Populations Of Inhibitory Neurons Research Articles

Cortical State Fluctuations across Layers of V1 during Visual Spatial Perception

SUMMARYMany factors modulate the state of cortical activity, but the importance of cortical state variability for sensory perception remains debated. We trained mice to detect spatially localized visual stimuli and simultaneously measured local field potentials and excitatory and inhibitory neuron populations across layers of primary visual cortex (V1). Cortical states with low spontaneous firing and correlations in excitatory neurons, and suppression of 3- to 7-Hz oscillations in layer 4, accurately predicted single-trial visual detection. Our results show that cortical states exert strong effects at the initial stage of cortical processing in V1 and can play a prominent role for visual spatial behavior in mice.

Adaptive Stimulus Design for Dynamic Recurrent Neural Network Models.

We present an adaptive stimulus design method for efficiently estimating the parameters of a dynamic recurrent network model with interacting excitatory and inhibitory neuronal populations. Although stimuli that are optimized for model parameter estimation should, in theory, have advantages over nonadaptive random stimuli, in practice it remains unclear in what way and to what extent an optimal design of time-varying stimuli may actually improve parameter estimation for this common type of recurrent network models. Here we specified the time course of each stimulus by a Fourier series whose amplitudes and phases were determined by maximizing a utility function based on the Fisher information matrix. To facilitate the optimization process, we have derived differential equations that govern the time evolution of the gradients of the utility function with respect to the stimulus parameters. The network parameters were estimated by maximum likelihood from the spike train data generated by an inhomogeneous Poisson process from the continuous network state. The adaptive design process was repeated in a closed loop, alternating between optimal stimulus design and parameter estimation from the updated stimulus-response data. Our results confirmed that, compared with random stimuli, optimally designed stimuli elicited responses with significantly better likelihood values for parameter estimation. Furthermore, all individual parameters, including the time constants and the connection weights, were recovered more accurately by the optimal design method. We also examined how the errors of different parameter estimates were correlated, and proposed heuristic formulas to account for the correlation patterns by an approximate parameter-confounding theory. Our results suggest that although adaptive optimal stimulus design incurs considerable computational cost even for the simplest excitatory-inhibitory recurrent network model, it may potentially help save time in experiments by reducing the number of stimuli needed for network parameter estimation.

Whole-Brain Multimodal Neuroimaging Model Using Serotonin Receptor Maps Explains Non-linear Functional Effects of LSD

Understanding the underlying mechanisms of the human brain in health and disease will require models with necessary and sufficient details to explain how function emerges from the underlying anatomy and is shaped by neuromodulation. Here, we provide such a detailed causal explanation using a whole-brain model integrating multimodal imaging in healthy human participants undergoing manipulation of the serotonin system. Specifically, we combined anatomical data from diffusion magnetic resonance imaging (dMRI) and functional magnetic resonance imaging (fMRI) with neurotransmitter data obtained with positron emission tomography (PET) of the detailed serotonin 2A receptor (5-HT2AR) density map. This allowed us to model the resting state (with and without concurrent music listening) and mechanistically explain the functional effects of 5-HT2AR stimulation with lysergic acid diethylamide (LSD) on healthy participants. The whole-brain model used a dynamical mean-field quantitative description of populations of excitatory and inhibitory neurons as well as the associated synaptic dynamics, where the neuronal gain function of the model is modulated bythe 5-HT2AR density. The model identified the causativemechanisms for the non-linear interactions between the neuronal and neurotransmitter system,which are uniquely linked to (1) the underlying anatomical connectivity, (2) the modulation by the specific brainwide distribution of neurotransmitter receptor density, and (3) the non-linear interactions between the two. Taking neuromodulatory activity into account when modeling global brain dynamics will lead to novel insights into human brain function in health and disease and opens exciting possibilities for drug discovery and design in neuropsychiatric disorders.

Burst synchronization in a scale-free neuronal network with inhibitory spike-timing-dependent plasticity.

We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barabási-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without considering iSTDP, BS was found to appear in a range of noise intensities for fixed synaptic inhibition strengths. In contrast, in our present work, we take into consideration iSTDP and investigate its effect on BS by varying the noise intensity. Our new main result is to find occurrence of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). We note that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic plasticity are reversed in comparison with those in excitatory synaptic plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic burst onset times. Finally, in the presence of iSTDP we investigate the effects of network architecture on BS by varying the symmetric attachment degree and the asymmetry parameter in the SFN.

