Gotowa bibliografia na temat „Fast Spiking Interneurons (FSINs)”

Rodents have a topographic map in primary somatosensory cortex of the contralateral facial whiskers. A brief period of whisker trimming causes the representation of the nontrimmed whiskers (spared) to expand into the cortex that has lost its principal sensory input (deprived). It has been hypothesized that this is mediated by a period of persistent disinhibition in deprived cortex that enables the expansion of spared whisker representations. Alternatively, it has been proposed that inhibition undergoes a biphasic change with an initial, brief period of disinhibition to promote plasticity in excitatory circuits followed by a more prolonged increase in inhibition to re-establish the excitatory – inhibitory balance. These hypotheses make different predictions about how inhibition changes during cortical map plasticity, which I have tested in this thesis. I focused on fast-spiking (FS) interneurons, which are thought to play an important role in adult cortical plasticity. I made electrophysiological recordings in layer 2/3 to determine how inhibitory circuitry in deprived cortex is affected by whisker deprivation. The amplitude of miniature excitatory postsynaptic potentials (mEPSPs) in deprived FS interneurons was increased with no change in mEPSP frequency suggesting that the global excitatory drive onto FS interneurons was potentiated. In contrast, the amplitude of miniature inhibitory postsynaptic currents (mIPSCs) in layer 2/3 pyramidal neurons was unchanged, but there was a small but significant increase in mIPSC frequency. I investigated feedback inhibitory circuits in more detail by recording from pairs of pyramidal cells and FS interneurons that were synaptically-connected. Surprisingly, I found that the strength of local excitation onto FS interneurons and the strength of FS – mediated inhibition on deprived pyramidal neurons were unchanged. I concluded that, contrary to two popular hypotheses, a brief period of sensory deprivation did not alter the feedback inhibition in layer 2/3 of deprived cortex.

Fast-spiking interneurons are the largest interneuronal population in neocortex. It is well documented that this population is crucial in many functions of the neocortex by subserving all aspects of neural computation, like gain control, and by enabling dynamic phenomena, like the generation of high frequency oscillations. Fast-spiking interneurons, which represent mainly the parvalbumin-expressing, soma-targeting basket cells, are also implicated in pathological dynamics, like the propagation of seizures or the impaired coordination of activity in schizophrenia. In the present thesis, I investigate the role of fast-spiking interneurons in such dynamic phenomena by using computational and experimental techniques. First, I introduce a neural mass model of the neocortical microcircuit featuring divisive inhibition, a gain control mechanism, which is thought to be delivered mainly by the soma-targeting interneurons. Its dynamics were analysed at the onset of chaos and during the phenomena of entrainment and long-range synchronization. It is demonstrated that the mechanism of divisive inhibition reduces the sensitivity of the network to parameter changes and enhances the stability and exibility of oscillations. Next, in vitro electrophysiology was used to investigate the propagation of activity in the network of electrically coupled fast-spiking interneurons. Experimental evidence suggests that these interneurons and their gap junctions are involved in the propagation of seizures. Using multi-electrode array recordings and optogenetics, I investigated the possibility of such propagating activity under the conditions of raised extracellular K+ concentration which applies during seizures. Propagated activity was recorded and the involvement of gap junctions was con rmed by pharmacological manipulations. Finally, the interaction between two oscillations was investigated. Two oscillations with di erent frequencies were induced in cortical slices by directly activating the pyramidal cells using optogenetics. Their interaction suggested the possibility of a coincidence detection mechanism at the circuit level. Pharmacological manipulations were used to explore the role of the inhibitory interneurons during this phenomenon. The results, however, showed that the observed phenomenon was not a result of synaptic activity. Nevertheless, the experiments provided some insights about the excitability of the tissue through scattered light while using optogenetics. This investigation provides new insights into the role of fast-spiking interneurons in the neocortex. In particular, it is suggested that the gain control mechanism is important for the physiological oscillatory dynamics of the network and that the gap junctions between these interneurons can potentially contribute to the inhibitory restraint during a seizure.

