of prelimbic-mPFC (PL), a region whose activity hinders extinction. Experiments using synaptic blockers revealed that these aging-related differences were intrinsic. Thus, changes in IL and PL intrinsic excitability may contribute to cognitive flexibility impairments observed during normal aging.

Target‐specific output patterns are predicted by the distribution of regular‐spiking and bursting pyramidal neurons in the subiculum

Pyramidal neurons in the subiculum project to a variety of cortical and subcortical areas in the brain to convey information processed in the hippocampus. Previous studies have shown that two groups of subicular pyramidal neurons--regular-spiking and bursting neurons--are distributed in an organized fashion along the proximal-distal axis, with more regular-spiking neurons close to CA1 (proximal) and more bursting neurons close to presubiculum (distal). Anatomically, neurons projecting to some targets are located more proximally along this axis, while others are located more distally. However, the relationship between the firing properties and the targets of subicular pyramidal neurons is not known. To study this relationship, we used in vivo injections of retrogradely transported fluorescent beads into each of nine different regions and conducted whole-cell current-clamp recordings from the bead-containing subicular neurons in acute brain slices. We found that subicular projections to each area were composed of a mixture of regular-spiking and bursting neurons. Neurons projecting to amygdala, lateral entorhinal cortex, nucleus accumbens, and medial/ventral orbitofrontal cortex were located primarily in the proximal subiculum and consisted mostly of regular-spiking neurons (∼80%). By contrast, neurons projecting to medial EC, presubiculum, retrosplenial cortex, and ventromedial hypothalamus were located primarily in the distal subiculum and consisted mostly of bursting neurons (∼80%). Neurons projecting to a thalamic nucleus were located in the middle portion of subiculum, and their probability of bursting was close to 50%. Thus, the fraction of bursting neurons projecting to each target region was consistent with the known distribution of regular-spiking and bursting neurons along the proximal-distal axis of the subiculum. Variation in the distribution of regular-spiking and bursting neurons suggests that different types of information are conveyed from the subiculum to its various targets.

Sex-specificity of associative learning-induced changes in GABAergic tonic inhibition in layer 4 neurons of mouse barrel cortex

We show that in naïve mice, tonic currents mediated by δ subunit-containing GABA A receptors in fast spiking interneurons are larger in females than in males while in regular spiking neurons such a difference was not observed. Moreover, in fast spiking interneurons, associative learning induced a larger reduction of these currents in females than in males. In contrast, in regular spiking neurons, learning similarly enhanced tonic currents in both sexes.

Tunable neuromimetic integrated system for emulating cortical neuron models

Nowadays, many software solutions are currently available for simulating neuron models. Less conventional than software-based systems, hardware-based solutions generally combine digital and analog forms of computation. In previous work, we designed several neuromimetic chips, including the Galway chip that we used for this paper. These silicon neurons are based on the Hodgkin–Huxley formalism and they are optimized for reproducing a large variety of neuron behaviors thanks to tunable parameters. Due to process variation and device mismatch in analog chips, we use a full-custom fitting method in voltage-clamp mode to tune our neuromimetic integrated circuits. By comparing them with experimental electrophysiological data of these cells, we show that the circuits can reproduce the main firing features of cortical cell types. In this paper, we present the experimental measurements of our system which mimic the four most prominent biological cells: fast spiking, regular spiking, intrinsically bursting, and low-threshold spiking neurons into analog neuromimetic integrated circuit dedicated to cortical neuron simulations. This hardware and software platform will allow to improve the hybrid technique, also called “dynamic-clamp,” that consists of connecting artificial and biological neurons to study the function of neuronal circuits.

