GABAergic contributions to gating, timing, and phase precession of hippocampal neuronal activity during theta oscillations

LTP is Absent in the CA1 Region of the Hippocampus of Male and Female Rett Syndrome Mouse Models

Rett syndrome (RTT) is a debilitating neurodevelopmental disorder caused by mutations in the X-linked methyl-CpG-binding protein 2 (MeCP2) gene, resulting in severe deficits in learning and memory. Alterations in synaptic plasticity have been reported in RTT, however most electrophysiological studies have been performed in male mice only, despite the fact that RTT is primarily found in females. In addition, most studies have focused on excitation, despite the emerging evidence for the important role of inhibition in learning and memory. Here, we performed an electrophysiological characterization in the CA1 region of the hippocampus in both males and females of RTT mouse models with a focus on neurogliaform (NGF) interneurons, given that they are the most abundant dendrite-targeting interneuron subtype in the hippocampus. We found that theta-burst stimulation (TBS) failed to induce long-term potentiation (LTP) in either pyramidal neurons or NGF interneurons in male or female RTT mice, with no apparent changes in short-term plasticity (STP). This failure to induce LTP was accompanied by excitation/inhibition (E/I) imbalances and altered excitability, in a sex- and cell-type specific manner. Specifically, NGF interneurons of male RTT mice displayed increased intrinsic excitability, a depolarized resting membrane potential, and decreased E/I balance, while in female RTT mice, the resting membrane potential was depolarized. Understanding the role of NGF interneurons in RTT animal models is crucial for developing targeted treatments to improve cognition in individuals with this disorder.

Phase relations of interneuronal activity relative to theta rhythm

The theta rhythm plays a crucial role in synchronizing neural activity during attention and memory processes. However, the mechanisms behind the formation of neural activity during theta rhythm generation remain unknown. To address this, we propose a mathematical model that explains the distribution of interneurons in the CA1 field during the theta rhythm phase. Our model consists of a network of seven types of interneurons in the CA1 field that receive inputs from the CA3 field, entorhinal cortex, and local pyramidal neurons in the CA1 field. By adjusting the parameters of the connections in the model. We demonstrate that it is possible to replicate the experimentally observed phase relations between interneurons and the theta rhythm. Our model predicts that populations of interneurons receive unimodal excitation and inhibition with coinciding peaks, and that excitation dominates to determine the firing dynamics of interneurons.

Manipulating single-unit theta phase-locking with PhaSER: An open-source tool for real-time phase estimation and manipulation

The precise timing of neuronal spiking relative to the brain's endogenous oscillations (i.e., phase-locking or spike-phase coupling) has long been hypothesized to coordinate cognitive processes and maintain excitatory-inhibitory homeostasis. Indeed, disruptions in theta phase-locking have been described in models of neurological diseases with associated cognitive deficits and seizures, such as Alzheimer's disease, temporal lobe epilepsy, and autism spectrum disorders. However, due to technical limitations, determining if phase-locking causally contributes to these disease phenotypes has not been possible until recently. To fill this gap and allow for the flexible manipulation of single-unit phase-locking to on-going endogenous oscillations, we developed PhaSER, an open-source tool that allows for phase-specific manipulations. PhaSER can deliver optogenetic stimulation at defined phases of theta in order to shift the preferred firing phase of neurons relative to theta in real-time. Here, we describe and validate this tool in a subpopulation of inhibitory neurons that express somatostatin (SOM) in the CA1 and dentate gyrus (DG) regions of the dorsal hippocampus. We show that PhaSER is able to accurately deliver a photo-manipulation that activates opsin+ SOM neurons at specified phases of theta in real-time in awake, behaving mice. Further, we show that this manipulation is sufficient to alter the preferred firing phase of opsin+ SOM neurons without altering the referenced theta power or phase. All software and hardware requirements to implement real-time phase manipulations during behavior are available online (https://github.com/ShumanLab/PhaSER).

Frequency matters: how changes in hippocampal theta frequency can influence temporal coding, anxiety-reduction, and memory

Hippocampal theta frequency is a somewhat neglected topic relative to theta power, phase, coherence, and cross-frequency coupling. Accordingly, here we review and present new data on variation in hippocampal theta frequency, focusing on functional associations (temporal coding, anxiety reduction, learning, and memory). Taking the rodent hippocampal theta frequency to running-speed relationship as a model, we identify two doubly-dissociable frequency components: (a) the slope component of the theta frequency-to-stimulus-rate relationship ("theta slope"); and (b) its y-intercept frequency ("theta intercept"). We identify three tonic determinants of hippocampal theta frequency. (1) Hotter temperatures increase theta frequency, potentially consistent with time intervals being judged as shorter when hot. Initial evidence suggests this occurs via the "theta slope" component. (2) Anxiolytic drugs with widely-different post-synaptic and pre-synaptic primary targets share the effect of reducing the "theta intercept" component, supporting notions of a final common pathway in anxiety reduction involving the hippocampus. (3) Novelty reliably decreases, and familiarity increases, theta frequency, acting upon the "theta slope" component. The reliability of this latter finding, and the special status of novelty for learning, prompts us to propose a Novelty Elicits Slowing of Theta frequency (NEST) hypothesis, involving the following elements: (1) Theta frequency slowing in the hippocampal formation is a generalised response to novelty of different types and modalities; (2) Novelty-elicited theta slowing is a hippocampal-formation-wide adaptive response functioning to accommodate the additional need for learning entailed by novelty; (3) Lengthening the theta cycle enhances associativity; (4) Even part-cycle lengthening may boost associativity; and (5) Artificial theta stimulation aimed at enhancing learning should employ low-end theta frequencies.

Lateral entorhinal cortex inputs modulate hippocampal dendritic excitability by recruiting a local disinhibitory microcircuit

The lateral entorhinal cortex (LEC) provides multisensory information to the hippocampus, directly to the distal dendrites of CA1 pyramidal neurons. LEC neurons perform important functions for episodic memory processing, coding for contextually salient elements of an environment or experience. However, we know little about the functional circuit interactions between the LEC and the hippocampus. We combine functional circuit mapping and computational modeling to examine how long-range glutamatergic LEC projections modulate compartment-specific excitation-inhibition dynamics in hippocampal area CA1. We demonstrate that glutamatergic LEC inputs can drive local dendritic spikes in CA1 pyramidal neurons, aided by the recruitment of a disinhibitory VIP interneuron microcircuit. Our circuit mapping and modeling further reveal that LEC inputs also recruit CCK interneurons that may act as strong suppressors of dendritic spikes. These results highlight a cortically driven GABAergic microcircuit mechanism that gates nonlinear dendritic computations, which may support compartment-specific coding of multisensory contextual features within the hippocampus.