Preparatory neural activity in premotor areas is critical for planning and execution of voluntary movements. Previous studies in monkeys and mice have revealed how the discharges of pyramidal, excitatory neurons (PNs) encode a motor plan for an upcoming movement (Afshar et al., 2011; Chen et al., 2017; Li et al., 2015). However, the contribution of GABAergic interneurons, specifically fast-spiking interneurons (FSNs), to voluntary movements remains poorly understood. Putative premotor areas involved in action planning have been demonstrated in rodents. In particular, in mice, a premotor area controlling voluntary licking has been identified in the anterior-lateral motor cortex (ALM) (Komiyama et al., 2010). Also, ALM partially overlaps with the rostral forelimb area (RFA), the previously defined premotor region involved in the control of paw movement in rats and mice (Rouiller et al., 1993; Tennant et al., 2011). To understand the excitatory-inhibitory microcircuit involved in action planning, here I compare directly the response properties of PNs and FSNs during licking behaviour and forelimb retraction in the mouse. Recordings are carried out with both acute electrodes and chronic microelectrode arrays from both the two premotor areas, i.e. the ALM – responsible for licking –, and RFA – involved in paw movement. Specifically, in a first set of experiments, I used head-restrained mice that spontaneously lick a reward delivered at random intervals from a drinking spout. Mice voluntary performed either single isolated or a burst of consecutive licks, which I categorized, a posteriori, in single (= 1 lick) and multiple licks (≥ 3 licks). During the task, I extracellularly recorded single units’ activity from ALM, using acute in vivo electrophysiology. I identified putative PNs and FSNs, based on well-established features of their waveforms, and investigated their functional properties during the movement. Unexpectedly, I report that optogenetically-verified FSNs showed an earlier and more sustained activation than PNs. In particular, most of the neurons’ activity anticipated the licking onset, consistently with an involvement of the ALM in movement planning. The majority of the neurons (~90%) increased their firing frequency in correspondence with the movement, but suppressive modulations were also observed in a subset of units. For both PNs and FSNs, I found significantly greater discharge during multiple than single licks and the peak discharge was significantly delayed for both subclasses during multiple licking events. However, FSNs modulated their activity about 100ms earlier than PNs. Furthermore, almost all FSNs showed a peak in their response before the beginning of the sequence of licks. Analysis of mean information content confirms that FSNs predict licking onset not only significantly better, but even earlier, than PNs. Chronic electrode arrays covering both the ALM and RFA were next used to simultaneously probe neural responses during (i) licking and (ii) forelimb pulling in a robotic device (Spalletti et al., 2017). I report that most of the FSNs respond with a stereotyped increase in their firing rates during both licking and pulling. In stark contrast, PNs show a variety of behaviours, dependent on movement type. At least for a minority of them, licking behaviour and forelimb retraction are represented as two different motor acts, reaching significant levels in the PNs. Accordingly, computational analysis shows that PNs carry more independent information than FSNs. Altogether, these data indicate that a global rise of GABAergic inhibition mediated by FSNs firing contributes to early action planning. Next, encouraged by the deeper understanding of the cortical microcircuits underlying movement planning in mice, I exploited this knowledge to explore more complex mechanisms, as action understanding. The neural circuits that integrate performed and observed actions have been found in the premotor cortex of monkeys and named as ‘mirror neurons system’ (di Pellegrino et al., 1992). Recently, the presence of mirror neurons have been demonstrated in rodents in the anterior cingulate cortex (Carrillo et al., 2019), but whether they could contribute to action understanding in the premotor cortex is still unclear. At behavioural level, the observation of actions can actually lead, in some cases, to the repetition of those same actions. This phenomenon has been named social facilitation, and the underlying motor program has been attributed to the mirror system (Ferrari et al., 2005). Here, I set up a behavioural task similar to the one exploited in monkeys to explore social facilitation in mice. I took advantage of licking behaviour to set up the social facilitation experiment. Therefore, head-restrained mice were allowed to lick water from a feeding needle. I found that mice can actually facilitated to lick more when another individual was engaged in the same action, supporting the hypothesis of a social facilitation in mouse. Altogether these results indicate that the observers’ behaviour was actually influenced by the demonstrators’ one, laying the groundwork for the study of mirror neurons in mice at cellular level.

Large parts of the cortex and the thalamus project into the striatum,which serves as the input stage of the basal ganglia. Information isintegrated in the striatal neural network and then passed on, via themedium spiny (MS) projection neurons, to the output stages of thebasal ganglia. In addition to the MS neurons there are also severaltypes of interneurons in the striatum, such as the fast spiking (FS)interneurons. I focused my research on the FS neurons, which formstrong inhibitory synapses onto the MS neurons. These striatal FSneurons are sparsely connected by electrical synapses (gap junctions),which are commonly presumed to synchronise their activity.Computational modelling with the GENESIS simulator was used toinvestigate the effect of gap junctions on a network of synapticallydriven striatal FS neurons. The simulations predicted a reduction infiring frequency dependent on the correlation between synaptic inputsto the neighbouring neurons, but only a slight synchronisation. Thegap junction effects on modelled FS neurons showing sub-thresholdoscillations and stuttering behaviour confirm these results andfurther indicate that hyperpolarising inputs might regulate the onsetof stuttering.The interactions between MS and FS neurons were investigated byincluding a computer model of the MS neuron. The hypothesis was thatdistal GABAergic input would lower the amplitude of back propagatingaction potentials, thereby reducing the calcium influx in thedendrites. The model verified this and further predicted that proximalGABAergic input controls spike timing, but not the amplitude ofdendritic calcium influx after initiation.Connecting models of neurons written in different simulators intonetworks raised technical problems which were resolved by integratingthe simulators within the MUSIC framework. This thesis discusses theissues encountered by using this implementation and gives instructionsfor modifying MOOSE scripts to use MUSIC and provides guidelines forachieving compatibility between MUSIC and other simulators.This work sheds light on the interactions between striatal FS and MSneurons. The quantitative results presented could be used to developa large scale striatal network model in the future, which would beapplicable to both the healthy and pathological striatum.<br>QC 20100720

