Distinct frequency preferences of different types of rat hippocampal neurones in response to oscillatory input currents

Distinct feedforward and feedback pathways for cell-type specific attention effects

Selective attention is thought to depend on enhanced firing activity in extrastriate areas. Theories suggest that this enhancement depends on selective inter-areal communication via gamma (30-80 Hz) phase-locking. To test this, we simultaneously recorded from different cell types and cortical layers of macaque V1 and V4. We find that while V1-V4 gamma phase-locking between local field potentials increases with attention, the V1 gamma rhythm does not engage V4 excitatory-neurons, but only fast-spiking interneurons in L4 of V4. By contrast, attention enhances V4 spike-rates in both excitatory and inhibitory cells, most strongly in L2/3. The rate increase in L2/3 of V4 precedes V1 in time. These findings suggest enhanced signal transmission with attention does not depend on inter-areal gamma phase-locking and show that the endogenous gamma rhythm has cell-type- and layer-specific effects on downstream target areas. Similar findings were made in the mouse visual system, based on opto-tagging of identified interneurons.

Review of EEG Affective Recognition with a Neuroscience Perspective

Emotions are a series of subconscious, fleeting, and sometimes elusive manifestations of the human innate system. They play crucial roles in everyday life-influencing the way we evaluate ourselves, our surroundings, and how we interact with our world. To date, there has been an abundance of research on the domains of neuroscience and affective computing, with experimental evidence and neural network models, respectively, to elucidate the neural circuitry involved in and neural correlates for emotion recognition. Recent advances in affective computing neural network models often relate closely to evidence and perspectives gathered from neuroscience to explain the models. Specifically, there has been growing interest in the area of EEG-based emotion recognition to adopt models based on the neural underpinnings of the processing, generation, and subsequent collection of EEG data. In this respect, our review focuses on providing neuroscientific evidence and perspectives to discuss how emotions potentially come forth as the product of neural activities occurring at the level of subcortical structures within the brain's emotional circuitry and the association with current affective computing models in recognizing emotions. Furthermore, we discuss whether such biologically inspired modeling is the solution to advance the field in EEG-based emotion recognition and beyond.

Dynamics of interaction between IH and IKLT currents to mediate double resonances of medial superior olive neurons related to sound localization

Neurons in the medial superior olive (MSO) exhibit high frequency responses such as subthreshold resonance, which is helpful to sensitively detect a small difference in the arrival time of sounds between two ears for precise sound localization. Recently, except for the high frequency depolarization resonance mediated by a low threshold potassium (IKLT) current, a low frequency hyperpolarization resonance mediated by a hyperpolarization-activated cation (IH) current is observed in experiments on the MSO neurons, forming double resonances. The complex dynamics underlying double resonances are studied in an MSO neuron model in the present paper. Firstly, double resonances similar to the experimental observations are simulated as the resting membrane potential is between half-activation voltages of IH and IKLT currents, and stimulation current (IZAP) with large amplitude and exponentially increasing frequency is applied. Secondly, multiple effective factors to modulate double resonances are obtained. Especially, the decrease of time constant of IKLT current and increase of conductance of IH and IKLT currents can enhance the depolarization resonance frequency for precise sound localization. Last, different frequency responses of slow IH and fast IKLT currents in formation of the resonances are acquired. A middle phase difference between IZAP and IKLT currents appears at a high frequency, and the interaction between the positive part of IZAP and the negative IKLT current forms the depolarization resonance. Interaction between the negative part of IZAP and positive IH current with a middle phase difference results in hyperpolarization resonance at a low frequency. Furthermore, the phase difference between IZAP and resonance current can well explain the increase of depolarization resonance frequency modulated by the increase of conductance of IH or IKLT currents. The results present the dynamical and biophysical mechanisms for the double resonances mediated by two currents in the MSO neurons, which is helpful to enhance the depolarization resonance frequency for precise sound localization.

Missing Puzzle Pieces in Dementia Research: HCN Channels and Theta Oscillations

Increasing evidence indicates a role of hyperpolarization activated cation (HCN) channels in controlling the resting membrane potential, pacemaker activity, memory formation, sleep, and arousal. Their disfunction may be associated with the development of epilepsy and age-related memory decline. Neuronal hyperexcitability involved in epileptogenesis and EEG desynchronization occur in the course of dementia in human Alzheimer's Disease (AD) and animal models, nevertheless the underlying ionic and cellular mechanisms of these effects are not well understood. Some suggest that theta rhythms involved in memory formation could be used as a marker of memory disturbances in the course of neurogenerative diseases, including AD. This review focusses on the interplay between hyperpolarization HCN channels, theta oscillations, memory formation and their role(s) in dementias, including AD. While individually, each of these factors have been linked to each other with strong supportive evidence, we hope here to expand this linkage to a more inclusive picture. Thus, HCN channels could provide a molecular target for developing new therapeutic agents for preventing and/or treating dementia.