Homogeneous inhibition is optimal for the phase precession of place cells in the CA1 field

Place cells are hippocampal neurons encoding the position of an animal in space. Studies of place cells are essential to understanding the processing of information by neural networks of the brain. An important characteristic of place cell spike trains is phase precession. When an animal is running through the place field, the discharges of the place cells shift from the ascending phase of the theta rhythm through the minimum to the descending phase. The role of excitatory inputs to pyramidal neurons along the Schaffer collaterals and the perforant pathway in phase precession is described, but the role of local interneurons is poorly understood. Our goal is estimating of the contribution of field CA1 interneurons to the phase precession of place cells using mathematical methods. The CA1 field is chosen because it provides the largest set of experimental data required to build and verify the model. Our simulations discover optimal parameters of the excitatory and inhibitory inputs to the pyramidal neuron so that it generates a spike train with the effect of phase precession. The uniform inhibition of pyramidal neurons best explains the effect of phase precession. Among interneurons, axo-axonal neurons make the greatest contribution to the inhibition of pyramidal cells.

A model of working memory for encoding multiple items and ordered sequences exploiting the theta-gamma code

Recent experimental evidence suggests that oscillatory activity plays a pivotal role in the maintenance of information in working memory, both in rodents and humans. In particular, cross-frequency coupling between theta and gamma oscillations has been suggested as a core mechanism for multi-item memory. The aim of this work is to present an original neural network model, based on oscillating neural masses, to investigate mechanisms at the basis of working memory in different conditions. We show that this model, with different synapse values, can be used to address different problems, such as the reconstruction of an item from partial information, the maintenance of multiple items simultaneously in memory, without any sequential order, and the reconstruction of an ordered sequence starting from an initial cue. The model consists of four interconnected layers; synapses are trained using Hebbian and anti-Hebbian mechanisms, in order to synchronize features in the same items, and desynchronize features in different items. Simulations show that the trained network is able to desynchronize up to nine items without a fixed order using the gamma rhythm. Moreover, the network can replicate a sequence of items using a gamma rhythm nested inside a theta rhythm. The reduction in some parameters, mainly concerning the strength of GABAergic synapses, induce memory alterations which mimic neurological deficits. Finally, the network, isolated from the external environment (“imagination phase”) and stimulated with high uniform noise, can randomly recover sequences previously learned, and link them together by exploiting the similarity among items.

Complementary roles of differential medial entorhinal cortex inputs to the hippocampus for the formation and integration of temporal and contextual memory (Systems Neuroscience)

In humans and rodents, the entorhinal cortical (EC)-hippocampal (HPC) circuit is crucial for the formation and recall of memory, preserving both spatial information and temporal information about the occurrence of past events. Both modeling and experimental studies have revealed circuits within this network that play crucial roles in encoding space and context. However, our understanding about the time-related aspects of memory is just beginning to be understood. In this review, we first describe updates regarding recent anatomical discoveries for the EC-HPC network, as several important neural circuits critical for memory formation have been discovered by newly developed neural tracing technologies. Second, we examine the complementary roles of multiple medial entorhinal cortical inputs, including newly discovered circuits, into the hippocampus for the temporal and spatial aspects of memory. Finally, we will discuss how temporal and contextual memory information is integrated in HPC cornu ammonis 1 cells. We provide new insights into the neural circuit mechanisms for anatomical and functional segregation and integration of the temporal and spatial aspects of memory encoding in the EC-HPC networks.

Spiking Neural Networks and Hippocampal Function: A Web-Accessible Survey of Simulations, Modeling Methods, and Underlying Theories

Computational modeling has contributed to hippocampal research in a wide variety of ways and through a large diversity of approaches, reflecting the many advanced cognitive roles of this brain region. The intensively studied neuron type circuitry of the hippocampus is a particularly conducive substrate for spiking neural models. Here we present an online knowledge base of spiking neural network simulations of hippocampal functions. First, we overview theories involving the hippocampal formation in subjects such as spatial representation, learning, and memory. Then we describe an original literature mining process to organize published reports in various key aspects, including: (i) subject area (e.g., navigation, pattern completion, epilepsy); (ii) level of modeling detail (Hodgkin-Huxley, integrate-and-fire, etc.); and (iii) theoretical framework (attractor dynamics, oscillatory interference, self-organizing maps, and others). Moreover, every peer-reviewed publication is also annotated to indicate the specific neuron types represented in the network simulation, establishing a direct link with the Hippocampome.org portal. The web interface of the knowledge base enables dynamic content browsing and advanced searches, and consistently presents evidence supporting every annotation. Moreover, users are given access to several types of statistical reports about the collection, a selection of which is summarized in this paper. This open access resource thus provides an interactive platform to survey spiking neural network models of hippocampal functions, compare available computational methods, and foster ideas for suitable new directions of research.

Quantitative investigation of memory recall performance of a computational microcircuit model of the hippocampus

Memory, the process of encoding, storing, and maintaining information over time to influence future actions, is very important in our lives. Losing it, it comes with a great cost. Deciphering the biophysical mechanisms leading to recall improvement should thus be of outmost importance. In this study, we embarked on the quest to improve computationally the recall performance of a bio-inspired microcircuit model of the mammalian hippocampus, a brain region responsible for the storage and recall of short-term declarative memories. The model consisted of excitatory and inhibitory cells. The cell properties followed closely what is currently known from the experimental neurosciences. Cells’ firing was timed to a theta oscillation paced by two distinct neuronal populations exhibiting highly regular bursting activity, one tightly coupled to the trough and the other to the peak of theta. An excitatory input provided to excitatory cells context and timing information for retrieval of previously stored memory patterns. Inhibition to excitatory cells acted as a non-specific global threshold machine that removed spurious activity during recall. To systematically evaluate the model’s recall performance against stored patterns, pattern overlap, network size, and active cells per pattern, we selectively modulated feedforward and feedback excitatory and inhibitory pathways targeting specific excitatory and inhibitory cells. Of the different model variations (modulated pathways) tested, ‘model 1’ recall quality was excellent across all conditions. ‘Model 2’ recall was the worst. The number of ‘active cells’ representing a memory pattern was the determining factor in improving the model’s recall performance regardless of the number of stored patterns and overlap between them. As ‘active cells per pattern’ decreased, the model’s memory capacity increased, interference effects between stored patterns decreased, and recall quality improved.

Septohippocampal transmission from parvalbumin-positive neurons features rapid recovery from synaptic depression

Parvalbumin-containing projection neurons of the medial-septum-diagonal band of Broca ([Formula: see text]) are essential for hippocampal rhythms and learning operations yet are poorly understood at cellular and synaptic levels. We combined electrophysiological, optogenetic, and modeling approaches to investigate [Formula: see text] neuronal properties. [Formula: see text] neurons had intrinsic membrane properties distinct from acetylcholine- and somatostatin-containing MS-DBB subtypes. Viral expression of the fast-kinetic channelrhodopsin ChETA-YFP elicited action potentials to brief (1-2 ms) 470 nm light pulses. To investigate [Formula: see text] transmission, light pulses at 5-50 Hz frequencies generated trains of inhibitory postsynaptic currents (IPSCs) in CA1 stratum oriens interneurons. Using a similar approach, optogenetic activation of local hippocampal PV ([Formula: see text]) neurons generated trains of [Formula: see text]-mediated IPSCs in CA1 pyramidal neurons. Both synapse types exhibited short-term depression (STD) of IPSCs. However, relative to [Formula: see text] synapses, [Formula: see text] synapses possessed lower initial release probability, transiently resisted STD at gamma (20-50 Hz) frequencies, and recovered more rapidly from synaptic depression. Experimentally-constrained mathematical synapse models explored mechanistic differences. Relative to the [Formula: see text] model, the [Formula: see text] model exhibited higher sensitivity to calcium accumulation, permitting a faster rate of calcium-dependent recovery from STD. In conclusion, resistance of [Formula: see text] synapses to STD during short gamma bursts enables robust long-range GABAergic transmission from MS-DBB to hippocampus.

