Differencing Algorithm for Exponentially Decaying Sinusoid Analysis

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.

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.

Neurons in the brain form thousands of connections, or synapses, with one another, allowing signals to pass from one cell to the next. To activate a neuron, a high enough activating signal or ‘action potential’ must be reached. However, the accepted view of signal transmission assumes that the great majority of synapses are too weak to activate neurons. This means that often simultaneous inputs from many neurons are required to trigger a single neuron’s activation. However, such coordination is likely unreliable as neurons can react differently to the same stimulus depending on the circumstances. An alternative way of transmitting signals has been reported in turtle brains, where impulses from a single neuron can trigger activity across a network of connections. Furthermore, these responses are reliably repeatable, activating the same neurons in the same order. Riquelme et al. set out to understand the mechanism that underlies this type of neuron activation using a mathematical model based on data from the turtle brain. These data showed that the neural network in the turtle’s brain had many weak synapses but also a few, rare, strong synapses. Simulating this neural network showed that those rare, strong synapses promote the signal’s reliability by providing a consistent route for the signal to travel through the network. The numerous weak synapses, on the other hand, have a regulatory role in providing flexibility to how the activation spreads. This combination of strong and weak connections produces a system that can reliably promote or stop the signal flow depending on the context. Riquelme et al.’s work describes a potential mechanism for how signals might travel reliably through neural networks in the brain, based on data from turtles. Experimental work will need to address whether strong connections play a similar role in other animal species, including humans. In the future, these results may be used as the basis to design new systems for artificial intelligence, building on the success of neural networks.

Npas4 Regulates Excitatory-Inhibitory Balance within Neural Circuits through Cell-Type-Specific Gene Programs

The firing patterns of neuronal populations often exhibit emergent collective oscillations, which can display substantial regularity even though the dynamics of individual elements is very stochastic. One of the many phenomena that is often studied in this context is coherence resonance, where additional noise leads to improved regularity of spiking activity in neurons. In this work, we investigate how the coherence resonance phenomenon manifests itself in populations of excitatory and inhibitory neurons. In our simulations, we use the coupled FitzHugh-Nagumo oscillators in the excitable regime and in the presence of neuronal noise. Formally, our model is based on the concept of a two-layered network, where one layer contains inhibitory neurons, the other excitatory neurons, and the interlayer connections represent heterotypic interactions. The neuronal activity is simulated in realistic coupling schemes in which neurons within each layer are connected with undirected connections, whereas neurons of different types are connected with directed interlayer connections. In this setting, we investigate how different neurophysiological determinants affect the coherence resonance. Specifically, we focus on the proportion of inhibitory neurons, the proportion of excitatory interlayer axons, and the architecture of interlayer connections between inhibitory and excitatory neurons. Our results reveal that the regularity of simulated neural activity can be increased by a stronger damping of the excitatory layer. This can be accomplished with a higher proportion of inhibitory neurons, a higher fraction of inhibitory interlayer axons, a stronger coupling between inhibitory axons, or by a heterogeneous configuration of interlayer connections. Our approach of modeling multilayered neuronal networks in combination with stochastic dynamics offers a novel perspective on how the neural architecture can affect neural information processing and provide possible applications in designing networks of artificial neural circuits to optimize their function via noise-induced phenomena.

Electrophysiological data from the rodent whisker/barrel cortex indicate that GABAergic, presumed inhibitory, neurons respond more vigorously to stimulation than glutamatergic, presumed excitatory, cells. However, these data represent very small neuronal samples in restrained, anesthetized, or narcotized animals or in cortical slices. Histochemical data from primate visual cortex, stained for the mitochondrial enzyme cytochrome oxidase (CO) and for GABA, show that GABAergic neurons are more highly reactive for CO than glutamatergic cells, indicating that inhibitory neurons are chronically more active than excitatory neurons but leaving doubt about the short-term stimulus dependence of this activation. Taken together, these results suggest that highly active inhibitory neurons powerfully influence relatively inactive excitatory cells but do not demonstrate directly the relative activities of excitatory and inhibitory neurons in the cortex during normal behavior. We used a novel double-labeling technique to approach the issue of excitatory and inhibitory neuronal activation during behavior. Our technique combines high-resolution 2-deoxyglucose (2DG), immunohistochemical staining for neurotransmitter-specific antibodies, and automated image analysis to collect the data. We find that putative inhibitory neurons in barrel cortex of behaving animals are, on average, much more heavily 2DG-labeled than presumed excitatory cells, a pattern not seen in animals anesthetized at the time of 2DG injection. This metabolic activation is dependent specifically on sensory inputs from the whiskers, because acute trimming of most whiskers greatly reduces 2DG labeling in both cell classes in columns corresponding to trimmed whiskers. Our results provide confirmation of the active GABAergic cell hypothesis suggested by CO and single-unit data. We conclude that strong activation of inhibitory cortical neurons must confer selective advantages that compensate for its inherent energy inefficiency.