Distributions of GABAergic and glutamatergic neurons in the brains of a diurnal and nocturnal rodent

Light influences the daily patterning of activity by both synchronizing internal clocks to environmental light-dark cycles and acutely modulating arousal states, a process known as masking. Masking responses are completely reversed in diurnal and nocturnal species. In nocturnal rodents, masking is mediated through a subset of intrinsically photosensitive retinal ganglion cells (ipRGCs) whose projections are similar in diurnal and nocturnal rodents. This raises the possibility that differences in responsivity to signals that these cells release might underlie chronotype differences in masking. We explored one aspect of this hypothesis by examining the distribution of excitatory and inhibitory neuronal populations in many ipRGC target areas of a diurnal species (Nile grass rat) and a nocturnal one (Norway rat). We discovered that while many of these regions were very similar in these two species, there were striking differences in the ventral lateral geniculate nucleus (vLGN; higher density of glutamate cells in Norway rats) and in the lateral habenula (LHb; GABAeric cells present in grass rats, but not Norway rats). These patterns raise the possibility that the vLGN and LHb contribute to differences in masking and/or circadian regulation of diurnal and nocturnal species.

Flexible resonance in prefrontal networks with strong feedback inhibition.

Oscillations are ubiquitous features of brain dynamics that undergo task-related changes in synchrony, power, and frequency. The impact of those changes on target networks is poorly understood. In this work, we used a biophysically detailed model of prefrontal cortex (PFC) to explore the effects of varying the spike rate, synchrony, and waveform of strong oscillatory inputs on the behavior of cortical networks driven by them. Interacting populations of excitatory and inhibitory neurons with strong feedback inhibition are inhibition-based network oscillators that exhibit resonance (i.e., larger responses to preferred input frequencies). We quantified network responses in terms of mean firing rates and the population frequency of network oscillation; and characterized their behavior in terms of the natural response to asynchronous input and the resonant response to oscillatory inputs. We show that strong feedback inhibition causes the PFC to generate internal (natural) oscillations in the beta/gamma frequency range (>15 Hz) and to maximize principal cell spiking in response to external oscillations at slightly higher frequencies. Importantly, we found that the fastest oscillation frequency that can be relayed by the network maximizes local inhibition and is equal to a frequency even higher than that which maximizes the firing rate of excitatory cells; we call this phenomenon population frequency resonance. This form of resonance is shown to determine the optimal driving frequency for suppressing responses to asynchronous activity. Lastly, we demonstrate that the natural and resonant frequencies can be tuned by changes in neuronal excitability, the duration of feedback inhibition, and dynamic properties of the input. Our results predict that PFC networks are tuned for generating and selectively responding to beta- and gamma-rhythmic signals due to the natural and resonant properties of inhibition-based oscillators. They also suggest strategies for optimizing transcranial stimulation and using oscillatory networks in neuromorphic engineering.

Immediate and Medium-term Changes in Cortical and Hippocampal Inhibitory Neuronal Populations after Diffuse TBI

Changes in inhibition following traumatic brain injury (TBI) appear to be one of the major factors that contribute to excitation:inhibition imbalance. Neuron pathology, interneurons in particular evolves from minutes to weeks post injury and follows a complex time course. Previously, we showed that in the long-term in diffuse TBI (dTBI), there was select reduction of specific dendrite-targeting neurons in sensory cortex and hippocampus while in motor cortex there was up-regulation of specific dendrite-targeting neurons. We now investigated the time course of dTBI effects on interneurons in neocortex and hippocampus. Brains were labeled with antibodies against calbindin (CB), parvalbumin (PV), calretinin (CR) neuropeptide Y (NPY), and somatostatin (SOM) at 24 h and 2 weeks post dTBI. We found time-dependent, brain area-specific changes in inhibition at 24 h and 2 weeks. At 24 h post-injury, reduction of dendrite-targeting inhibitory neurons occurred in sensory cortex and hippocampus. At 2 weeks, we found compensatory changes in the somatosensory cortex and CA2/3 of hippocampus affected at 24 h, with affected interneuronal populations returning to sham levels. However, DG of hippocampus now showed reduction of dendrite-targeting inhibitory neurons. Finally, with respect to motor cortex, there was an upregulation of dendrite-targeting interneurons in the supragranular layers at 24 h returning to normal levels by 2 weeks. Overall, our findings reconfirm that dendritic inhibition is particularly susceptible to brain trauma, but also show that there are complex brain-area-specific changes in inhibitory neuronal numbers and in compensatory changes, rather than a simple monotonic progression of changes post-dTBI.