Focal Dorsal Hippocampal Nav1.1 Knock Down Alters Place Cell Temporal Coordination and Spatial Behavior

Alterations in the voltage-gated sodium channel Nav.1.1 are implicated in various neurological disorders, including epilepsy, Alzheimer's disease, and autism spectrum disorders. Previous studies suggest that the reduction of Nav1.1 expression leads to a decrease of fast spiking activity in inhibitory neurons. Because interneurons (INs) play a critical role in the temporal organization of neuronal discharge, we hypothesize that Nav1.1 dysfunction will negatively impact neuronal coordination in vivo. Using shRNA interference, we induced a focal Nav1.1 knock-down (KD) in the dorsal region of the right hippocampus of adult rats. Focal, unilateral Nav1.1 KD decreases the performance in a spatial novelty recognition task and the firing rate in INs, but not in pyramidal cells. It reduced theta/gamma coupling of hippocampal oscillations and induced a shift in pyramidal cell theta phase preference. Nav1.1 KD degraded spatial accuracy and temporal coding properties of place cells, such as theta phase precession and compression of ongoing sequences. Aken together, these data demonstrate that a deficit in Nav1.1 alters the temporal coordination of neuronal firing in CA1 and impairs behaviors that rely on the integrity of this network. They highlight the potential contribution of local inhibition in neuronal coordination and its impact on behavior in pathological conditions.

The Unexplored Territory of Neural Models: Potential Guides for Exploring the Function of Metabotropic Neuromodulation

The space of possible neural models is enormous and under-explored. Single cell computational neuroscience models account for a range of dynamical properties of membrane potential, but typically do not address network function. In contrast, most models focused on network function address the dimensions of excitatory weight matrices and firing thresholds without addressing the complexities of metabotropic receptor effects on intrinsic properties. There are many under-explored dimensions of neural parameter space, and the field needs a framework for representing what has been explored and what has not. Possible frameworks include maps of parameter spaces, or efforts to categorize the fundamental elements and molecules of neural circuit function. Here we review dimensions that are under-explored in network models that include the metabotropic modulation of synaptic plasticity and presynaptic inhibition, spike frequency adaptation due to calcium-dependent potassium currents, and afterdepolarization due to calcium-sensitive non-specific cation currents and hyperpolarization activated cation currents. Neuroscience research should more effectively explore possible functional models incorporating under-explored dimensions of neural function.

A neural circuit model for a contextual association task inspired by recommender systems

Behavioral data shows that humans and animals have the capacity to learn rules of associations applied to specific examples, and generalize these rules to a broad variety of contexts. This article focuses on neural circuit mechanisms to perform a context-dependent association task that requires linking sensory stimuli to behavioral responses and generalizing to multiple other symmetrical contexts. The model uses neural gating units that regulate the pattern of physiological connectivity within the circuit. These neural gating units can be used in a learning framework that performs low-rank matrix factorization analogous to recommender systems, allowing generalization with high accuracy to a wide range of additional symmetrical contexts. The neural gating units are trained with a biologically inspired framework involving traces of Hebbian modification that are updated based on the correct behavioral output of the network. This modeling demonstrates potential neural mechanisms for learning context-dependent association rules and for the change in selectivity of neurophysiological responses in the hippocampus. The proposed computational model is evaluated using simulations of the learning process and the application of the model to new stimuli. Further, human subject behavioral experiments were performed and the results validate the key observation of a low-rank synaptic matrix structure linking stimuli to responses.

Hippocampal-Cortical Memory Trace Transfer and Reactivation Through Cell-Specific Stimulus and Spontaneous Background Noise

The hippocampus plays important roles in memory formation and retrieval through sharp-wave-ripples. Recent studies have shown that certain neuron populations in the prefrontal cortex (PFC) exhibit coordinated reactivations during awake ripple events. These experimental findings suggest that the awake ripple is an important biomarker, through which the hippocampus interacts with the neocortex to assist memory formation and retrieval. However, the computational mechanisms of this ripple based hippocampal-cortical coordination are still not clear due to the lack of unified models that include both the hippocampal and cortical networks. In this work, using a coupled biophysical model of both CA1 and PFC, we investigate possible mechanisms of hippocampal-cortical memory trace transfer and the conditions that assist reactivation of the transferred memory traces in the PFC. To validate our model, we first show that the local field potentials generated in the hippocampus and PFC exhibit ripple range activities that are consistent with the recent experimental studies. Then we demonstrate that during ripples, sequence replays can successfully transfer the information stored in the hippocampus to the PFC recurrent networks. We investigate possible mechanisms of memory retrieval in PFC networks. Our results suggest that the stored memory traces in the PFC network can be retrieved through two different mechanisms, namely the cell-specific input representing external stimuli and non-specific spontaneous background noise representing spontaneous memory recall events. Importantly, in both cases, the memory reactivation quality is robust to network connection loss. Finally, we find out that the quality of sequence reactivations is enhanced by both increased number of SWRs and an optimal background noise intensity, which tunes the excitability of neurons to a proper level. Our study presents a mechanistic explanation for the memory trace transfer from the hippocampus to neocortex through ripple coupling in awake states and reports two different mechanisms by which the stored memory traces can be successfully retrieved.

Memory Prosthesis: Is It Time for a Deep Neuromimetic Computing Approach?

Memory loss, one of the most dreaded afflictions of the human condition, presents considerable burden on the world's health care system and it is recognized as a major challenge in the elderly. There are only a few neuromodulation treatments for memory dysfunctions. Open loop deep brain stimulation is such a treatment for memory improvement, but with limited success and conflicting results. In recent years closed-loop neuroprosthesis systems able to simultaneously record signals during behavioral tasks and generate with the use of internal neural factors the precise timing of stimulation patterns are presented as attractive alternatives and show promise in memory enhancement and restoration. A few such strides have already been made in both animals and humans, but with limited insights into their mechanisms of action. Here, I discuss why a deep neuromimetic computing approach linking multiple levels of description, mimicking the dynamics of brain circuits, interfaced with recording and stimulating electrodes could enhance the performance of current memory prosthesis systems, shed light into the neurobiology of learning and memory and accelerate the progress of memory prosthesis research. I propose what the necessary components (nodes, structure, connectivity, learning rules, and physiological responses) of such a deep neuromimetic model should be and what type of data are required to train/test its performance, so it can be used as a true substitute of damaged brain areas capable of restoring/enhancing their missing memory formation capabilities. Considerations to neural circuit targeting, tissue interfacing, electrode placement/implantation, and multi-network interactions in complex cognition are also provided.