GLUT8 glucose transporter is localized to excitatory and inhibitory neurons in the rat hippocampus

Spiking Irregularity and Frequency Modulate the Behavioral Report of Single-Neuron Stimulation

The brain is a network system in which excitatory and inhibitory neurons keep activity balanced in the highly non-random connectivity pattern of the microconnectome. It is well known that the relative percentage of inhibitory neurons is much smaller than excitatory neurons in the cortex. So, in general, how inhibitory neurons can keep the balance with the surrounding excitatory neurons is an important question. There is much accumulated knowledge about this fundamental question. This study quantitatively evaluated the relatively higher functional contribution of inhibitory neurons in terms of not only properties of individual neurons, such as firing rate, but also in terms of topological mechanisms and controlling ability on other excitatory neurons. We combined simultaneous electrical recording (~2.5 hours) of ~1000 neurons in vitro, and quantitative evaluation of neuronal interactions including excitatory-inhibitory categorization. This study accurately defined recording brain anatomical targets, such as brain regions and cortical layers, by inter-referring MRI and immunostaining recordings. The interaction networks enabled us to quantify topological influence of individual neurons, in terms of controlling ability to other neurons. Especially, the result indicated that highly influential inhibitory neurons show higher controlling ability of other neurons than excitatory neurons, and are relatively often distributed in deeper layers of the cortex. Furthermore, the neurons having high controlling ability are more effectively limited in number than central nodes of k-cores, and these neurons also participate in more clustered motifs. In summary, this study suggested that the high controlling ability of inhibitory neurons is a key mechanism to keep balance with a large number of other excitatory neurons beyond simple higher firing rate. Application of the selection method of limited important neurons would be also applicable for the ability to effectively and selectively stimulate E/I imbalanced disease states.

The functional implications of correlations in cortical networks are still highly debated [1] and theoreticians are intensely searching for a self-consistent solution of the correlation structure in recurrent networks. Feed-forward descriptions have been presented as approximations [2] and different aspects of correlation functions in the asynchronous irregular state have been accurately predicted, such as the zero time lag correlation [3] and its scaling with network size on a coarse time scale [1]. Previous approaches do, however, not explain the differences between the correlation functions for excitatory and inhibitory neurons and they do not describe their temporal structure, an experimentally observable feature that has important functional consequences for synaptic plasticity [4]. The approximation of neural dynamics by a linear response kernel is a powerful technique in the analysis of recurrent networks. Here we use Hawkes processes [4,5] to model the spiking activity of a neuron as a rate-modulated Poisson process, where incoming synaptic events cause exponentially decaying deflections of the instantaneous firing rate that superimpose linearly. We analytically determine the correlation structure of recurrent random networks of these excitatory and inhibitory linear neurons with delayed pulse-coupling. We show that this minimal linear model is sufficient to explain generic features of correlations: The origin of troughs near the center peak, the asymmetry between excitatory and inhibitory neurons, and the emergence of damped oscillatory correlation functions (Fig. ​(Fig.1A).1A). In our derivation we employ a novel series expansion of the correlation function in terms of resonance frequencies of the delayed feedback system, that is valid in the whole parameter regime of inhibition dominated networks. Previous expansions were limited to a feedback gain below 1 [4]. Our results identify two distinct contributions to the correlation: a feed-forward term due to correlated inputs (Fig. ​(Fig.1B,1B, black) and a self-feedback term due to the activity of the neurons under consideration (Fig. ​(Fig.1B,1B, gray). This self-feedback explains the asymmetry of correlations between excitatory and inhibitory neurons (Fig. ​(Fig.1A,1A, black: simulation, gray: theory). Figure 1