Granule cells perform frequency-dependent pattern separation in a computational model of the dentate gyrus

Mnemonic discrimination (MD) may be dependent on oscillatory perforant path input frequencies to the hippocampus in a "U"-shaped fashion, where some studies show that slow and fast input frequencies support MD, while other studies show that intermediate frequencies disrupt MD. We hypothesize that pattern separation (PS) underlies frequency-dependent MD performance. We aim to study, in a computational model of the hippocampal dentate gyrus (DG), the network and cellular mechanisms governing this putative "U"-shaped PS relationship. We implemented a biophysical model of the DG that produces the hypothesized "U"-shaped input frequency-PS relationship, and its associated oscillatory electrophysiological signatures. We subsequently evaluated the network's PS ability using an adapted spatiotemporal task. We undertook systematic lesion studies to identify the network-level mechanisms driving the "U"-shaped input frequency-PS relationship. A minimal circuit of a single granule cell (GC) stimulated with oscillatory inputs was also used to study potential cellular-level mechanisms. Lesioning synapses onto GCs did not impact the "U"-shaped input frequency-PS relationship. Furthermore, GC inhibition limits PS performance for fast frequency inputs, while enhancing PS for slow frequency inputs. GC interspike interval was found to be input frequency dependent in a "U"-shaped fashion, paralleling frequency-dependent PS observed at the network level. Additionally, GCs showed an attenuated firing response for fast frequency inputs. We conclude that independent of network-level inhibition, GCs may intrinsically be capable of producing a "U"-shaped input frequency-PS relationship. GCs may preferentially decorrelate slow and fast inputs via spike timing reorganization and high frequency filtering.

Physiological features of parvalbumin-expressing GABAergic interneurons contributing to high-frequency oscillations in the cerebral cortex

Parvalbumin-expressing (PV+) inhibitory interneurons drive gamma oscillations (30-80 Hz), which underlie higher cognitive functions. In this review, we discuss two groups/aspects of fundamental properties of PV+ interneurons. In the first group (dubbed Before Axon), we list properties representing optimal synaptic integration in PV+ interneurons designed to support fast oscillations. For example: [i] Information can neither enter nor leave the neocortex without the engagement of fast PV+ -mediated inhibition; [ii] Voltage responses in PV+ interneuron dendrites integrate linearly to reduce impact of the fluctuations in the afferent drive; and [iii] Reversed somatodendritic Rm gradient accelerates the time courses of synaptic potentials arriving at the soma. In the second group (dubbed After Axon), we list morphological and biophysical properties responsible for (a) short synaptic delays, and (b) efficient postsynaptic outcomes. For example: [i] Fast-spiking ability that allows PV+ interneurons to outpace other cortical neurons (pyramidal neurons). [ii] Myelinated axon (which is only found in the PV+ subclass of interneurons) to secure fast-spiking at the initial axon segment; and [iii] Inhibitory autapses - autoinhibition, which assures brief biphasic voltage transients and supports postinhibitory rebounds. Recent advent of scientific tools, such as viral strategies to target PV cells and the ability to monitor PV cells via in vivo imaging during behavior, will aid in defining the role of PV cells in the CNS. Given the link between PV+ interneurons and cognition, in the future, it would be useful to carry out physiological recordings in the PV+ cell type selectively and characterize if and how psychiatric and neurological diseases affect initiation and propagation of electrical signals in this cortical sub-circuit. Voltage imaging may allow fast recordings of electrical signals from many PV+ interneurons simultaneously.