Hippocampal Sequences During Exploration: Mechanisms and Functions

Although the hippocampus plays a critical role in spatial and episodic memories, the mechanisms underlying memory formation, stabilization, and recall for adaptive behavior remain relatively unknown. During exploration, within single cycles of the ongoing theta rhythm that dominates hippocampal local field potentials, place cells form precisely ordered sequences of activity. These neural sequences result from the integration of both external inputs conveying sensory-motor information, and intrinsic network dynamics possibly related to memory processes. Their endogenous replay during subsequent sleep is critical for memory consolidation. The present review discusses possible mechanisms and functions of hippocampal theta sequences during exploration. We present several lines of evidence suggesting that these neural sequences play a key role in information processing and support the formation of initial memory traces, and discuss potential functional distinctions between neural sequences emerging during theta vs. awake sharp-wave ripples.

Computer Modeling of Alzheimer's Disease-Simulations of Synaptic Plasticity and Memory in the CA3-CA1 Hippocampal Formation Microcircuit

This paper aims to present computer modeling of synaptic plasticity and memory in the CA3-CA1 hippocampal formation microcircuit. The computer simulations showed a comparison of a pathological model in which Alzheimer's disease (AD) was simulated by synaptic degradation in the hippocampus and control model (healthy) of CA3-CA1 networks with modification of weights for the memory. There were statistically higher spike values of both CA1 and CA3 pyramidal cells in the control model than in the pathological model (p = 0.0042 for CA1 and p = 0.0033 for CA3). A similar outcome was achieved for frequency (p = 0.0002 for CA1 and p = 0.0001 for CA3). The entropy of pyramidal cells of the healthy CA3 network seemed to be significantly higher than that of AD (p = 0.0304). We need to study a lot of physiological parameters and their combinations of the CA3-CA1 hippocampal formation microcircuit to understand AD. High statistically correlations were obtained between memory, spikes and synaptic deletion in both CA1 and CA3 cells.

A network model of behavioural performance in a rule learning task

Humans demonstrate differences in performance on cognitive rule learning tasks which could involve differences in properties of neural circuits. An example model is presented to show how gating of the spread of neural activity could underlie rule learning and the generalization of rules to previously unseen stimuli. This model uses the activity of gating units to regulate the pattern of connectivity between neurons responding to sensory input and subsequent gating units or output units. This model allows analysis of network parameters that could contribute to differences in cognitive rule learning. These network parameters include differences in the parameters of synaptic modification and presynaptic inhibition of synaptic transmission that could be regulated by neuromodulatory influences on neural circuits. Neuromodulatory receptors play an important role in cognitive function, as demonstrated by the fact that drugs that block cholinergic muscarinic receptors can cause cognitive impairments. In discussions of the links between neuromodulatory systems and biologically based traits, the issue of mechanisms through which these linkages are realized is often missing. This model demonstrates potential roles of neural circuit parameters regulated by acetylcholine in learning context-dependent rules, and demonstrates the potential contribution of variation in neural circuit properties and neuromodulatory function to individual differences in cognitive function.This article is part of the theme issue 'Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'.

γ-Aminobutyric Acid Type A Receptor Potentiation Inhibits Learning in a Computational Network Model

BACKGROUND

Propofol produces memory impairment at concentrations well below those abolishing consciousness. Episodic memory, mediated by the hippocampus, is most sensitive. Two potentially overlapping scenarios may explain how γ-aminobutyric acid receptor type A (GABAA) potentiation by propofol disrupts episodic memory-the first mediated by shifting the balance from excitation to inhibition while the second involves disruption of rhythmic oscillations. We use a hippocampal network model to explore these scenarios. The basis for these experiments is the proposal that the brain represents memories as groups of anatomically dispersed strongly connected neurons.

METHODS

A neuronal network with connections modified by synaptic plasticity was exposed to patterned stimuli, after which spiking output demonstrated evidence of stimulus-related neuronal group development analogous to memory formation. The effect of GABAA potentiation on this memory model was studied in 100 unique networks.

RESULTS

GABAA potentiation consistent with moderate propofol effects reduced neuronal group size formed in response to a patterned stimulus by around 70%. Concurrently, accuracy of a Bayesian classifier in identifying learned patterns in the network output was reduced. Greater potentiation led to near total failure of group formation. Theta rhythm variations had no effect on group size or classifier accuracy.

CONCLUSIONS

Memory formation is widely thought to depend on changes in neuronal connection strengths during learning that enable neuronal groups to respond with greater facility to familiar stimuli. This experiment suggests the ability to form such groups is sensitive to alteration in the balance between excitation and inhibition such as that resulting from administration of a γ-aminobutyric acid-mediated anesthetic agent.

Adeno-associated viral overexpression of neuroligin 2 in the mouse hippocampus enhances GABAergic synapses and impairs hippocampal-dependent behaviors

The cell adhesion molecule neuroligin2 (NLGN2) regulates GABAergic synapse development, but its role in neural circuit function in the adult hippocampus is unclear. We investigated GABAergic synapses and hippocampus-dependent behaviors following viral-vector-mediated overexpression of NLGN2. Transducing hippocampal neurons with AAV-NLGN2 increased neuronal expression of NLGN2 and membrane localization of GABAergic postsynaptic proteins gephyrin and GABA_ARγ2, and presynaptic vesicular GABA transporter protein (VGAT) suggesting trans-synaptic enhancement of GABAergic synapses. In contrast, glutamatergic postsynaptic density protein-95 (PSD-95) and presynaptic vesicular glutamate transporter (VGLUT) protein were unaltered. Moreover, AAV-NLGN2 significantly increased parvalbumin immunoreactive (PV⁺) synaptic boutons co-localized with postsynaptic gephyrin⁺ puncta. Furthermore, these changes were demonstrated to lead to cognitive impairments as shown in a battery of hippocampal-dependent mnemonic tasks and social behaviors.

Dichotomous Dynamics in E-I Networks with Strongly and Weakly Intra-connected Inhibitory Neurons

The interconnectivity between excitatory and inhibitory neural networks informs mechanisms by which rhythmic bursts of excitatory activity can be produced in the brain. One such mechanism, Pyramidal Interneuron Network Gamma (PING), relies primarily upon reciprocal connectivity between the excitatory and inhibitory networks, while also including intra-connectivity of inhibitory cells. The causal relationship between excitatory activity and the subsequent burst of inhibitory activity is of paramount importance to the mechanism and has been well studied. However, the role of the intra-connectivity of the inhibitory network, while important for PING, has not been studied in detail, as most analyses of PING simply assume that inhibitory intra-connectivity is strong enough to suppress subsequent firing following the initial inhibitory burst. In this paper we investigate the role that the strength of inhibitory intra-connectivity plays in determining the dynamics of PING-style networks. We show that networks with weak inhibitory intra-connectivity exhibit variations in burst dynamics of both the excitatory and inhibitory cells that are not obtained with strong inhibitory intra-connectivity. Networks with weak inhibitory intra-connectivity exhibit excitatory rhythmic bursts with weak excitatory-to-inhibitory synapses for which classical PING networks would show no rhythmic activity. Additionally, variations in dynamics of these networks as the excitatory-to-inhibitory synaptic weight increases illustrates the important role that consistent pattern formation in the inhibitory cells serves in maintaining organized and periodic excitatory bursts. Finally, motivated by these results and the known diversity of interneurons, we show that a PING-style network with two inhibitory subnetworks, one strongly intra-connected and one weakly intra-connected, exhibits organized and periodic excitatory activity over a larger parameter regime than networks with a homogeneous inhibitory population. Taken together, these results serve to better articulate the role of inhibitory intra-connectivity in generating PING-like rhythms, while also revealing how heterogeneity amongst inhibitory synapses might make such rhythms more robust to a variety of network parameters.