Sensory Learning Differentially Affects GABAergic Tonic Currents in Excitatory Neurons and Fast Spiking Interneurons in Layer 4 of Mouse Barrel Cortex

Pairing tactile stimulation of whiskers with a tail shock is known to result in expansion of cortical representation of stimulated vibrissae and in the increase in synaptic GABAergic transmission. However, the impact of such sensory learning in classical conditioning paradigm on GABAergic tonic currents has not been addressed. To this end, we performed whole cell patch-clamp slice recordings of tonic currents from neurons (excitatory regular spiking, regular spiking nonpyramidal, and fast spiking interneurons) of layer 4 of the barrel cortex from naive and trained mice. Interestingly, endogenous tonic GABAergic currents measured from the excitatory neurons in the cortical representation of "trained" vibrissae were larger than in the "naïve" or pseudoconditioned ones. On the contrary, sensory learning markedly reduced tonic currents in the fast spiking interneurons but not in regular spiking nonpyramidal neurons. Changes of tonic currents were accompanied by changes in the input resistances-decrease in regular spiking and increase in fast spiking neurons, respectively. Applications of nipecotic acid, a GABA uptake blocker, enhanced the tonic currents, but the impact of the sensory learning remained qualitatively the same as in the case of the tonic currents. Similar to endogenous tonic currents, sensory learning enhanced currents induced by THIP (superagonist for delta subunit-containing GABA(A) receptors) in regular spiking neurons, whereas the opposite was observed for the fast spiking interneurons. In conclusion, our data show that the sensory learning strongly affects the GABAergic tonic currents in a cell-specific manner and suggest that the underlying mechanism involves regulation of expression of delta subunit-containing GABA(A) receptors.

Corrigendum

CorrigendaCorrigendumPublished Online:01 May 2010https://doi.org/10.1152/jn.z9k-0110-corr.2010Original articleMoreSectionsPDF (343 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations Volume 97, March 2007Muresan RC, Savin C. Resonance or Integration? Self-Sustained Dynamics and Excitability of Neural Microcircuits. J Neurophysiol 97: , 2007; doi:10.1152/jn.01043.2006; http://jn.physiology.org/cgi/content/full/97/3/1911.Due to use of an erroneous line of code when determining subthreshold frequency-response properties of neuron models, the test frequency was approximately half of the plotted frequency. This led to erroneous plotting of frequencies in Fig. 1, with the frequency axis depicting approximately double the real values. This mistake does not affect any other simulations or analyses and the results and conclusions of the paper remain entirely valid. A corrected version of Fig. 1 is presented below.Fig. 1.Frequency response characteristics of integrate-and-fire (IF), regular-spiking (RS), and resonator (RES) neurons to subthreshold input. Neurons are stimulated using a ZAP function (see text for details) and deviation from the rest of their membrane potentials is monitored. Frequency spectra of the membrane potential responses are divided by the frequency spectrum of the input current to yield the frequency-dependent impedances.Download figureDownload PowerPointCorrection of this error also leads to the following correction in the text. The last four sentences in the last paragraph in the section “Subthreshold behavior,” should read (boldface indicates altered text): “Remarkably different, the RS neuron's impedance has a very broadly tuned peak at ∼22 Hz, remaining rather unselective for all frequencies. Its impedance is low compared with that of the other neurons. The RES neuron is a clear resonator, acting as a band-pass filter (Hutcheon and Yarom 2000). It is well tuned to a central frequency of ∼52 Hz with a relatively wide, asymmetric peak, spanning frequencies from ∼10 to ∼50 Hz (see Fig. 1)”.This article has no references to display. Download PDF Previous Back to Top Next FiguresReferencesRelatedInformation Related ArticlesResonance or Integration? Self-Sustained Dynamics and Excitability of Neural Microcircuits 01 Mar 2007Journal of Neurophysiology More from this issue > Volume 103Issue 5May 2010Pages 2932-2932 Copyright & PermissionsCopyright © 2010 the American Physiological Societyhttps://doi.org/10.1152/jn.z9k-0110-corr.2010History Published online 1 May 2010 Published in print 1 May 2010 Metrics

Effects of training to perform a working memory Task on regular spiking and fast spiking neurons in the lateral prefrontal cortex

Effects of training to perform a working memory Task on regular spiking and fast spiking neurons in the lateral prefrontal cortex

Activity-dependent changes in the electrophysiological properties of regular spiking neurons in the sensorimotor cortex of the rat in vitro

