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Cortical gain regulation allows neurons to respond adaptively to changing inputs. Neural gain is modulated by internal and external influences, including attentional and arousal states, motor activity and neuromodulatory input. These influences converge to a common set of mechanisms for gain modulation, including GABAergic inhibition, synaptically driven fluctuations in membrane potential, changes in cellular conductance and changes in other biophysical neural properties. Recent work has identified GABAergic interneurons as targets of neuromodulatory input and mediators of state-dependent gain modulation. Here, we review the engagement and effects of gain modulation in the cortex. We highlight key recent findings that link phenomenological observations of gain modulation to underlying cellular and circuit-level mechanisms. Finally, we place these cellular and circuit interactions in the larger context of their impact on perception and cognition.

Declines in amphibians are a global problem, influenced by complex local factors. While many factors contribute to these declines, much attention has been focused on the roles of contaminants and pathogens. Throughout eastern South Dakota, row-crop farming has contributed to habitat degradation for many amphibians, often through increases in environmental contaminants. For two years we visited four wetlands (two reference wetlands, two agricultural wetlands) to measure water-borne corticosterone (CORT) release rates and ranavirus in larval Western Tiger Salamander (Ambystoma mavortium). We found that both water-borne CORT release rates and ranavirus infection load were greater in larval salamanders from agricultural wetlands compared to reference wetlands. We also found that water-borne CORT release rates were greater in ranavirus-infected individuals compared to uninfected individuals and that water-borne CORT is positively correlated with ranavirus infection load. Though the causal relationships among contaminants, CORT, and ranavirus infection are difficult to determine, chronically elevated CORT is known to be immunosuppressive and may result in high infection loads. This study further describes the negative effects of crop production on amphibian health, provides the first evidence of ranavirus in South Dakota, and supports the use of water-borne CORT as a biomarker of amphibian population health in row-crop landscapes.

A plethora of recent experimental literature implicates the abrupt, synchronous activation of GABAergic interneurons in driving the sudden change in brain activity that heralds seizure initiation. However, the mechanisms predisposing an inhibitory network toward sudden coherence specifically during ictogenesis remain unknown. We address this question by comparing simulated inhibitory networks containing control interneurons and networks containing hyper-excitable interneurons modeled to mimic treatment with 4-Aminopyridine (4-AP), an agent commonly used to model seizuresin vivoandin vitro. Ourin silicostudy demonstrates that model inhibitory networks with 4-AP interneurons are more prone than their control counterparts to exist in a bistable state in which asynchronously firing networks can abruptly transition into synchrony due to a brief perturbation. We further show that perturbations driving this transition could reasonably arisein vivobased on models of background excitatory synaptic activity in the cortex. Thus, these results propose a mechanism by which an inhibitory network can transition from incoherent to coherent dynamics in a fashion that may precipitate seizure as a downstream effect. Moreover, this mechanism specifically explains why inhibitory networks containing hyper-excitable interneurons are more vulnerable to this state change, and how such networks can undergo this transition without a permanent change in the drive to the system. This, in turn, potentially explains such networks’ increased vulnerability to seizure initiated by GABAergic activity.Author summaryFor decades, the study of epilepsy has focused on the hypothesis that over-excitation or dis-inhibition of pyramidal neurons underlies the transition from normal brain activity to seizure. However, a variety of recent experimental findings have implicated a sudden synchronous burst of activity amongst inhibitory interneurons in driving this transition. Given the counter-intuitive nature of these findings and the correspondingly novel hypothesis of seizure generation, the articulation of a feasible mechanism of action underlying this dynamic is of paramount importance for this theory’s viability. Here, we use computational techniques, particularly the concept of bistability in the context of dynamical systems, to propose a mechanism for the necessary first step in such a process: the sudden synchronization of a network of inhibitory interneurons. This is the first detailed proposal of a computational mechanism explaining any aspect of this hypothesis of which we are aware. By articulating a mechanism that not only underlies this transition, but does so in a fashion explaining why ictogenic networks might be more prone to this behavior, we provide critical support for this novel hypothesis of seizure generation and potential insight into the larger question of why individuals with epilepsy are particularly vulnerable to seizure.