Hippocampal interneuronal dysfunction and hyperexcitability in a porcine model of concussion

Cognitive impairment is a common symptom following mild traumatic brain injury (mTBI or concussion) and can persist for years in some individuals. Hippocampal slice preparations following closed-head, rotational acceleration injury in swine have previously demonstrated reduced axonal function and hippocampal circuitry disruption. However, electrophysiological changes in hippocampal neurons and their subtypes in a large animal mTBI model have not been examined. Using in vivo electrophysiology techniques, we examined laminar oscillatory field potentials and single unit activity in the hippocampal network 7 days post-injury in anesthetized minipigs. Concussion altered the electrophysiological properties of pyramidal cells and interneurons differently in area CA1. While the firing rate, spike width and amplitude of CA1 interneurons were significantly decreased post-mTBI, these parameters were unchanged in CA1 pyramidal neurons. In addition, CA1 pyramidal neurons in TBI animals were less entrained to hippocampal gamma (40-80 Hz) oscillations. Stimulation of the Schaffer collaterals also revealed hyperexcitability across the CA1 lamina post-mTBI. Computational simulations suggest that reported changes in interneuronal physiology may be due to alterations in voltage-gated sodium channels. These data demonstrate that a single concussion can lead to significant neuronal and circuit level changes in the hippocampus, which may contribute to cognitive dysfunction following mTBI.

The alpha2 nicotinic acetylcholine receptor, a subunit with unique and selective expression in inhibitory interneurons associated with principal cells

Nicotinic acetylcholine receptors (nAChRs) play crucial roles in various human disorders, with the α7, α4, α6, and α3-containing nAChR subtypes extensively studied in relation to conditions such as Alzheimer's disease, Parkinson's disease, nicotine dependence, mood disorders, and stress disorders. In contrast, the α2-nAChR subunit has received less attention due to its more restricted expression and the scarcity of specific agonists and antagonists for studying its function. Nevertheless, recent research has shed light on the unique expression pattern of the Chrna2 gene, which encodes the α2-nAChR subunit, and its involvement in distinct populations of inhibitory interneurons. This review highlights the structure, pharmacology, localization, function, and disease associations of α2-containing nAChRs and points to the unique expression pattern of the Chrna2 gene and its role in different inhibitory interneuron populations. These populations, including the oriens lacunosum moleculare (OLM) cells in the hippocampus, Martinotti cells in the neocortex, and Renshaw cells in the spinal cord, share common features and contribute to recurrent inhibitory microcircuits. Thus, the α2-nAChR subunit's unique expression pattern in specific interneuron populations and its role in recurrent inhibitory microcircuits highlight its importance in various physiological processes. Further research is necessary to uncover the comprehensive functionality of α2-containing nAChRs, delineate their specific contributions to neuronal circuits, and investigate their potential as therapeutic targets for related disorders.

Cell-type-specific propagation of visual flicker

Rhythmic flicker stimulation has gained interest as a treatment for neurodegenerative diseases and as a method for frequency tagging neural activity. Yet, little is known about the way in which flicker-induced synchronization propagates across cortical levels and impacts different cell types. Here, we use Neuropixels to record from the lateral geniculate nucleus (LGN), the primary visual cortex (V1), and CA1 in mice while presenting visual flicker stimuli. LGN neurons show strong phase locking up to 40 Hz, whereas phase locking is substantially weaker in V1 and is absent in CA1. Laminar analyses reveal an attenuation of phase locking at 40 Hz for each processing stage. Gamma-rhythmic flicker predominantly entrains fast-spiking interneurons. Optotagging experiments show that these neurons correspond to either parvalbumin (PV+) or narrow-waveform somatostatin (Sst+) neurons. A computational model can explain the observed differences based on the neurons' capacitative low-pass filtering properties. In summary, the propagation of synchronized activity and its effect on distinct cell types strongly depend on its frequency.

Principles of large-scale neural interactions

What mechanisms underlie flexible inter-areal communication in the cortex? We consider four mechanisms for temporal coordination and their contributions to communication: (1) Oscillatory synchronization (communication-through-coherence); (2) communication-through-resonance; (3) non-linear integration; and (4) linear signal transmission (coherence-through-communication). We discuss major challenges for communication-through-coherence based on layer- and cell-type-specific analyses of spike phase-locking, heterogeneity of dynamics across networks and states, and computational models for selective communication. We argue that resonance and non-linear integration are viable alternative mechanisms that facilitate computation and selective communication in recurrent networks. Finally, we consider communication in relation to cortical hierarchy and critically examine the hypothesis that feedforward and feedback communication use fast (gamma) and slow (alpha/beta) frequencies, respectively. Instead, we propose that feedforward propagation of prediction errors relies on the non-linear amplification of aperiodic transients, whereas gamma and beta rhythms represent rhythmic equilibrium states that facilitate sustained and efficient information encoding and amplification of short-range feedback via resonance.