Effect of inhibitory spike-timing-dependent plasticity on fast sparsely synchronized rhythms in a small-world neuronal network

We consider the Watts–Strogatz small-world network (SWN) consisting of inhibitory fast spiking Izhikevich interneurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without iSTDP, fast sparsely synchronized rhythms, associated with diverse cognitive functions, were found to appear in a range of large noise intensities for fixed strong synaptic inhibition strengths. Here, we investigate the effect of iSTDP on fast sparse synchronization (FSS) by varying the noise intensity D. We employ an asymmetric anti-Hebbian time window for the iSTDP update rule [which is in contrast to the Hebbian time window for the excitatory STDP (eSTDP)]. Depending on values of D, population-averaged values of saturated synaptic inhibition strengths are potentiated [long-term potentiation (LTP)] or depressed [long-term depression (LTD)] in comparison with the initial mean value, and dispersions from the mean values of LTP/LTD are much increased when compared with the initial dispersion, independently of D. In most cases of LTD where the effect of mean LTD is dominant in comparison with the effect of dispersion, good synchronization (with higher spiking measure) is found to get better via LTD, while bad synchronization (with lower spiking measure) is found to get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). Emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, we also investigate the effects of network architecture on FSS by changing the rewiring probability p of the SWN in the presence of iSTDP.

Experience-dependent MeCP2 expression in the excitatory cells of mouse visual thalamus.

Loss or gain of copy number of the gene encoding the transcription factor methyl-CpG-binding protein 2 (MeCP2) leads to neurodevelopmental disorders (Rett and MeCP2 duplication syndrome), indicating that precisely regulated MeCP2 expression during development is critical for mental health. Consistent with this idea, MeCP2 null mutants exhibit synaptic regression in the dorsal lateral geniculate nucleus (dLGN), the visual relay center in the thalamus, a phenotype resembling that of animals reared in the dark during the visual sensitive period. It remains unclear how MeCP2 expression is regulated during circuit formation and maturation, especially in excitatory and inhibitory populations of neurons. We found that, concomitant with the initiation of the dark-rearing sensitive period, MeCP2 protein levels were elevated in glutamatergic but not GABAergic neurons of the dLGN. Moreover, MeCP2 expression in glutamatergic populations was selectively reduced by dark-rearing. Therefore, we propose that visual experience–dependent MeCP2 induction in glutamatergic populations is essential for synaptic maturation within the dLGN.

Identification of a Rhythmic Firing Pattern in the Enteric Nervous System That Generates Rhythmic Electrical Activity in Smooth Muscle.

The enteric nervous system (ENS) contains millions of neurons essential for organization of motor behavior of the intestine. It is well established that the large intestine requires ENS activity to drive propulsive motor behaviors. However, the firing pattern of the ENS underlying propagating neurogenic contractions of the large intestine remains unknown. To identify this, we used high-resolution neuronal imaging with electrophysiology from neighboring smooth muscle. Myoelectric activity underlying propagating neurogenic contractions along murine large intestine [also referred to as colonic migrating motor complexes, (CMMCs)] consisted of prolonged bursts of rhythmic depolarizations at a frequency of ∼2 Hz. Temporal coordination of this activity in the smooth muscle over large spatial fields (∼7 mm, longitudinally) was dependent on the ENS. During quiescent periods between neurogenic contractions, recordings from large populations of enteric neurons, in mice of either sex, revealed ongoing activity. The onset of neurogenic contractions was characterized by the emergence of temporally synchronized activity across large populations of excitatory and inhibitory neurons. This neuronal firing pattern was rhythmic and temporally synchronized across large numbers of ganglia at ∼2 Hz. ENS activation preceded smooth muscle depolarization, indicating rhythmic depolarizations in smooth muscle were controlled by firing of enteric neurons. The cyclical emergence of temporally coordinated firing of large populations of enteric neurons represents a unique neural motor pattern outside the CNS. This is the first direct observation of rhythmic firing in the ENS underlying rhythmic electrical depolarizations in smooth muscle. The pattern of neuronal activity we identified underlies the generation of CMMCs.SIGNIFICANCE STATEMENT How the enteric nervous system (ENS) generates neurogenic contractions of smooth muscle in the gastrointestinal (GI) tract has been a long-standing mystery in vertebrates. It is well known that myogenic pacemaker cells exist in the GI tract [called interstitial cells of Cajal (ICCs)] that generate rhythmic myogenic contractions. However, the mechanisms underlying the generation of rhythmic neurogenic contractions of smooth muscle in the GI tract remains unknown. We developed a high-resolution neuronal imaging method with electrophysiology to address this issue. This technique revealed a novel pattern of rhythmic coordinated neuronal firing in the ENS that has never been identified. Rhythmic neuronal firing in the ENS was found to generate rhythmic neurogenic depolarizations in smooth muscle that underlie contraction of the GI tract.