Theta oscillations are essential for learning and memory, and their generation requires GABAergic interneurons. To better understand how theta is generated, we explored how parvalbumin (PV) and somatostatin (SOM) interneurons in CA1 stratum oriens/alveus fire during hippocampal theta and investigated synaptic mechanisms underlying their behavior. Combining the use of transgenic mice to visually identify PV and SOM interneurons and the intact hippocampal preparation that can generate theta oscillations in vitro without cholinergic agonists, we performed simultaneous field and whole-cell recordings. We found that PV interneurons uniformly fire strongly phase-locked to theta, whereas SOM neurons are more heterogeneous with only a proportion of cells displaying tight phase-locking. Differences in phase-locking strength could be explained by disparity in excitatory inputs received; PV neurons received significantly larger EPSCs compared with SOM neurons, and the degree of phase-locking in SOM neurons was significantly correlated with the size of EPSCs. In contrast, IPSC amplitude did not differ between cell types. We determined that the local CA1 rhythm plays a more dominant role in driving CA1 interneuron firing than afferent inputs from the CA3. Last, we show that PV and strongly phase-locked SOM neurons fire near the peak of CA1 theta, under the tight control of excitatory inputs that arise at a specific phase of each theta cycle. These results reveal a fundamental mechanism of neuronal phase-locking and highlight an important role of excitation from the local network in governing firing behavior during rhythmic network states. SIGNIFICANCE STATEMENT Rhythmic activity in the theta range (3–12 Hz) is important for proper functioning of the hippocampus, a brain area essential for learning and memory. To understand how theta rhythm is generated, we investigated how two types of inhibitory neurons, those that express parvalbumin and somatostatin, fire action potentials during theta in an in vitro preparation of the mouse hippocampus. We found that the amount of excitatory input they receive from the local network determines how closely their spikes follow the network theta rhythm. Our findings reveal an important role of local excitatory input in driving inhibitory neuron firing during rhythmic states and may have implications for diseases, such as epilepsy and Alzheimer9s disease, which affect the hippocampus and related areas.

The original publication has been updated. A typographical error has been corrected and references have been updated.

Determining the biological details and mechanisms that are essential for the generation of population rhythms in the mammalian brain is a challenging problem. This problem cannot be addressed either by experimental or computational studies in isolation. Here we show that computational models that are carefully linked with experiment provide insight into this problem. Using the experimental context of a whole hippocampus preparation in vitro that spontaneously expresses theta frequency (3---12 Hz) population bursts in the CA1 region, we create excitatory network models to examine whether cellular adaptation bursting mechanisms could critically contribute to the generation of this rhythm. We use biologically-based cellular models of CA1 pyramidal cells and network sizes and connectivities that correspond to the experimental context. By expanding our mean field analyses to networks with heterogeneity and non all-to-all coupling, we allow closer correspondence with experiment, and use these analyses to greatly extend the range of parameter values that are explored. We find that our model excitatory networks can produce theta frequency population bursts in a robust fashion.Thus, even though our networks are limited by not including inhibition at present, our results indicate that cellular adaptation in pyramidal cells could be an important aspect for the occurrence of theta frequency population bursting in the hippocampus. These models serve as a starting framework for the inclusion of inhibitory cells and for the consideration of additional experimental features not captured in our present network models.

Summary GABAergic interneurons play important roles in cortical circuit development. However, there are multiple populations of interneurons and their respective developmental contributions remain poorly explored. Neuregulin 1 ( NRG1 ) and its interneuron-specific receptor ERBB4 are critical genes for interneuron maturation. Using a conditional ErbB4 deletion, we tested the role of vasoactive intestinal peptide (VIP)-expressing interneurons in the postnatal maturation of cortical circuits in vivo . ErbB4 removal from VIP interneurons during development leads to changes in their activity, along with severe dysregulation of cortical temporal organization and state dependence. These alterations emerge during adolescence, and mature animals in which VIP interneurons lack ErbB4 exhibit reduced cortical responses to sensory stimuli and impaired sensory learning. Our data support a key role for VIP interneurons in cortical circuit development and suggest a possible contribution to pathophysiology in neurodevelopmental disorders. These findings provide a new perspective on the role of GABAergic interneuron diversity in cortical development. Video Abstract

Scientists have observed local field potential theta rhythms (3–12 Hz) in the hippocampus for decades, but understanding the mechanisms underlying their generation is complicated by their diversity in pharmacological and frequency profiles. In addition, interactions with other brain structures and oscillatory drives to the hippocampus during distinct brain states has made it difficult to identify hippocampus-specific properties directly involved in theta generation. To overcome this, we develop cellular-based network models using a whole hippocampus in vitro preparation that spontaneously generates theta rhythms. Building on theoretical and computational analyses, we find that spike frequency adaptation and postinhibitory rebound constitute a basis for theta generation in large, minimally connected CA1 pyramidal (PYR) cell network models with fast-firing parvalbumin-positive (PV+) inhibitory cells. Sparse firing of PYR cells and large excitatory currents onto PV+ cells are present as in experiments. The particular theta frequency is more controlled by PYR-to-PV+ cell interactions rather than PV+-to-PYR cell interactions. We identify two scenarios by which theta rhythms can emerge, and they can be differentiated by the ratio of excitatory to inhibitory currents to PV+ cells, but not to PYR cells. Only one of the scenarios is consistent with data from the whole hippocampus preparation, which leads to the prediction that the connection probability from PV+ to PYR cells needs to be larger than from PYR to PV+ cells. Our models can serve as a platform on which to build and develop an understanding of in vivo theta generation.