Reduced oriens-lacunosum/moleculare cell model identifies biophysical current balances for in vivo theta frequency spiking resonance

Conductance-based models have played an important role in the development of modern neuroscience. These mathematical models are powerful "tools" that enable theoretical explorations in experimentally untenable situations, and can lead to the development of novel hypotheses and predictions. With advances in cell imaging and computational power, multi-compartment models with morphological accuracy are becoming common practice. However, as more biological details are added, they make extensive explorations and analyses more challenging largely due to their huge computational expense. Here, we focus on oriens-lacunosum/moleculare (OLM) cell models. OLM cells can contribute to functionally relevant theta rhythms in the hippocampus by virtue of their ability to express spiking resonance at theta frequencies, but what characteristics underlie this is far from clear. We converted a previously developed detailed multi-compartment OLM cell model into a reduced single compartment model that retained biophysical fidelity with its underlying ion currents. We showed that the reduced OLM cell model can capture complex output that includes spiking resonance in in vivo-like scenarios as previously obtained with the multi-compartment model. Using the reduced model, we were able to greatly expand our in vivo-like scenarios. Applying spike-triggered average analyses, we were able to to determine that it is a combination of hyperpolarization-activated cation and muscarinic type potassium currents that specifically allow OLM cells to exhibit spiking resonance at theta frequencies. Further, we developed a robust Kalman Filtering (KF) method to estimate parameters of the reduced model in real-time. We showed that it may be possible to directly estimate conductance parameters from experiments since this KF method can reliably extract parameter values from model voltage recordings. Overall, our work showcases how the contribution of cellular biophysical current details could be determined and assessed for spiking resonance. As well, our work shows that it may be possible to directly extract these parameters from current clamp voltage recordings.

Interictal localization of the epileptogenic zone: Utilizing the observed resonance behavior in the spectral band of surrounding inhibition

Introduction

The gold standard for identification of the epileptogenic zone (EZ) continues to be the visual inspection of electrographic changes around seizures' onset by experienced electroencephalography (EEG) readers. Development of an epileptogenic focus localization tool that can delineate the EZ from analysis of interictal (seizure-free) periods is still an open question of great significance for improved diagnosis (e.g., presurgical evaluation) and treatment of epilepsy (e.g., surgical outcome).

Methods

We developed an EZ interictal localization algorithm (EZILA) based on novel analysis of intracranial EEG (iEEG) using a univariate periodogram-type power measure, a straight-forward ranking approach, a robust dimensional reduction method and a clustering technique. Ten patients with temporal and extra temporal lobe epilepsies, and matching the inclusion criteria of having iEEG recordings at the epilepsy monitoring unit (EMU) and being Engel Class I ≥12 months post-surgery, were recruited in this study.

Results

In a nested k-fold cross validation statistical framework, EZILA assigned the highest score to iEEG channels within the EZ in all patients (10/10) during the first hour of the iEEG recordings and up to their first typical clinical seizure in the EMU (i.e., early interictal period). To further validate EZILA's performance, data from two new (Engel Class I) patients were analyzed in a double-blinded fashion; the EZILA successfully localized iEEG channels within the EZ from interictal iEEG in both patients.

Discussion

Out of the sampled brain regions, iEEG channels in the EZ were most frequently and maximally active in seizure-free (interictal) periods across patients in specific narrow gamma frequency band (∼60-80 Hz), which we have termed focal frequency band (FFB). These findings are consistent with the hypothesis that the EZ may interictally be regulated (controlled) by surrounding inhibitory neurons with resonance characteristics within this narrow gamma band.

Theta patterns of stimulation induce synaptic and intrinsic potentiation in O-LM interneurons

Brain oscillations have long-lasting effects on synaptic and cellular properties. For instance, synaptic stimulation at theta (θ) frequency induces persistent depression of both excitatory synaptic transmission and intrinsic excitability in CA1 principal neurons. However, the incidence of θ activity on synaptic transmission and intrinsic excitability in hippocampal GABAergic interneurons is unclear. We report here the induction of both synaptic and intrinsic potentiation in oriens-lacunosum moleculare (O-LM) interneurons following stimulation of afferent glutamatergic inputs in the θ frequency range (∼5 Hz). Long-term synaptic potentiation (LTP) is induced by synaptic activation of calcium-permeable AMPA receptors (CP-AMPAR), whereas long-term potentiation of intrinsic excitability (LTP-IE) results from the mGluR1-dependent down-regulation of Kv7 voltage-dependent potassium channel and hyperpolarization activated and cyclic nucleotide-gated (HCN) channel through the depletion of phosphatidylinositol-4,5-biphosphate (PIP2). LTP and LTP-IE are reversible, demonstrating that both synaptic and intrinsic changes are bidirectional in O-LM cells. We conclude that synaptic activity at θ frequency induces both synaptic and intrinsic potentiation in O-LM interneurons, i.e., the opposite of what is typically seen in glutamatergic neurons.

