Neurons hear their echo

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.

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.

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.

We study the propagation of traveling solitary pulses in one-dimensional cortical networks with two types of one-dimensional architectures: networks of excitatory neurons and networks composed of both excitatory and inhibitory neurons. Each neuron is represented by the integrate-and-fire model, and is allowed to fire only one spike. The velocity and stability of propagating, continuous pulses are calculated analytically. For excitatory-only networks, two continuous pulses with different velocities exist if the synaptic coupling is larger than a minimal value; the pulse with the lower velocity is always unstable. Above a certain critical value of the constant delay, continuous pulses lose stability via a Hopf bifurcation, and lurching pulses with spatio-temporal periodicity emerge. The parameter regime for which lurching occurs is strongly affected by the synaptic footprint (connectivity) shape. Two types of stable propagating pulses are observed in networks of excitatory and inhibitory neurons. During fast pulses, inhibitory neurons fire a short time before or after the excitatory neurons. During slow pulses, inhibitory cells fire well before neighboring excitatory cells, and potentials of excitatory cells become negative and then positive before they fire. This work shows that simple models of spiking neurons exhibit a large variety of propagating pulses with various spatio-temporal properties.

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.

The cerebral cortex exhibits a sophisticated neural architecture across its six layers. Recently, it was found that these layers exhibit different ratios of excitatory to inhibitory (EI) neurons, ranging from 4 to 9. This ratio is a key factor for achieving the often reported balance of excitation and inhibition, a hallmark of cortical computation. However, neither previous theoretical nor simulation studies have addressed how these differences in EI ratio will affect layer-specific dynamics and computational properties. We investigate this question using a sparsely connected network model of excitatory and inhibitory neurons. To keep the network in a physiological range of firing rates, we varied the inhibitory firing threshold or the synaptic strength between excitatory and inhibitory neurons. We find that decreasing the EI ratio allows the network to explore a higher-dimensional space and enhance its capacity to represent complex input. By comparing the empirical EI ratios of layer 2/3 and layer 4 in the rodent barrel cortex, we predict that layer 2/3 has a higher dimensionality and coding capacity than layer 4. Furthermore, our analysis of primary visual cortex data from the Allen Brain Institute corroborates these modelling results, also demonstrating increased dimensionality and coding capabilities of layer 2/3.Experimental studies indicate that the ratio of excitatory to inhibitory neurons varies across different cortical layers. In this study, we investigate how these varying excitatory-to-inhibitory (EI) ratios affect the layer-specific dynamics and computational capacity of cortical networks. We modeled a randomly connected network of spiking neurons, incorporating different EI ratios based on experimental observations. Our findings reveal that as the influence of inhibition increases, corresponding to lower EI ratios, the network explores a higher dimensionality in its activity, thereby enhancing its capacity to encode high-dimensional inputs. These results align with our analysis of experimental data recorded from layers 2/3 and layer 4 of the rodent primary visual cortex. Specifically, our findings support the hypothesis that layer 2/3, which has a lower EI ratio compared to layer 4, possesses a greater computational capacity.

CD40-activated CD40L-mediated reverse signalling is a major physiological regulator of neurite growth from excitatory and inhibitory neurons in the developing central nervous system (CNS). Whereas in excitatory pyramidal neurons, CD40L reverse signalling promotes the growth and elaboration of dendrites and axons, in inhibitory GABAergic striatal medium spiny neurons (MSNs), it restricts neurite growth and branching. In pyramidal neurons, we previously reported that CD40L reverse signalling activates an interconnected and interdependent signalling network involving protein kinase C (PKC), extracellular regulated kinases 1 and 2 (ERK1/2), and c-Jun N-terminal kinase (JNK) signalling pathways that regulates dendrite and axon growth. Here, we have studied whether these signalling pathways also influence neurite growth from striatal inhibitory MSNs. To unequivocally activate CD40L reverse signalling, we treated MSN cultures from CD40-deficient mice with CD40-Fc. Here, we report that activation of CD40L reverse signalling in these cultures also increased the phosphorylation of PKC, ERK1/2, and JNK. Using pharmacological activators and inhibitors of these signalling pathways singularly and in combination, we have shown that, as in pyramidal neurons, these signalling pathways work in an interconnected and interdependent network to regulate the neurite growth, but their functions, relationships, and interdependencies are different from those observed in pyramidal neurons. Furthermore, immunoprecipitation studies showed that stimulation of CD40L reverse signalling recruits the catalytic fragment of Syk tyrosine kinase, but in contrast to pyramidal neurons, PKC does not participate in this recruitment. Our findings show that distinctive networks of three signalling pathways mediate the opposite effects of CD40L reverse signalling on neurite growth in excitatory and inhibitory neurons.

Reelin is a secretory protein involved in a variety of processes in forebrain development and function, including neuronal migration, dendrite growth, spine formation, and synaptic plasticity. Most of the function of Reelin is focused on excitatory neurons; however, little is known about its effects on inhibitory neurons and inhibitory synapses. In this study, we investigated the phosphatidylinositol 3-kinase/Akt pathway of Reelin in primary cortical and hippocampal neurons. Individual neurons were visualized using immunofluorescence to distinguish inhibitory neurons from excitatory neurons. Reelin-rich protein supplementation significantly induced the phosphorylation of Akt and ribosomal S6 protein in excitatory neurons, but not in most inhibitory neurons. In somatostatin-expressing inhibitory neurons, one of major subtypes of inhibitory neurons, Reelin-rich protein supplementation induced the phosphorylation of S6. Subsequently, we investigated whether or not Reelin-rich protein supplementation affected dendrite development in cultured inhibitory neurons. Reelin-rich protein supplementation did not change the total length of dendrites in inhibitory neurons in vitro. Finally, we examined the development of inhibitory synapses in primary hippocampal neurons and found that Reelin-rich protein supplementation significantly reduced the density of gephyrin–VGAT-positive clusters in the dendritic regions without changing the expression levels of several inhibitory synapse-related proteins. These findings indicate a new role for Reelin in specific groups of inhibitory neurons and the development of inhibitory synapses, which may contribute to the underlying cellular mechanisms of RELN-associated neurological disorders.