Inhibitory suppression of heterogeneously tuned excitation enhances spatial coding in CA1 place cells

Place cells in the CA1 region of the hippocampus express location-specific firing despite receiving a steady barrage of heterogeneously tuned excitatory inputs that should compromise output dynamic range and timing. We examined the role of synaptic inhibition in countering the deleterious effects of off-target excitation. Intracellular recordings in behaving mice demonstrate that bimodal excitation drives place cells, while unimodal excitation drives weaker or no spatial tuning in interneurons. Optogenetic hyperpolarization of interneurons had spatially uniform effects on place cell membrane potential dynamics, substantially reducing spatial selectivity. These data and a computational model suggest that spatially uniform inhibitory conductance enhances rate coding in place cells by suppressing out-of-field excitation and by limiting dendritic amplification. Similarly, we observed that inhibitory suppression of phasic noise generated by out-of-field excitation enhances temporal coding by expanding the range of theta phase precession. Thus, spatially uniform inhibition allows proficient and flexible coding in hippocampal CA1 by suppressing heterogeneously tuned excitation.

Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit

The hippocampal theta rhythm plays important roles in information processing; however, the mechanisms of its generation are not well understood. We developed a data-driven, supercomputer-based, full-scale (1:1) model of the rodent CA1 area and studied its interneurons during theta oscillations. Theta rhythm with phase-locked gamma oscillations and phase-preferential discharges of distinct interneuronal types spontaneously emerged from the isolated CA1 circuit without rhythmic inputs. Perturbation experiments identified parvalbumin-expressing interneurons and neurogliaform cells, as well as interneuronal diversity itself, as important factors in theta generation. These simulations reveal new insights into the spatiotemporal organization of the CA1 circuit during theta oscillations.

Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit

The hippocampal theta rhythm plays important roles in information processing; however, the mechanisms of its generation are not well understood. We developed a data-driven, supercomputer-based, full-scale (1:1) model of the rodent CA1 area and studied its interneurons during theta oscillations. Theta rhythm with phase-locked gamma oscillations and phase-preferential discharges of distinct interneuronal types spontaneously emerged from the isolated CA1 circuit without rhythmic inputs. Perturbation experiments identified parvalbumin-expressing interneurons and neurogliaform cells, as well as interneuronal diversity itself, as important factors in theta generation. These simulations reveal new insights into the spatiotemporal organization of the CA1 circuit during theta oscillations.

DOI:http://dx.doi.org/10.7554/eLife.18566.001

Intrinsic Cellular Properties and Connectivity Density Determine Variable Clustering Patterns in Randomly Connected Inhibitory Neural Networks

The plethora of inhibitory interneurons in the hippocampus and cortex play a pivotal role in generating rhythmic activity by clustering and synchronizing cell firing. Results of our simulations demonstrate that both the intrinsic cellular properties of neurons and the degree of network connectivity affect the characteristics of clustered dynamics exhibited in randomly connected, heterogeneous inhibitory networks. We quantify intrinsic cellular properties by the neuron's current-frequency relation (IF curve) and Phase Response Curve (PRC), a measure of how perturbations given at various phases of a neurons firing cycle affect subsequent spike timing. We analyze network bursting properties of networks of neurons with Type I or Type II properties in both excitability and PRC profile; Type I PRCs strictly show phase advances and IF curves that exhibit frequencies arbitrarily close to zero at firing threshold while Type II PRCs display both phase advances and delays and IF curves that have a non-zero frequency at threshold. Type II neurons whose properties arise with or without an M-type adaptation current are considered. We analyze network dynamics under different levels of cellular heterogeneity and as intrinsic cellular firing frequency and the time scale of decay of synaptic inhibition are varied. Many of the dynamics exhibited by these networks diverge from the predictions of the interneuron network gamma (ING) mechanism, as well as from results in all-to-all connected networks. Our results show that randomly connected networks of Type I neurons synchronize into a single cluster of active neurons while networks of Type II neurons organize into two mutually exclusive clusters segregated by the cells' intrinsic firing frequencies. Networks of Type II neurons containing the adaptation current behave similarly to networks of either Type I or Type II neurons depending on network parameters; however, the adaptation current creates differences in the cluster dynamics compared to those in networks of Type I or Type II neurons. To understand these results, we compute neuronal PRCs calculated with a perturbation matching the profile of the synaptic current in our networks. Differences in profiles of these PRCs across the different neuron types reveal mechanisms underlying the divergent network dynamics.

Neurogliaform cells in cortical circuits

Recent research into local-circuit GABAergic inhibitory interneurons of the mammalian central nervous system has provided unprecedented insight into the mechanics of neuronal circuitry and its dysfunction. Inhibitory interneurons consist of a broad array of anatomically and neurochemically diverse cell types, and this suggests that each occupies an equally diverse functional role. Although neurogliaform cells were observed by Cajal over a century ago, our understanding of the functional role of this class of interneurons is in its infancy. However, it is rapidly becoming clear that this cell type operates under a distinct repertoire of rules to provide novel forms of inhibitory control of numerous afferent pathways.

Hippocampal sharp wave-ripple: A cognitive biomarker for episodic memory and planning

Sharp wave ripples (SPW-Rs) represent the most synchronous population pattern in the mammalian brain. Their excitatory output affects a wide area of the cortex and several subcortical nuclei. SPW-Rs occur during "off-line" states of the brain, associated with consummatory behaviors and non-REM sleep, and are influenced by numerous neurotransmitters and neuromodulators. They arise from the excitatory recurrent system of the CA3 region and the SPW-induced excitation brings about a fast network oscillation (ripple) in CA1. The spike content of SPW-Rs is temporally and spatially coordinated by a consortium of interneurons to replay fragments of waking neuronal sequences in a compressed format. SPW-Rs assist in transferring this compressed hippocampal representation to distributed circuits to support memory consolidation; selective disruption of SPW-Rs interferes with memory. Recently acquired and pre-existing information are combined during SPW-R replay to influence decisions, plan actions and, potentially, allow for creative thoughts. In addition to the widely studied contribution to memory, SPW-Rs may also affect endocrine function via activation of hypothalamic circuits. Alteration of the physiological mechanisms supporting SPW-Rs leads to their pathological conversion, "p-ripples," which are a marker of epileptogenic tissue and can be observed in rodent models of schizophrenia and Alzheimer's Disease. Mechanisms for SPW-R genesis and function are discussed in this review.