Modeling relationships between rhythmic processes and neuronal spike timing

Neurons are embedded in complex networks, where they participate in repetitive, coordinated interactions with other neurons. Neuronal spike timing is thus predictably constrained by a range of ionic currents that shape activity at both short (milliseconds) and longer (tens to hundreds of milliseconds) timescales, but we lack analytical tools to rigorously identify these relationships. Here, we innovate a modeling approach to test the relationship between oscillations in the local field potential (LFP) and neuronal spike timing. We use kernel density estimation to relate single neuron spike timing and the phase of LFP rhythms (in simulated and hippocampal CA1 neuronal spike trains). We then combine phase and short (3 ms) spike history information within a logistic regression framework ("phaseSH models"), and show that models that leverage refractory constraints and oscillatory phase information can effectively test whether-and the degree to which-rhythmic currents (as measured from the LFP) reliably explain variance in neuronal spike trains. This approach allows researchers to systematically test the relationship between oscillatory activity and neuronal spiking dynamics as they unfold over time and as they shift to adapt to distinct behavioral conditions.

Network resonance can be generated independently at distinct levels of neuronal organization

Resonance is defined as maximal response of a system to periodic inputs in a limited frequency band. Resonance may serve to optimize inter-neuronal communication, and has been observed at multiple levels of neuronal organization. However, it is unknown how neuronal resonance observed at the network level is generated and how network resonance depends on the properties of the network building blocks. Here, we first develop a metric for quantifying spike timing resonance in the presence of background noise, extending the notion of spiking resonance for in vivo experiments. Using conductance-based models, we find that network resonance can be inherited from resonances at other levels of organization, or be intrinsically generated by combining mechanisms across distinct levels. Resonance of membrane potential fluctuations, postsynaptic potentials, and single neuron spiking can each be generated independently of resonance at any other level and be propagated to the network level. At all levels of organization, interactions between processes that give rise to low- and high-pass filters generate the observed resonance. Intrinsic network resonance can be generated by the combination of filters belonging to different levels of organization. Inhibition-induced network resonance can emerge by inheritance from resonance of membrane potential fluctuations, and be sharpened by presynaptic high-pass filtering. Our results demonstrate a multiplicity of qualitatively different mechanisms that can generate resonance in neuronal systems, and provide analysis tools and a conceptual framework for the mechanistic investigation of network resonance in terms of circuit components, across levels of neuronal organization.

How one part of the brain responds to periodic input from another part depends on resonant circuit properties. Resonance is a basic property of physical systems, and has been experimentally observed at various levels of neuronal organization both in vitro and in vivo. Yet how resonance is generated in neuronal networks is largely unknown. In particular, whether resonance can be generated directly at the level of a network of spiking neurons remains to be determined. Using detailed biophysical modeling, we develop a conceptual framework according to which resonance at a given level of organization is generated by the interplay of low- and high-pass filters, implemented at either the same or across levels of neuronal organization. We tease apart representative, biophysically-plausible generative mechanisms of resonance at four different levels of organization: membrane potential fluctuations, single neuron spiking, synaptic transmission, and neuronal networks. We identify conditions under which resonance at one level can be inherited to another level of organization, provide conclusive evidence that resonance at each level can be generated without resonance at any other level, and describe a number of representative routes to network resonance. The proposed framework facilitates the investigation of resonance in neuronal systems.

Parvalbumin interneuron dendrites enhance gamma oscillations

Dendrites are essential determinants of the input-output relationship of single neurons, but their role in network computations is not well understood. Here, we use a combination of dendritic patch-clamp recordings and in silico modeling to determine how dendrites of parvalbumin (PV)-expressing basket cells contribute to network oscillations in the gamma frequency band. Simultaneous soma-dendrite recordings from PV basket cells in the dentate gyrus reveal that the slope, or gain, of the dendritic input-output relationship is exceptionally low, thereby reducing the cell's sensitivity to changes in its input. By simulating gamma oscillations in detailed network models, we demonstrate that the low gain is key to increase spike synchrony in PV basket cell assemblies when cells are driven by spatially and temporally heterogeneous synaptic inputs. These results highlight the role of inhibitory neuron dendrites in synchronized network oscillations.