Sensorimotor performance is highly dependent on the level of physical activity. For instance, a period of disuse induces an impairment of motor performance, which is the result of combined muscular, spinal and supraspinal mechanisms. Concerning this latter origin, our hypothesis was that intrinsic properties and input/output coupling of cells within the sensorimotor cortex might participate to the alteration in cortical motor control. The aim of the present study was thus to examine the basic electrophysiological characteristics of cortical cells in control rats and in animals submitted to 14 days of hindlimb unloading, a model of sensorimotor deprivation. Intracellular recordings were obtained in vitro from coronal slices from cortical hindpaw representation area. We have also made an attempt to determine the morphological characteristics as well as the location of the investigated neurons by biocytin labelling. Passive properties of neurons were affected by hindlimb unloading: input resistance and time constant were decreased (−20%), the rheobase was increased (+34%), whereas the resting potential was unchanged. The frequency–current relationships were also modified, the curve being shifted towards right. The size of body area of recorded neurons was unchanged in unloaded rats. Taken together, these data reflect a decrease in excitability of cortical cells in response to a decreased cortical activation.

Grouping and classifying electrophysiologically-defined classes of neocortical neurons by single cell, whole-genome expression profiling

The diversity of neuronal cell types and how to classify them are perennial questions in neuroscience. The advent of global gene expression analysis raised the possibility that comprehensive transcription profiling will resolve neuronal cell types into groups that reflect some or all aspects of their phenotype. This approach has been successfully used to compare gene expression between groups of neurons defined by a common property. Here we extend this approach to ask whether single neuron gene expression profiling can prospectively resolve neuronal subtypes into groups, independent of any phenotypic information, and whether those groups reflect meaningful biological properties of those neurons. We applied methods we have developed to compare gene expression among single neural stem cells to study global gene expression in 18 randomly picked neurons from layer II/III of the early postnatal mouse neocortex. Cells were selected by morphology and by firing characteristics and electrical properties, enabling the definition of each cell as either fast- or regular-spiking, corresponding to a class of inhibitory interneurons or excitatory pyramidal cells. Unsupervised clustering of young neurons by global gene expression resolved the cells into two groups and those broadly corresponded with the two groups of fast- and regular-spiking neurons. Clustering of the entire, diverse group of 18 neurons of different developmental stages also successfully grouped neurons in accordance with the electrophysiological phenotypes, but with more cells misassigned among groups. Genes specifically enriched in regular spiking neurons were identified from the young neuron expression dataset. These results provide a proof of principle that single-cell gene expression profiling may be used to group and classify neurons in a manner reflecting their known biological properties and may be used to identify cell-specific transcripts.

Increasing the Potassium Channel Density in Regularly Spiking Pyramidal Cells can Turn them into Fast Spiking

The threshold dynamic of neurons can be classified into two main types: regular spiking neurons, showing a continuous frequency-stimulation current relationship and thus an arbitrarily low frequency at threshold current, and fast spiking neurons, showing a discontinuous relationship and a minimum frequency for repetitive firing. In a previous investigation of a hippocampal neuron model, we showed that the membrane density of critical ion channels is important for the bifurcation type and consequently for the threshold dynamics; it can cause the model to switch between fast and regular spiking. In the present study we extend our previous analysis with experimental tests, using the dynamic clamp technique. We injected currents, calculated from voltage clamp descriptions of potassium currents, into regularly spiking pyramidal cells, thereby altering their threshold dynamics to fast spiking. The results confirm the conclusion from the previous study that the type of threshold dynamics of neurons can critically depend on the channel density. Moreover, we show by analysing other well-described membrane models with techniques from nonlinear dynamical system theory how the channel density as bifurcation parameter is influenced by other parameters such as channel kinetics, in some cases reducing the threshold dynamics exclusively to fast spiking. In conclusion, the overall structure of the phase space around the stationary potentials must be taken into account when trying to understand the threshold dynamics of neurons, and, consequently, the global oscillatory activity of networks of neurons.