Harnessing the power of theta: natural manipulations of cognitive performance during hippocampal theta-contingent eyeblink conditioning

Neurobiological oscillations are regarded as essential to normal information processing, including coordination and timing of cells and assemblies within structures as well as in long feedback loops of distributed neural systems. The hippocampal theta rhythm is a 3–12 Hz oscillatory potential observed during cognitive processes ranging from spatial navigation to associative learning. The lower range, 3–7 Hz, can occur during immobility and depends upon the integrity of cholinergic forebrain systems. Several studies have shown that the amount of pre-training theta in the rabbit strongly predicts the acquisition rate of classical eyeblink conditioning and that impairment of this system substantially slows the rate of learning. Our lab has used a brain-computer interface (BCI) that delivers eyeblink conditioning trials contingent upon the explicit presence or absence of hippocampal theta. A behavioral benefit of theta-contingent training has been demonstrated in both delay and trace forms of the paradigm with a two- to four-fold increase in learning speed. This behavioral effect is accompanied by enhanced amplitude and synchrony of hippocampal local field potential (LFP)s, multi-unit excitation, and single-unit response patterns that depend on theta state. Additionally, training in the presence of hippocampal theta has led to increases in the salience of tone-induced unit firing patterns in the medial prefrontal cortex, followed by persistent multi-unit activity during the trace interval. In cerebellum, rhythmicity and precise synchrony of stimulus time-locked LFPs with those of hippocampus occur preferentially under the theta condition. Here we review these findings, integrate them into current models of hippocampal-dependent learning and suggest how improvement in our understanding of neurobiological oscillations is critical for theories of medial temporal lobe processes underlying intact and pathological learning.

State-dependencies of learning across brain scales

Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous “spontaneous” shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly.

A computational study on how theta modulated inhibition can account for the long temporal windows in the entorhinal-hippocampal loop

A recent experimental study (Mizuseki, Sirota, Pastalkova, & Buzsaki, 2009) has shown that the temporal delays between population activities in successive entorhinal and hippocampal anatomical stages are longer (about 70-80ms) than expected from axon conduction velocities and passive synaptic integration of feed-forward excitatory inputs. We investigate via computer simulations the mechanisms that give rise to such long temporal delays in the hippocampus structures. A model of the dentate gyrus (DG), CA3 and CA1 microcircuits is presented that uses biophysical representations of the major cell types including granule cells, CA3 and CA1 pyramidal cells (PCs) and six types of interneurons: basket cells (BCs), axo-axonic cells (AACs), bistratified cells (BSCs), oriens lacunosum-moleculare cells (OLMs), mossy cells (MCs) and hilar perforant path associated cells (HC). Inputs to the network came from the entorhinal cortex (EC) (layers 2 and 3) and the medial septum (MS). The model simulates accurately the timing of firing of different hippocampal cells with respect to the theta rhythm. The model shows that the experimentally reported long temporal delays in the DG, CA3 and CA1 hippocampal regions are due to theta modulated somatic and axonic inhibition. The model further predicts that the phase at which the CA1 PCs fire with respect to the theta rhythm is determined primarily by their increased dendritic excitability caused by the decrease of the axial resistance and the A-type K(+) conductance along their dendritic trunk. The model predicted latencies by which the DG, CA3 and CA1 principal cells fire are inline with the experimental evidence. Finally, the model proposes functional roles for the different inhibitory interneurons in the retrieval of the memory pattern by the DG, CA3 and CA1 networks. The model makes a number of predictions, which can be tested experimentally, thus leading to a better understanding of the biophysical computations in the hippocampus.

Using multi-compartment ensemble modeling as an investigative tool of spatially distributed biophysical balances: application to hippocampal oriens-lacunosum/moleculare (O-LM) cells

Multi-compartmental models of neurons provide insight into the complex, integrative properties of dendrites. Because it is not feasible to experimentally determine the exact density and kinetics of each channel type in every neuronal compartment, an essential goal in developing models is to help characterize these properties. To address biological variability inherent in a given neuronal type, there has been a shift away from using hand-tuned models towards using ensembles or populations of models. In collectively capturing a neuron's output, ensemble modeling approaches uncover important conductance balances that control neuronal dynamics. However, conductances are never entirely known for a given neuron class in terms of its types, densities, kinetics and distributions. Thus, any multi-compartment model will always be incomplete. In this work, our main goal is to use ensemble modeling as an investigative tool of a neuron's biophysical balances, where the cycling between experiment and model is a design criterion from the start. We consider oriens-lacunosum/moleculare (O-LM) interneurons, a prominent interneuron subtype that plays an essential gating role of information flow in hippocampus. O-LM cells express the hyperpolarization-activated current (Ih). Although dendritic Ih could have a major influence on the integrative properties of O-LM cells, the compartmental distribution of Ih on O-LM dendrites is not known. Using a high-performance computing cluster, we generated a database of models that included those with or without dendritic Ih. A range of conductance values for nine different conductance types were used, and different morphologies explored. Models were quantified and ranked based on minimal error compared to a dataset of O-LM cell electrophysiological properties. Co-regulatory balances between conductances were revealed, two of which were dependent on the presence of dendritic Ih. These findings inform future experiments that differentiate between somatic and dendritic Ih, thereby continuing a cycle between model and experiment.

A kinetic model for the frequency dependence of cholinergic modulation at hippocampal GABAergic synapses

In this paper we use a simple model of presynaptic neuromodulation of GABA signaling to decipher paired whole-cell recordings of frequency dependent cholinergic neuromodulation at CA1 parvalbumin-containing basket cell (PV BC)-pyramidal cell synapses. Variance-mean analysis is employed to normalize the data, which is then used to estimate parameters in the mathematical model. Various parameterizations and hidden parameter dependencies are investigated using Markov Chain Monte Carlo (MCMC) parameter estimation techniques. This analysis reveals that frequency dependence of cholinergic modulation requires both calcium-dependent recovery from depression and mAChR-induced inhibition of presynaptic calcium entry. A reduction in calcium entry into the presynaptic terminal in the kinetic model accounted for the frequency-dependent effects of mAChR activation.

Modeling inheritance of phase precession in the hippocampal formation

Spatial information about the environment is encoded by the activity of place and grid cells in the hippocampal formation. As an animal traverses a cell's firing field, action potentials progressively shift to earlier phases of the theta oscillation (6-10 Hz). This "phase precession" is observed also in the prefrontal cortex and the ventral striatum, but mechanisms for its generation are unknown. However, once phase precession exists in one region, it might also propagate to downstream regions. Using a computational model, we analyze such inheritance of phase precession, for example, from the entorhinal cortex to CA1 and from CA3 to CA1. We find that distinctive subthreshold and suprathreshold features of the membrane potential of CA1 pyramidal cells (Harvey et al., 2009; Mizuseki et al., 2012; Royer et al., 2012) can be explained by inheritance and that excitatory input is essential. The model explains how inhibition modulates the slope and range of phase precession and provides two main testable predictions. First, theta-modulated inhibitory input to a CA1 pyramidal cell is not necessary for phase precession. Second, theta-modulated inhibitory input on its own generates membrane potential peaks that are in phase with peaks of the extracellular field. Furthermore, we suggest that the spatial distribution of field centers of a population of phase-precessing input cells determines, not only the place selectivity, but also the characteristics of phase precession of the targeted output cell. The inheritance model thus can explain why phase precession is observed throughout the hippocampal formation and other areas of the brain.