Organization of the core respiratory network: Insights from optogenetic and modeling studies.

The circuit organization within the mammalian brainstem respiratory network, specifically within and between the pre-Bötzinger (pre-BötC) and Bötzinger (BötC) complexes, and the roles of these circuits in respiratory pattern generation are continuously debated. We address these issues with a combination of optogenetic experiments and modeling studies. We used transgenic mice expressing channelrhodopsin-2 under the VGAT-promoter to investigate perturbations of respiratory circuit activity by site-specific photostimulation of inhibitory neurons within the pre-BötC or BötC. The stimulation effects were dependent on the intensity and phase of the photostimulation. Specifically: (1) Low intensity (≤ 1.0 mW) pulses delivered to the pre-BötC during inspiration did not terminate activity, whereas stronger stimulations (≥ 2.0 mW) terminated inspiration. (2) When the pre-BötC stimulation ended in or was applied during expiration, rebound activation of inspiration occurred after a fixed latency. (3) Relatively weak sustained stimulation (20 Hz, 0.5–2.0 mW) of pre-BötC inhibitory neurons increased respiratory frequency, while a further increase of stimulus intensity (> 3.0 mW) reduced frequency and finally (≥ 5.0 mW) terminated respiratory oscillations. (4) Single pulses (0.2–5.0 s) applied to the BötC inhibited rhythmic activity for the duration of the stimulation. (5) Sustained stimulation (20 Hz, 0.5–3.0 mW) of the BötC reduced respiratory frequency and finally led to apnea. We have revised our computational model of pre-BötC and BötC microcircuits by incorporating an additional population of post-inspiratory inhibitory neurons in the pre-BötC that interacts with other neurons in the network. This model was able to reproduce the above experimental findings as well as previously published results of optogenetic activation of pre-BötC or BötC neurons obtained by other laboratories. The proposed organization of pre-BötC and BötC circuits leads to testable predictions about their specific roles in respiratory pattern generation and provides important insights into key circuit interactions operating within brainstem respiratory networks.

Highly scalable generation of DNA methylation profiles in single cells.

We present a highly scalable assay for whole-genome methylation profiling of single cells. We use our approach, single-cell combinatorial indexing for methylation analysis (sci-MET), to produce 3,282 single-cell bisulfite sequencing libraries and achieve read alignment rates of 68 ± 8%. We apply sci-MET to discriminate the cellular identity of a mixture of three human cell lines and to identify excitatory and inhibitory neuronal populations from mouse cortical tissue.

Representation of the stomatopod's retinal midband in the optic lobes: Putative neural substrates for integrating chromatic, achromatic and polarization information.

Stomatopods have an elaborate visual system served by a retina that is unique to this class of pancrustaceans. Its upper and lower eye hemispheres encode luminance and linear polarization while an equatorial band of photoreceptors termed the midband detects color, circularly polarized light and linear polarization in the ultraviolet. In common with many malacostracan crustaceans, stomatopods have stalked eyes, but they can move these independently within three degrees of rotational freedom. Both eyes separately use saccadic and scanning movements but they can also move in a coordinated fashion to track selected targets or maintain a forward eyestalk posture during swimming. Visual information is initially processed in the first two optic neuropils, the lamina and the medulla, where the eye's midband is represented by enlarged regions within each neuropil that contain populations of neurons, the axons of which are segregated from the neuropil regions subtending the hemispheres. Neuronal channels representing the midband extend from the medulla to the lobula where populations of putative inhibitory glutamic acid decarboxylase-positive neurons and tyrosine hydroxylase-positive neurons intrinsic to the lobula have specific associations with the midband. Here we investigate the organization of the midband representation in the medulla and the lobula in the context of their overall architecture. We discuss the implications of observed arrangements, in which midband inputs to the lobula send out collaterals that extend across the retinotopic mosaic pertaining to the hemispheres. This organization suggests an integrative design that diverges from the eumalacostracan ground pattern and, for the stomatopod, enables color and polarization information to be integrated with luminance information that presumably encodes shape and motion.