Inhibitory GABAergic neurons, although forming a minor proportion of the neuronal population in the central nervous system, have been reported to be crucial for different physiological states of the brain. Among the vast diversity of this neuronal subpopulation, the fast spiking interneurons (FSINs) have been studied in great detail owing to their morphological and physiological attributes and functional correlates. Due to their perisomatic targeting and rapid spiking nature, they have been strongly associated with spike time and gain control of their target neurons in neuronal microcircuits across different regions of the brain. Plastic alterations of neuronal synaptic and intrinsic properties have been associated with learning and memory. However, a vast majority of the studies performed so far pertains to excitatory neurons. Although some recent studies have looked into plasticity of inhibition, little is known about plastic changes in the inhibitory neurons. Owing to the morpho-physiological properties of the FSINs and their massive connectivity, plastic alterations in them can cascade to their connected neuronal microcircuit. The dentate gyrus (DG) forms an important gateway of information for the hippocampus and has been associated with pattern separation. The granule cells which are predominantly known to target interneurons discharge in the gamma frequency range. Hilar interneurons including the FSINs are known to show membrane potential oscillations phase-locked with the extracellularly recorded oscillations. However, the consequent response of a FSIN to repetitive excitatory gamma synaptic bursts presented either in isolation or in association with membrane potential modulations has not received attention. We show that the FSINs of the DG sub field express a robust long lasting decrease in intrinsic excitability after experiencing bursts of synaptic stimulation of the mossy fiber pathway at gamma frequency (30 Hz), repeated at delta (2 Hz) or theta frequency (4 Hz). Interestingly, the GCs did not express any plasticity of intrinsic excitability upon experiencing similar gamma bursts repeated at delta frequency. The change in intrinsic excitability in the FSINs was observed to be strongly dependent on the somatic current supplement that altered the membrane potential in phase with the synaptic gamma bursts. The plasticity was found to be dependent on the post synaptic calcium flux through the calcium-permeable AMPA receptors (CP-AMPARs) and also on post synaptic HCN channel conductance. Further, decreased excitability in the FSINs exhibited decreased inhibition in the post-synaptic putative granule cells. Additionally, we have used network simulations to predict that the spiking rate of an excitatory neuron is strongly dependent on the intrinsic excitability of a perisomatic targeting interneuron; both integrated in a feedback microcircuit. Given the importance of FSINs in network synchronization, understanding how intrinsic excitability and its plasticity in the FSINs can affect the network attributes is of seminal interest in the field of neuronal circuit dynamics and plasticity. We used computational simulation of physiologically scaled down neuronal networks consisting of experimentally constrained models of neurons to address this question. Intrinsic excitability in FSINs has been experimentally observed to be altered due to changes in their input resistance and changes in their action potential threshold. To alter the input resistance of the FSINs, we changed the specific membrane resistance (Rm), while to change the action potential threshold we altered the peak delayed potassium conductance (gKDbar) In Wang-Buzsaki type FSIN-FSIN interconnected network models (II network) we observed an increase in the network frequency with increase in FSIN Rm while the network coherence did not change due to the altered FSIN Rm. However, in the same network there was a drastic decrease in both network coherence and network frequency with increase in gKDbar. Next, we built an EI network using 250 model excitatory neurons (ENs) and 50 model FSINs. The ENs were reciprocally connected to the FSINs. Moreover, the FSINs were also interconnected among themselves while the ENs were not. In these EI networks we observed that decreased FSIN Rm, which decreased their excitability, caused a monotonic increase in the excitatory network coherence. However, increased FSIN gKDbar which also decreased their excitability caused a decrease in the excitatory network coherence. The excitatory network frequency was decreased with decreased FSIN Rm or with increased FSIN gKDbar. However, EI networks having decreased FSIN input resistance (~ 50 MΩ) could partially rescue the excitatory network coherence from the desynchronizing effect of increased FSIN gKDbar. In EI networks having higher FSIN input resistance (~ 110 MΩ); even a small increase in FSIN gKDbar caused a drastic decrease in the excitatory network coherence. The phenomenon of altered EI network activity due to altered FSIN Rm or FSIN gKDbar was observed to be significantly independent of the proportion of the FSIN population undergoing the alterations. The observation that even a small proportion of the entire FSIN population (10% and 40%; for FSIN Rm dependence and FSIN gKDbar dependence respectively) can cause a massive shift in the EI network activity indicated the strong influence of FSIN intrinsic excitability on network dynamics. We also observed that the dependence of FSIN Rm on EI network activity was quite robust in the physiological range of the network synaptic parameters. Overall from these studies we observed that DG FSINs express activity dependent plasticity of intrinsic excitability after experiencing near physiological synaptic excitation. Further, altered intrinsic excitability of FSINs can cause robust changes in the connected network. The study suggests possible intrinsic strategies in FSINs which might be functional in neuronal microcircuits during different physiological and pathological conditions.