Scientists have observed theta rhythms (3–12 Hz) in the hippocampus for decades, but we do not have a clear understanding of how they are generated. This is largely due to the complex, multi-scale and nonlinear nature of the brain. To obtain insight into mechanisms underlying the generation of theta rhythms, we develop cellular-based network models of the hippocampus based on a whole hippocampus in vitro preparation that spontaneously generates theta rhythms. Building on theoretical and computational analyses, we find that spike frequency adaptation and post-inhibitory rebound constitute a basis for theta generation in large, minimally connected CA1 pyramidal (PYR) cell network models with fast-firing parvalbumin-positive (PV+) inhibitory cells. The particular theta frequency is more controlled by PYR to PV+ cell interactions rather than PV+ to PYR cell ones. We identify two scenarios by which theta rhythms can emerge and they can be differentiated by the ratio of excitatory to inhibitory currents to PV+ cells, but not to PYR cells. Only one of the scenarios is consistent with data from the whole hippocampus preparation, which leads to the prediction that the connection probability from PV+ to PYR cells needs to be larger than from PYR to PV+ cells. Our models can serve as a platform on which to build and develop an understanding of in vivo theta generation, and of microcircuit dynamics in the hippocampus.SignificanceBrain rhythms have been linked to cognition and are disrupted in disease. This makes it essential to understand mechanisms underlying their generation. Theory and mathematical models help provide an understanding and generate hypotheses. Together with experiment they contribute a framework to dissect the cellular contributions to network activity. However, models are inherently biological approximations, and thus the specific experimental and theoretical context upon which they are built will shape their output. If the approximations and contexts are not taken into account, particularly when using previously constructed models, misinterpretations can arise. Here, we use both theory and microcircuit models derived from a specific experimental context to provide insight into cellular-based mechanisms involved in theta rhythm generation in the hippocampus.

Scientists have observed local field potential theta rhythms (3–12 Hz) in the hippocampus for decades, but understanding the mechanisms underlying their generation is complicated by their diversity in pharmacological and frequency profiles. In addition, interactions with other brain structures and oscillatory drives to the hippocampus during distinct brain states has made it difficult to identify hippocampus-specific properties directly involved in theta generation. To overcome this, we develop cellular-based network models using a whole hippocampusin vitropreparation that spontaneously generates theta rhythms. Building on theoretical and computational analyses, we find that spike frequency adaptation and postinhibitory rebound constitute a basis for theta generation in large, minimally connected CA1 pyramidal (PYR) cell network models with fast-firing parvalbumin-positive (PV+) inhibitory cells. Sparse firing of PYR cells and large excitatory currents onto PV+cells are present as in experiments. The particular theta frequency is more controlled by PYR-to-PV+cell interactions rather than PV+-to-PYR cell interactions. We identify two scenarios by which theta rhythms can emerge, and they can be differentiated by the ratio of excitatory to inhibitory currents to PV+cells, but not to PYR cells. Only one of the scenarios is consistent with data from the whole hippocampus preparation, which leads to the prediction that the connection probability from PV+to PYR cells needs to be larger than from PYR to PV+cells. Our models can serve as a platform on which to build and develop an understanding ofin vivotheta generation.

There is an undisputed need and requirement for theoretical and computational studies in Neuroscience today. Furthermore, it is clear that oscillatory dynamical output from brain networks is representative of various behavioural states, and it is becoming clear that one could consider these outputs as measures of normal and pathological brain states. Although mathematical modeling of oscillatory dynamics in the context of neurological disease exists, it is a highly challenging endeavour because of the many levels of organization in the nervous system. This challenge is coupled with the increasing knowledge of cellular specificity and network dysfunction that is associated with disease. Recently, whole hippocampus in vitro preparations from control animals have been shown to spontaneously express oscillatory activities. In addition, when using preparations derived from animal models of disease, these activities show particular alterations. These preparations present an opportunity to address challenges involved with using models to gain insight because of easier access to simultaneous cellular and network measurements, and pharmacological modulations. We propose that by developing and using models with direct links to experiment at multiple levels, which at least include cellular and microcircuit, a cycling can be set up and used to help us determine critical mechanisms underlying neurological disease. We illustrate our proposal using our previously developed inhibitory network models in the context of these whole hippocampus preparations and show the importance of having direct links at multiple levels.