GABAergic neurotransmission and new strategies of neuromodulation to compensate synaptic dysfunction in early stages of Alzheimer's disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline, brain atrophy due to neuronal and synapse loss, and formation of two pathological lesions: extracellular amyloid plaques, composed largely of amyloid-beta peptide (Aβ), and neurofibrillary tangles formed by intracellular aggregates of hyperphosphorylated tau protein. Lesions mainly accumulate in brain regions that modulate cognitive functions such as the hippocampus, septum or amygdala. These brain structures have dense reciprocal glutamatergic, cholinergic, and GABAergic connections and their relationships directly affect learning and memory processes, so they have been proposed as highly susceptible regions to suffer damage by Aβ during AD course. Last findings support the emerging concept that soluble Aβ peptides, inducing an initial stage of synaptic dysfunction which probably starts 20–30 years before the clinical onset of AD, can perturb the excitatory–inhibitory balance of neural circuitries. In turn, neurotransmission imbalance will result in altered network activity that might be responsible of cognitive deficits in AD. Therefore, Aβ interactions on neurotransmission systems in memory-related brain regions such as amygdaloid complex, medial septum or hippocampus are critical in cognitive functions and appear as a pivotal target for drug design to improve learning and dysfunctions that manifest with age. Since treatments based on glutamatergic and cholinergic pharmacology in AD have shown limited success, therapies combining modulators of different neurotransmission systems including recent findings regarding the GABAergic system, emerge as a more useful tool for the treatment, and overall prevention, of this dementia. In this review, focused on inhibitory systems, we will analyze pharmacological strategies to compensate neurotransmission imbalance that might be considered as potential therapeutic interventions in AD.

Midazolam and atropine alter theta oscillations in the hippocampal CA1 region by modulating both the somatic and distal dendritic dipoles

Theta (4-12 Hz) oscillations in the hippocampus play an important role in learning and memory. They are altered by a wide variety of drugs that impair memory, and these effects may underlie or contribute to drug-induced amnesia. However, the network mechanisms linking drug actions with changes in memory formation remain poorly defined. Here, we used a multisite linear electrode array to measure local field potentials simultaneously across the CA1 layers of the hippocampus during active exploration, and employed current source density analysis and computational modeling to investigate how midazolam and atropine-two amnestic drugs that are used clinically and experimentally-change the relative timing and strength of the drivers of θ-oscillations. We found that two dipoles are present, with active inputs that are centered at the soma and the distal apical dendrite and passive return pathways that overlap in the mid-apical dendrite. Both drugs shifted the position of the phase reversal in the local field potential that occurred in the mid-apical dendritic region, but in opposite directions, by changing the strength of the dendritic pole, without altering the somatic pole or relative timing. Computational modeling showed that this constellation of changes, as well as an additional effect on a variably present mid-apical pole, could be produced by simultaneous changes in the active somatic and distal dendritic inputs. These network-level changes, produced by two amnestic drugs that target different types of receptors, may thus serve as a common basis for impaired memory encoding.

Direct excitation of parvalbumin-positive interneurons by M1 muscarinic acetylcholine receptors: roles in cellular excitability, inhibitory transmission and cognition

Parvalbumin-containing (PV) neurons, a major class of GABAergic interneurons, are essential circuit elements of learning networks. As levels of acetylcholine rise during active learning tasks, PV neurons become increasingly engaged in network dynamics. Conversely, impairment of either cholinergic or PV interneuron function induces learning deficits. Here, we examined PV interneurons in hippocampus (HC) and prefrontal cortex (PFC) and their modulation by muscarinic acetylcholine receptors (mAChRs). HC PV cells, visualized by crossing PV-CRE mice with Rosa26YFP mice, were anatomically identified as basket cells and PV bistratified cells in the stratum pyramidale; in stratum oriens, HC PV cells were electrophysiologically distinct from somatostatin-containing cells. With glutamatergic transmission pharmacologically blocked, mAChR activation enhanced PV cell excitability in both CA1 HC and PFC; however, CA1 HC PV cells exhibited a stronger postsynaptic depolarization than PFC PV cells. To delete M1 mAChRs genetically from PV interneurons, we created PV-M1 knockout mice by crossing PV-CRE and floxed M1 mice. The elimination of M1 mAChRs from PV cells diminished M1 mAChR immunoreactivity and muscarinic excitation of HC PV cells. Selective cholinergic activation of HC PV interneurons using Designer Receptors Exclusively Activated by Designer Drugs technology enhanced the frequency and amplitude of inhibitory synaptic currents in CA1 pyramidal cells. Finally, relative to wild-type controls, PV-M1 knockout mice exhibited impaired novel object recognition and, to a lesser extent, impaired spatial working memory, but reference memory remained intact. Therefore, the direct activation of M1 mAChRs on PV cells contributes to some forms of learning and memory.

Genetic suppression of transgenic APP rescues Hypersynchronous network activity in a mouse model of Alzeimer's disease

Alzheimer's disease (AD) is associated with an elevated risk for seizures that may be fundamentally connected to cognitive dysfunction. Supporting this link, many mouse models for AD exhibit abnormal electroencephalogram (EEG) activity in addition to the expected neuropathology and cognitive deficits. Here, we used a controllable transgenic system to investigate how network changes develop and are maintained in a model characterized by amyloid β (Aβ) overproduction and progressive amyloid pathology. EEG recordings in tet-off mice overexpressing amyloid precursor protein (APP) from birth display frequent sharp wave discharges (SWDs). Unexpectedly, we found that withholding APP overexpression until adulthood substantially delayed the appearance of epileptiform activity. Together, these findings suggest that juvenile APP overexpression altered cortical development to favor synchronized firing. Regardless of the age at which EEG abnormalities appeared, the phenotype was dependent on continued APP overexpression and abated over several weeks once transgene expression was suppressed. Abnormal EEG discharges were independent of plaque load and could be extinguished without altering deposited amyloid. Selective reduction of Aβ with a γ-secretase inhibitor has no effect on the frequency of SWDs, indicating that another APP fragment or the full-length protein was likely responsible for maintaining EEG abnormalities. Moreover, transgene suppression normalized the ratio of excitatory to inhibitory innervation in the cortex, whereas secretase inhibition did not. Our results suggest that APP overexpression, and not Aβ overproduction, is responsible for EEG abnormalities in our transgenic mice and can be rescued independently of pathology.

Sleep and movement differentiates actions of two types of somatostatin-expressing GABAergic interneuron in rat hippocampus

Neuropeptides acting on pre- and postsynaptic receptors are coreleased with GABA by interneurons including bistratified and O-LM cells, both expressing somatostatin but innervating segregated dendritic domains of pyramidal cells. Neuropeptide release requires high-frequency action potentials, but the firing patterns of most peptide/GABA-releasing interneurons during behavior are unknown. We show that behavioral and network states differentiate the activities of bistratified and O-LM cells in freely moving rats. Bistratified cells fire at higher rates during sleep than O-LM cells and, unlike O-LM cells, strongly increase spiking during sharp wave-associated ripples (SWRs). In contrast, O-LM interneurons decrease firing during sleep relative to awake states and are mostly inhibited during SWRs. During movement, both cell types fire cooperatively at the troughs of theta oscillations but with different frequencies. Somatostatin and GABA are differentially released to distinct dendritic zones of CA1 pyramidal cells during sleep and wakefulness to coordinate segregated glutamatergic inputs from entorhinal cortex and CA3.