Slow population activities (SPAs) are population activities in the brain with frequencies of less than 5 Hz. SPAs are prominent in many brain structures including the neocortex and the hippocampus. Examples of SPAs include the neocortical EEG δ waves and the hippocampal large amplitude irregular activities during NREM sleep. These in vivo SPAs are believed to play a fundamental role in brain plasticity. However, despite many experimental attempts to understand SPAs, their mechanisms are still not well understood. It is unclear how the individual neurons can sustain low frequency activities on the network as a whole. In this thesis, we demonstrate that a mathematical and computational perspective is indispensable in understanding slow population phenomena and generating testable hypotheses for future experiments. Our focus is on a hippocampal slice preparation exhibiting spontaneous, inhibitory-based SPAs (hippocampal SPAs). We develop a multi-pronged approach consisting of parameter extraction, simulation, and mathematical analysis to elucidate the mechanisms responsible for hippocampal SPAs. Our results suggest that hippocampal SPAs are an emergent phenomenon. In other words, the network “slowness” is not directly represented by any particular individual element within the network. Instead, the low frequency activities on the network are the result of interactions between synaptic and intrinsic characteristics of individual inhibitory interneurons. Our simulations quantify these characteristics which underlie hippocampal SPAs. Specifically, our simulations predict that individual interneurons should 1) be moderately fast-spiking above threshold before the increase in spike frequency slows down with increasing drive, and 2) be well connected with one another for SPAs to occur. We also predict that excitatory noise levels have a larger influence on hippocampal SPAs than mean excitatory drive. Subsequent mathematical analyses show that the synaptic and intrinsic conditions of individual interneurons as predicted by simulations promote network multi-stability. Hippocampal SPAs occur when the network switches from one network firing state to another. Since many of the parameters we use for simulations are extracted from experiments, our simulation model is likely a reasonable representation of actual biological mechanisms in hippocampal networks.

<p>Interval timing, defined as timing and time perception in the seconds-to-minutes range, is a higher-order cognitive function that has been shown to be critically dependent upon cortico-striatal circuits in the brain. However, our understanding of how different neuronal subtypes within these circuits cooperate to subserve interval timing remains elusive. The present study was designed to investigate this issue by focusing on the spike waveforms of neurons and their synchronous firing patterns with local field potentials (LFPs) recorded from cortico-striatal circuits while rats were performing two standard interval-timing tasks. Experiment 1 demonstrated that neurons in cortico-striatal circuits can be classified into 4 different clusters based on their distinct spike waveforms and behavioral correlates. These distinct neuronal populations were shown to be differentially involved in timing and reward processing. More importantly, the LFP-spike synchrony data suggested that neurons in 1 particular cluster were putative fast-spiking interneurons (FSIs) in the striatum and these neurons responded to both timing and reward processing. Experiment 2 reported electrophysiological data that were similar with previous findings, but identified a different cluster of striatal neurons - putative tonically-active neurons (TANs), revealed by their distinct spike waveforms and special firing patterns during the acquisition of the task. These firing patterns of FSIs and TANs were in contrast with potential striatal medium-spiny neurons (MSNs) that preferentially responded to temporal processing in the current study. Experiment 3 further investigated the proposal that interval timing is subserved by cortico-striatal circuits by using microstimulation. The findings revealed a stimulation frequency-dependent "stop" or "reset" response pattern in rats receiving microstimulation in either the cortex or the striatum during the performance of the timing task. Taken together, the current findings further support that interval timing is represented in cortico-striatal networks that involve multiple types of interneurons (e.g., FSIs and TANs) functionally connected with the principal projection neurons (i.e., MSNs) in the dorsal striatum. When specific components of these complex networks are electrically stimulated, the ongoing timing processes are temporarily "stopped" or "reset" depending on the properties of the stimulation.</p><br>Dissertation