Sleep Disruption and Cancer: Chicken or the Egg?

Sleep is a nearly ubiquitous phenomenon across the phylogenetic tree, highlighting its essential role in ensuring fitness across evolutionary time. Consequently, chronic disruption of the duration, timing, or structure of sleep can cause widespread problems in multiple physiological systems, including those that regulate energy balance, immune function, and cognitive capacity, among others. Many, if not all these systems, become altered throughout the course of cancer initiation, growth, metastatic spread, treatment, and recurrence. Recent work has demonstrated how changes in sleep influence the development of chronic diseases, including cancer, in both humans and animal models. A common finding is that for some cancers (e.g., breast), chronic disruption of sleep/wake states prior to disease onset is associated with an increased risk for cancer development. Additionally, sleep disruption after cancer initiation is often associated with worse outcomes. Recently, evidence suggesting that cancer itself can affect neuronal circuits controlling sleep and wakefulness has accumulated. Patients with cancer often report difficulty falling asleep, difficulty staying asleep, and severe fatigue, during and even years after treatment. In addition to the psychological stress associated with cancer, cancer itself may alter sleep homeostasis through changes to host physiology and via currently undefined mechanisms. Moreover, cancer treatments (e.g., chemotherapy, radiation, hormonal, and surgical) may further worsen sleep problems through complex biological processes yet to be fully understood. This results in a “chicken or the egg” phenomenon, where it is unclear whether sleep disruption promotes cancer or cancer reciprocally disrupts sleep. This review will discuss existing evidence for both hypotheses and present a framework through which the interactions between sleep and cancer can be dissociated and causally investigated.

The voltage and spiking responses of subthreshold resonant neurons to structured and fluctuating inputs: persistence and loss of resonance and variability

We systematically investigate the response of neurons to oscillatory currents and synaptic-like inputs and we extend our investigation to non-structured synaptic-like spiking inputs with more realistic distributions of presynaptic spike times. We use two types of chirp-like inputs consisting of (i) a sequence of cycles with discretely increasing frequencies over time, and (ii) a sequence having the same cycles arranged in an arbitrary order. We develop and use a number of frequency-dependent voltage response metrics to capture the different aspects of the voltage response, including the standard impedance (Z) and the peak-to-trough amplitude envelope ([Formula: see text]) profiles. We show that Z-resonant cells (cells that exhibit subthreshold resonance in response to sinusoidal inputs) also show [Formula: see text]-resonance in response to sinusoidal inputs, but generally do not (or do it very mildly) in response to square-wave and synaptic-like inputs. In the latter cases the resonant response using Z is not predictive of the preferred frequencies at which the neurons spike when the input amplitude is increased above subthreshold levels. We also show that responses to conductance-based synaptic-like inputs are attenuated as compared to the response to current-based synaptic-like inputs, thus providing an explanation to previous experimental results. These response patterns were strongly dependent on the intrinsic properties of the participating neurons, in particular whether the unperturbed Z-resonant cells had a stable node or a focus. In addition, we show that variability emerges in response to chirp-like inputs with arbitrarily ordered patterns where all signals (trials) in a given protocol have the same frequency content and the only source of uncertainty is the subset of all possible permutations of cycles chosen for a given protocol. This variability is the result of the multiple different ways in which the autonomous transient dynamics is activated across cycles in each signal (different cycle orderings) and across trials. We extend our results to include high-rate Poisson distributed current- and conductance-based synaptic inputs and compare them with similar results using additive Gaussian white noise. We show that the responses to both Poisson-distributed synaptic inputs are attenuated with respect to the responses to Gaussian white noise. For cells that exhibit oscillatory responses to Gaussian white noise (band-pass filters), the response to conductance-based synaptic inputs are low-pass filters, while the response to current-based synaptic inputs may remain band-pass filters, consistent with experimental findings. Our results shed light on the mechanisms of communication of oscillatory activity among neurons in a network via subthreshold oscillations and resonance and the generation of network resonance.