Identification of a novel gene regulating amygdala-mediated fear extinction

Recent years have seen advances in our understanding of the neural circuits associated with trauma-related disorders, and the development of relevant assays for these behaviors in rodents. Although inherited factors are known to influence individual differences in risk for these disorders, it has been difficult to identify specific genes that moderate circuit functions to affect trauma-related behaviors. Here, we exploited robust inbred mouse strain differences in Pavlovian fear extinction to uncover quantitative trait loci (QTL) associated with this trait. We found these strain differences to be resistant to developmental cross-fostering and associated with anatomical variation in basolateral amygdala (BLA) perineuronal nets, which are developmentally implicated in extinction. Next, by profiling extinction-driven BLA expression of QTL-linked genes, we nominated Ppid (peptidylprolyl isomerase D, a member of the tetratricopeptide repeat (TPR) protein family) as an extinction-related candidate gene. We then showed that Ppid was enriched in excitatory and inhibitory BLA neuronal populations, but at lower levels in the extinction-impaired mouse strain. Using a virus-based approach to directly regulate Ppid function, we demonstrated that downregulating BLA-Ppid impaired extinction, while upregulating BLA-Ppid facilitated extinction and altered in vivo neuronal extinction encoding. Next, we showed that Ppid colocalized with the glucocorticoid receptor (GR) in BLA neurons and found that the extinction-facilitating effects of Ppid upregulation were blocked by a GR antagonist. Collectively, our results identify Ppid as a novel gene involved in regulating extinction via functional actions in the BLA, with possible implications for understanding genetic and pathophysiological mechanisms underlying risk for trauma-related disorders.

Somatostatin and parvalbumin inhibitory synapses onto hippocampal pyramidal neurons are regulated by distinct mechanisms

Excitation-inhibition balance is critical for optimal brain function, yet the mechanisms underlying the tuning of inhibition from different populations of inhibitory neurons are unclear. Here, we found evidence for two distinct pathways through which excitatory neurons cell-autonomously modulate inhibitory synapses. Synapses from parvalbumin-expressing interneurons onto hippocampal pyramidal neurons are regulated by neuronal firing, signaling through L-type calcium channels. Synapses from somatostatin-expressing interneurons are regulated by NMDA receptors, signaling through R-type calcium channels. Thus, excitatory neurons can cell-autonomously regulate their inhibition onto different subcellular compartments through their input (glutamatergic signaling) and their output (firing). Separately, while somatostatin and parvalbumin synapses onto excitatory neurons are both dependent on a common set of postsynaptic proteins, including gephyrin, collybistin, and neuroligin-2, decreasing neuroligin-3 expression selectively decreases inhibition from somatostatin interneurons, and overexpression of neuroligin-3 selectively enhances somatostatin inhibition. These results provide evidence that excitatory neurons can selectively regulate two distinct sets of inhibitory synapses.

Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models.

Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, “The Virtual Brain (TVB)”, based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes.

Non-canonical heterogeneous cellular distribution and co-localization of CaMKII\u03b1 and CaMKII\u03b2 in the spinal superficial dorsal horn

Ca2+/calmodulin-dependent protein kinase II (CaMKII) is a key enzyme in long-term plasticity in many neurons, including in the nociceptive circuitry of the spinal dorsal horn. However, although the role of CaMKII heterooligomers in neuronal plasticity is isoform-dependent, the distribution and co-localization of CaMKII isoforms in the dorsal horn have not been comprehensively investigated. Here, quantitative immunofluorescence analysis was used to examine the distribution of the two major neuronal CaMKII isoforms, α and β, in laminae I–III of the rat dorsal horn, with reference to inhibitory interneurons and neuronal populations defined by expression of parvalbumin, calretinin, and calbindin D28k. Unexpectedly, all or nearly all inhibitory and excitatory neurons showed both CaMKIIα and CaMKIIβ immunoreactivity, although at highly variable levels. Lamina III neurons showed less CaMKIIα immunoreactivity than laminae I–II neurons. Whereas CaMKIIα immunoreactivity was found at nearly similar levels in inhibitory and excitatory neurons, CaMKIIβ generally showed considerably lower immunoreactivity in inhibitory neurons. Distinct populations of inhibitory calretinin neurons and excitatory parvalbumin neurons exhibited high CaMKIIα-to-CaMKIIβ immunoreactivity ratios. CaMKIIα and CaMKIIβ immunoreactivity showed positive correlation at GluA2+ puncta in pepsin-treated tissue. These results suggest that, unlike the forebrain, the dorsal horn is characterized by similar expression of CaMKIIα in excitatory and inhibitory neurons, whereas CaMKIIβ is less expressed in inhibitory neurons. Moreover, CaMKII isoform expression varies considerably within and between neuronal populations defined by laminar location, calcium-binding protein expression, and transmitter phenotype, suggesting differences in CaMKII function both between and within neuronal populations in the superficial dorsal horn.

Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick

Balanced networks are a frequently employed basic model for neuronal networks in the mammalian neocortex. Large numbers of excitatory and inhibitory neurons are recurrently connected so that the numerous positive and negative inputs that each neuron receives cancel out on average. Neuronal firing is therefore driven by fluctuations in the input and resembles the irregular and asynchronous activity observed in cortical in vivo data. Recently, the balanced network model has been extended to accommodate clusters of strongly interconnected excitatory neurons in order to explain persistent activity in working memory-related tasks. This clustered topology introduces multistability and winnerless competition between attractors and can capture the high trial-to-trial variability and its reduction during stimulation that has been found experimentally. In this prospect article, we review the mean field description of balanced networks of binary neurons and apply the theory to clustered networks. We show that the stable fixed points of networks with clustered excitatory connectivity tend quickly towards firing rate saturation, which is generally inconsistent with experimental data. To remedy this shortcoming, we then present a novel perspective on networks with locally balanced clusters of both excitatory and inhibitory neuron populations. This approach allows for true multistability and moderate firing rates in activated clusters over a wide range of parameters. Our findings are supported by mean field theory and numerical network simulations. Finally, we discuss possible applications of the concept of joint excitatory and inhibitory clustering in future cortical network modelling studies.

P.1.j.001 - Hormone and menstrual cycle effects on sleep spindle parameters in oral contraceptive users and controls

We consider the Watts–Strogatz small-world network (SWN) consisting of inhibitory fast spiking Izhikevich interneurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without iSTDP, fast sparsely synchronized rhythms, associated with diverse cognitive functions, were found to appear in a range of large noise intensities for fixed strong synaptic inhibition strengths. Here, we investigate the effect of iSTDP on fast sparse synchronization (FSS) by varying the noise intensity D. We employ an asymmetric anti-Hebbian time window for the iSTDP update rule [which is in contrast to the Hebbian time window for the excitatory STDP (eSTDP)]. Depending on values of D, population-averaged values of saturated synaptic inhibition strengths are potentiated [long-term potentiation (LTP)] or depressed [long-term depression (LTD)] in comparison with the initial mean value, and dispersions from the mean values of LTP/LTD are much increased when compared with the initial dispersion, independently of D. In most cases of LTD where the effect of mean LTD is dominant in comparison with the effect of dispersion, good synchronization (with higher spiking measure) is found to get better via LTD, while bad synchronization (with lower spiking measure) is found to get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). Emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, we also investigate the effects of network architecture on FSS by changing the rewiring probability p of the SWN in the presence of iSTDP.

A Laminar Organization for Selective Cortico-Cortical Communication.

The neocortex is central to mammalian cognitive ability, playing critical roles in sensory perception, motor skills and executive function. This thin, layered structure comprises distinct, functionally specialized areas that communicate with each other through the axons of pyramidal neurons. For the hundreds of such cortico-cortical pathways to underlie diverse functions, their cellular and synaptic architectures must differ so that they result in distinct computations at the target projection neurons. In what ways do these pathways differ? By originating and terminating in different laminae, and by selectively targeting specific populations of excitatory and inhibitory neurons, these “interareal” pathways can differentially control the timing and strength of synaptic inputs onto individual neurons, resulting in layer-specific computations. Due to the rapid development in transgenic techniques, the mouse has emerged as a powerful mammalian model for understanding the rules by which cortical circuits organize and function. Here we review our understanding of how cortical lamination constrains long-range communication in the mammalian brain, with an emphasis on the mouse visual cortical network. We discuss the laminar architecture underlying interareal communication, the role of neocortical layers in organizing the balance of excitatory and inhibitory actions, and highlight the structure and function of layer 1 in mouse visual cortex.