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Bistratified and O-LM cells release GABA and somatostatin to distinct dendrites

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During movement the two cells cooperate temporally but fire at different frequencies

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During sleep bistratified cells are strongly active, O-LM cells decrease firing

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Behavior differentiates GABA and somatostatin release to distinct dendritic zones

On phasic inhibition during hippocampal theta

Two computational models are used to explore the possible implications of recent experimental data (Royer et al. 2012) on phasic inhibition during theta frequency (4-10 Hz) oscillations in the hippocampi of actively behaving rodents. A working hypothesis from previous experimental and modelling studies is that a theta cycle is divided into encoding (when synaptic plasticity is enhanced) and recall (when plasticity is suppressed) half cycles. Using a compartmental model of a CA1 pyramidal cell, including dendritic spines, we demonstrate that out-of-phase perisomatic and dendritic inhibition, respectively, can promote the necessary conditions for these half cycles. Perisomatic inhibition allows dendritic calcium spikes that promote synaptic LTP, while minimising cell output. Dendritic inhibition, on the other hand, both controls cell output and suppresses dendritic calcium spikes, preventing LTP. The exact phase relationship between these sub-cycles may not be fixed. Using a simple sum-of-sinusoids activity model, we suggest an interpretation of the data of Royer et al. (2012) in which a fixed-phase encoding sub-cycle is surrounded by a flexible-phase recall cycle that follows the peak of excitatory drive and consequent phase precession of activity as an animal passes through a pyramidal cell's place field.

Dopaminergic modulation of GABAergic transmission in the entorhinal cortex: concerted roles of α1 adrenoreceptors, inward rectifier K⁺, and T-type Ca²⁺ channels

Whereas the entorhinal cortex (EC) receives profuse dopaminergic innervations from the midbrain, the effects of dopamine (DA) on γ-Aminobutyric acid (GABA)ergic interneurons in this brain region have not been determined. We probed the actions of DA on GABAA receptor-mediated synaptic transmission in the EC. Application of DA increased the frequency, not the amplitude, of spontaneous IPSCs (sIPSCs) and miniature IPSCs (mIPSCs) recorded from entorhinal principal neurons, but slightly reduced the amplitude of the evoked IPSCs. The effects of DA were unexpectedly found to be mediated by α1 adrenoreceptors, but not by DA receptors. DA endogenously released by the application of amphetamine also increased the frequency of sIPSCs. Ca(2+) influx via T-type Ca(2+) channels was required for DA-induced facilitation of sIPSCs and mIPSCs. DA depolarized and enhanced the firing frequency of action potentials of interneurons. DA-induced depolarization was independent of extracellular Na(+) and Ca(2+) and did not require the functions of hyperpolarization-activated (Ih) channels and T-type Ca(2+) channels. DA-generated currents showed a reversal potential close to the K(+) reversal potential and inward rectification, suggesting that DA inhibits the inward rectifier K(+) channels (Kirs). Our results demonstrate that DA facilitates GABA release by activating α1 adrenoreceptors to inhibit Kirs, which further depolarize interneurons resulting in secondary Ca(2+) influx via T-type Ca(+) channels.

Theta rhythm and the encoding and retrieval of space and time

Physiological data demonstrates theta frequency oscillations associated with memory function and spatial behavior. Modeling and data from animals provide a perspective on the functional role of theta rhythm, including correlations with behavioral performance and coding by timing of spikes relative to phase of oscillations. Data supports a theorized role of theta rhythm in setting the dynamics for encoding and retrieval within cortical circuits. Recent data also supports models showing how network and cellular theta rhythmicity allows neurons in the entorhinal cortex and hippocampus to code time and space as a possible substrate for encoding events in episodic memory. Here we discuss these models and relate them to current physiological and behavioral data.

A MULTI-LAYER NEURAL-MASS MODEL FOR LEARNING SEQUENCES USING THETA/GAMMA OSCILLATIONS

A neural mass model for the memorization of sequences is presented. It exploits three layers of cortical columns that generate a theta/gamma rhythm. The first layer implements an auto-associative memory working in the theta range; the second segments objects in the gamma range; finally, the feedback interactions between the third and the second layers realize a hetero-associative memory for learning a sequence. After training with Hebbian and anti-Hebbian rules, the network recovers sequences and accounts for the phase-precession phenomenon.

Spiking neural network model for memorizing sequences with forward and backward recall

We present an oscillatory network of conductance based spiking neurons of Hodgkin-Huxley type as a model of memory storage and retrieval of sequences of events (or objects). The model is inspired by psychological and neurobiological evidence on sequential memories. The building block of the model is an oscillatory module which contains excitatory and inhibitory neurons with all-to-all connections. The connection architecture comprises two layers. A lower layer represents consecutive events during their storage and recall. This layer is composed of oscillatory modules. Plastic excitatory connections between the modules are implemented using an STDP type learning rule for sequential storage. Excitatory neurons in the upper layer project star-like modifiable connections toward the excitatory lower layer neurons. These neurons in the upper layer are used to tag sequences of events represented in the lower layer. Computer simulations demonstrate good performance of the model including difficult cases when different sequences contain overlapping events. We show that the model with STDP type or anti-STDP type learning rules can be applied for the simulation of forward and backward replay of neural spikes respectively.

Oscillatory activity during maintenance of spatial and temporal information in working memory

Working memory (WM) processes help keep information in an active state so it can be used to guide future behavior. Although numerous studies have investigated brain activity associated with spatial WM in humans and monkeys, little research has focused on the neural mechanisms of WM for temporal order information, and how processing of temporal and spatial information might differ. Available evidence indicates that similar frontoparietal regions are recruited during temporal and spatial WM, although there are data suggesting that they are distinct processes. The mechanisms that allow for differential maintenance of these two types of information are unclear. One possibility is that neural oscillations may differentially contribute to temporal and spatial WM. In the present study, we used scalp electroencephalography (EEG) to compare patterns of oscillatory activity during maintenance of spatial and temporal information in WM. Time-frequency analysis of EEG data revealed enhanced left frontal theta (5-8 Hz), enhanced posterior alpha (9-12 Hz), and enhanced left posterior beta (14-28 Hz) power during the delay period of correct temporal order trials compared to correct spatial trials. In contrast, gamma (30-50 Hz) power at right lateral frontal sites was increased during the delay period of spatial WM trials, as compared to temporal WM trials. The present results are consistent with the idea that neural oscillatory patterns provide distinct mechanisms for the maintenance of temporal and spatial information in WM. Specifically, theta oscillations are most critical for the maintenance of temporal information in WM. Possible roles of higher frequency oscillations in temporal and spatial memory are also discussed.