From mechanisms to functions: The role of theta and gamma coherence in the intrahippocampal circuits

Brain rhythms are essential for information processing in neuronal networks. Oscillations recorded in different brain regions can be synchronized and have a constant phase difference, that is, they can be coherent. Coherence between local field potential (LFP) signals from different brain regions may be correlated with the performance of cognitive tasks, indicating that these regions of the brain are jointly involved in the information processing. Why does coherence occur and how is it related to the information transfer between different regions of the hippocampal formation? In this article, we discuss possible mechanisms of theta and gamma coherence and its role in the hippocampus-dependent attention and memory processes, since theta and gamma rhythms are most pronounced in these processes. We review in vivo studies of interactions between different regions of the hippocampal formation in theta and gamma frequency bands. The key propositions of the review are as follows: (1) coherence emerges from synchronous postsynaptic currents in principal neurons as a result of synchronization of neuronal spike activity; (2) the synchronization of neuronal spike patterns in two regions of the hippocampal formation can be realized through induction or resonance; (3) coherence at a specific time point reflects the transfer of information between the regions of the hippocampal formation; (4) the physiological roles of theta and gamma coherence are different due to their different functions and mechanisms of generation. All hippocampal neurons are involved in theta activity, and theta coherence arranges the firing order of principal neurons throughout the hippocampal formation. In contrast, gamma coherence reflects the coupling of active neuronal ensembles. Overall, the coherence of LFPs between different areas of the brain is an important physiological process based on the synchronized neuronal firing, and it is essential for cooperative information processing.

Predictive coding of natural images by V1 firing rates and rhythmic synchronization

Predictive coding is an important candidate theory of self-supervised learning in the brain. Its central idea is that sensory responses result from comparisons between bottom-up inputs and contextual predictions, a process in which rates and synchronization may play distinct roles. We recorded from awake macaque V1 and developed a technique to quantify stimulus predictability for natural images based on self-supervised, generative neural networks. We find that neuronal firing rates were mainly modulated by the contextual predictability of higher-order image features, which correlated strongly with human perceptual similarity judgments. By contrast, V1 gamma (γ)-synchronization increased monotonically with the contextual predictability of low-level image features and emerged exclusively for larger stimuli. Consequently, γ-synchronization was induced by natural images that are highly compressible and low-dimensional. Natural stimuli with low predictability induced prominent, late-onset beta (β)-synchronization, likely reflecting cortical feedback. Our findings reveal distinct roles of synchronization and firing rates in the predictive coding of natural images.

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Predictability in natural images quantified with self-supervised neural networks

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V1 firing rates decrease with predictability of high- not low-level image features

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γ-synchronization increases with predictability of low-level image features

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Late-onset β-synchronization for natural scenes with low predictability

Theta activity paradoxically boosts gamma and ripple frequency sensitivity in prefrontal interneurons

Fast oscillations in cortical circuits critically depend on GABAergic interneurons. Which interneuron types and populations can drive different cortical rhythms, however, remains unresolved and may depend on brain state. Here, we measured the sensitivity of different GABAergic interneurons in prefrontal cortex under conditions mimicking distinct brain states. While fast-spiking neurons always exhibited a wide bandwidth of around 400 Hz, the response properties of spike-frequency adapting interneurons switched with the background input's statistics. Slowly fluctuating background activity, as typical for sleep or quiet wakefulness, dramatically boosted the neurons' sensitivity to gamma and ripple frequencies. We developed a time-resolved dynamic gain analysis and revealed rapid sensitivity modulations that enable neurons to periodically boost gamma oscillations and ripples during specific phases of ongoing low-frequency oscillations. This mechanism predicts these prefrontal interneurons to be exquisitely sensitive to high-frequency ripples, especially during brain states characterized by slow rhythms, and to contribute substantially to theta-gamma cross-frequency coupling.