Male ejaculation acutely suppresses sexual motivation in male mice. In contrast, relatively little is known about how male ejaculation affects sexual motivation and sexual behavior in female mice. How the brain responds to completion of mating is also unclear. Here, by using self-paced mating assay, we first demonstrate that female mice show decreased sexual motivation acutely after experiencing male ejaculation. By using brain-wide analysis of activity-dependent labeling, we next pinpointed the medial preoptic area as a brain region strongly activated during the post-ejaculatory period. Furthermore, using freely moving in vivo calcium imaging to compare neural activity of inhibitory and excitatory neurons in the medial preoptic area, we revealed that a subset of the neurons in this region respond significantly and specifically to male ejaculation but not to female-to-male sniffing or to male mounting. While there were excitatory and inhibitory neurons that showed increased response to male ejaculation, the response magnitude as well as the proportion of neurons responding to the event was significantly larger in the inhibitory neuron population. Next, by unbiased classification of their responses, we also found a subpopulation of neurons that increase their activity late after the onset of male ejaculation. These neurons were all inhibitory indicating that male ejaculation induces a prolonged inhibitory activity in the medial preoptic area. Lastly, we found that chemogenetic activation of medial preoptic area neurons that were active during post-ejaculatory period, but not during appetitive or consummatory periods, were sufficient to suppress female sexual motivation. Together, our data illuminate the importance of medial preoptic area as a brain node which encodes a negative signal that sustains low sexual motivation state after the female mice experience ejaculation.

Animal studies have shown that cerebellar projections influence both excitatory and inhibitory neurones in the motor cortex but this connectivity has yet to be demonstrated in human subjects. In human subjects, magnetic or electrical stimulation of the cerebellum 5-7 ms before transcranial magnetic stimulation (TMS) of the motor cortex decreases the TMS-induced motor-evoked potential (MEP), indicating a cerebellar inhibition of the motor cortex (CBI). TMS also reveals inhibitory and excitatory circuits of the motor cortex, including a short-interval intracortical inhibition (SICI), long-interval intracortical inhibition (LICI) and intracortical facilitation (ICF). This study used magnetic cerebellar stimulation to investigate connections between the cerebellum and these cortical circuits. Three experiments were performed on 11 subjects. The first experiment showed that with increasing test stimulus intensities, LICI, CBI and ICF decreased, while SICI increased. The second experiment showed that the presence of CBI reduced SICI and increased ICF. The third experiment showed that the interaction between CBI and LICI reduced CBI. Collectively, these findings suggest that cerebellar stimulation results in changes to both inhibitory and excitatory neurones in the human motor cortex.

Basal ganglia circuits in Parkinson’s disease constitute a prominent example of neural system of coupled inhibitory and excitatory neurons, which exhibit synchronous activity [1]. The phase-locking of neural activity in this system, as analyzed in experimental data intraoperatively recorded in Parkinsonian patients, exhibits intermittent temporal dynamics [2]. The mechanisms of this intermittent synchronous dynamics make the subject of this study. The conductance-based network models of subthalamo-pallidal circuitry of basal ganglia have being shown to reproduce the intermittent temporal patterns of phase-locking observed in experiments with high fidelity [3]. We start with this network model of synaptically coupled excitatory subthalamic and inhibitory pallidal bursting neurons. We further reduce it to the model of two mutually coupled inhibitory neurons with self-inhibition. This network is presented at Figure ​Figure1.1. While real subthalamic neurons are excitatory, the resulting two-neurons network is adequate simplification of the original circuitry under assumption of high fidelity of responses of inhibitory pallidal cells. While this reduction may be associated with the loss of some other biologically realistic features, it produces a relatively generic circuit, therefore the studied mechanism may be quite general too. Figure 1 The network architecture. Each connection is inhibitory synapse. We used geometric dynamical systems and singular perturbation methods to reduce the full model to a simpler set of equations. Mathematical analysis was completed using three slow variables with two different time scales. Intermittently synchronous oscillations are generated by overlapped spiking which crucially depend on the geometry of slow phase plane and the interplay between slow variables as well as the strength of synapses. Two slow variables are responsible for the generation of out-of-phase stable solution and the other slower variable for irregular and intermittent activity pattern. The results of analysis can be traced to particular values of biophysical parameters (synaptic strength and parameters of calcium dynamics), which are known to be impacted in Parkinson’s disease.

Membrane Potential Dynamics of GABAergic Neurons in the Barrel Cortex of Behaving Mice

Homer1a, an activity-dependent induced member of the scaffold protein family Homer, plays an essential role in synaptic reorganization and is widely used as a neuronal activity marker. However, the cell type transcribing Homer1a remains unidentified. Here, we investigated the main cell types of the amygdala, hippocampus, primary somatosensory cortex, and dorsal striatum that express Homer1a. Homer1a expression relevant to the baseline neural activity as well as exposure to unfamiliar and conditioned contexts was preferentially detected in the excitatory neurons within the basolateral amygdala, hippocampus, and neocortex, and in inhibitory neurons within the central amygdala and dorsal striatum. These findings indicate that Homer1a is expressed in the principal neurons of each region regardless of whether they are excitatory or inhibitory neurons.