From several experimental and modeling studies, it has become apparent that networks of parvalbumin-positive (PV+), fast-firing interneurons play an essential role in generating population gamma rhythms [1]. Many features of these perisomatically-inhibiting PV+ cell networks influence the generation of gamma oscillations, a number of which have been explored in network models [2,3]. These include the synaptic delay, the decay time constant of the postsynaptic conductance, and whether the reversal potential is hyperpolarizing or shunting. While it is clear that all these features affect whether and how much coherence occurs at gamma frequencies, it is much less clear which features (i.e. parameters and parameter balances) may be essential in various biological contexts. To address this, we used our previously constructed network model of hippocampal CA1 PV+ cells [4]. Network size, connectivity, and cellular components would all be expected to affect the network dynamics. Thus, experimental data from an intact hippocampus in vitro was used to obtain a clear biological context, and importantly, the amount of input that these cells receive during ongoing theta population activities was estimated. Using these cellular components and input characteristics, we found that coherent gamma oscillations could emerge within experimental constraints for particular parameter balances. Here we investigate the effect of appropriately sized and connected networks. Each cell in the network received excitatory input (Iapplied ), which was heterogeneous across cells, and synaptic inhibition from presynaptic interneurons, which had a particular maximal conductance value (gPV). We systematically varied gPV and Iapplied within the experimentally determined ranges, and determined the coherence of the network model population firing and the network frequency for each combination of these values. For each of these network simulation sets, we also varied the connectivity probability as well as network size to explore how these factors affect the network’s ability to produce coherent network firing. We find that when our connection probability is similar to what has been found experimentally, our networks exhibit a sharp transition from random firing to network coherence with only a small change in Iapplied. However, as connectivity in the network is increased beyond experimentally estimated values, this sharp transition disappears. Instead, a larger window of coherence is achieved, but with a smooth transition from random to coherent firing. Similarly, decreasing the network size increases the window of coherent firing, and while these sharp transitions remain for changing network sizes, the parameter regimes in which they occur changes significantly when the network size becomes much smaller than experimental estimates. Our work indicates that CA1 fastspiking PV+ networks are capable of producing gamma population rhythms, and that perturbation in and out of coherent states can occur abruptly given the sharp transitions obtained in our network simulations. Since our simulations approximate a biological context, it may be that gating in and out of coherence is a design property to allow generation of theta/gamma network oscillations.

Our PV+ interneurons are represented with an Izhikevich-type model [2], and involve parameter values that are designed to approximate the cell’ si ntrinsic properties. To determine these parameters, spike characteristics and passive properties were extracted from wholecell patch clamp recordings of PV+ interneurons in the CA1 region of an intact hippocampal preparation in vitro. Our network model is composed of these individual PV+ cell models, and the network size, architecture, and synaptic properties are chosen to be consistent with those found in the literature. Recordings during emergent network oscillations [3] provided us with information about realistic firing rates and synaptic activity of PV+ interneurons. These firing rates, used in combination with the cell’s intrinsic frequency-current profile, provided physiological constraints on the amount of synaptic current the PV+ cells receive during these spontaneous network oscillations. Under voltage clamp, excitatory post-synaptic current peaks are used in our model as an upper bound on the range of synaptic input. We used this network model to determine whether coherent rhythms could be produced within experimental constraints. Our model produced intrinsic properties and spiking behaviors which approximated the experimentally determined membrane capacitance, resting membrane potential, threshold potential, spike width, spike peak potential, peak after-hyperpolarizing potential, and amount of adaptation. Model parameters were determined such that the slope of the model’s frequency-current profile and the model rheobase current were within the range of our experimental data. As such, we have produced a network model of PV+ interneurons that has direct links to cellular characteristics with model parameters that have clear biological interpretations. In addition, network simulations of our PV+ interneuron model produced coherent gamma output. Since the firing properties and network architecture of PV+ interneurons puts them in an ideal position to influence network activity, this cell type will likely remain a focus of experimentalists and modelers alike. A model such as ours, with clear links to biology, may be used as a platform to investigate the role of these fast-firing PV+ interneurons in network oscillations and behaviour.