Lamotrigine Attenuates Neuronal Excitability, Depresses GABA Synaptic Inhibition, and Modulates Theta Rhythms in Rat Hippocampus

Theta oscillations generated in hippocampal (HPC) and cortical neuronal networks are involved in various aspects of brain function, including sensorimotor integration, movement planning, memory formation and attention. Disruptions of theta rhythms are present in individuals with brain disorders, including epilepsy and Alzheimer’s disease. Theta rhythm generation involves a specific interplay between cellular (ion channel) and network (synaptic) mechanisms. HCN channels are theta modulators, and several medications are known to enhance their activity. We investigated how different doses of lamotrigine (LTG), an HCN channel modulator, and antiepileptic and neuroprotective agent, would affect HPC theta rhythms in acute HPC slices (in vitro) and anaesthetized rats (in vivo). Whole-cell patch clamp recordings revealed that LTG decreased GABA_A-fast transmission in CA3 cells, in vitro. In addition, LTG directly depressed CA3 and CA1 pyramidal neuron excitability. These effects were partially blocked by ZD 7288, a selective HCN blocker, and are consistent with decreased excitability associated with antiepileptic actions. Lamotrigine depressed HPC theta oscillations in vitro, also consistent with its neuronal depressant effects. In contrast, it exerted an opposite, enhancing effect, on theta recorded in vivo. The contradictory in vivo and in vitro results indicate that LTG increases ascending theta activating medial septum/entorhinal synaptic inputs that over-power the depressant effects seen in HPC neurons. These results provide new insights into LTG actions and indicate an opportunity to develop more precise therapeutics for the treatment of dementias, memory disorders and epilepsy.

A mechanism for inter-areal coherence through communication based on connectivity and oscillatory power

Inter-areal coherence between field potentials is a widespread phenomenon in cortex. Coherence has been hypothesized to reflect phase-synchronization between oscillators and flexibly gate communication according to behavioral and cognitive demands. We reveal an alternative mechanism where coherence is not the cause but the consequence of communication and naturally emerges because spiking activity in a sending area causes post-synaptic potentials both in the same and in other areas. Consequently, coherence depends in a lawful manner on power and phase-locking in the sender and connectivity. Changes in oscillatory power explained prominent changes in fronto-parietal and LGN-V1 coherence across behavioral conditions. Optogenetic experiments and excitatory-inhibitory network simulations identified afferent synaptic inputs rather than spiking entrainment as the principal determinant of coherence. These findings suggest that unique spectral profiles of different brain areas automatically give rise to large-scale coherence patterns that follow anatomical connectivity and continuously reconfigure as a function of behavior and cognition.

The Relationship Between the Rhythmic Components of the Brain Electrical Activity During the Development of Status Epilepticus: An Operational Model of Brain Rhythms Generation

Background/Introduction: Despite the fact that brain rhythms are widely studied and officially classified, there is no consensus on their relationship, which can shed light on the genesis of rhythmic activity, its synchronization, functional role, and the formation of pathological reactions. Using the experimental status epilepticus (SE) as a model of brain in a hypersynchronized state with well-defined rhythms, we aimed to study the relationship between the rhythmic components of the brain electrical activity. Materials and Methods: Local field potentials (LFPs) were recorded simultaneously from the hippocampus, entorhinal cortex, medial septum, and amygdala during normal conditions and after kainic acid (KA) administration in waking guinea pigs. The dynamical spectral LFP properties were analyzed with the aid of Fast Fourier transform. Results: KA induces prominent SE with periodic combination of epileptiform discharge complexes and relatively quiet interdischarge intervals in the electrical activity of the brain. We have shown that new components appeared in the LFP spectra during the development of SE, representing a sequential doubling of the frequency, which had initially been dominating in the background records. Discussion: The phenomenon of frequency doubling can be interpreted as the octave principle of the LFP spectrum rhythmic carcass structure. The spectra of discharge complexes represent an alternation of harmonic spectra, where fundamental frequency coincides with one of the doubled frequencies dominating in the interdischarge activity. Using a nonlinear recurrent operation of rhythm multiplication and the obtained data we propose an operational model of the generation of rhythms and pathological discharges in the brain based on the octave principle. Impact statement In this study, we examined the relationship between the rhythmic components of the electrical activity of the limbic structures during the experimental status epilepticus and propose an operational model of brain rhythms generation based on the octave principle. Our study demonstrates that using fundamental principles (nonlinearity and the presence of recurrence), it is possible to explain the genesis and phenomenology of the electrical activity of brain structures in normal and pathological conditions.

Electroencephalography as a Non-Invasive Biomarker of Alzheimer's Disease: A Forgotten Candidate to Substitute CSF Molecules?

Biomarkers for disease diagnosis and prognosis are crucial in clinical practice. They should be objective and quantifiable and respond to specific therapeutic interventions. Optimal biomarkers should reflect the underlying process (pathological or not), be reproducible, widely available, and allow measurements repeatedly over time. Ideally, biomarkers should also be non-invasive and cost-effective. This review aims to focus on the usefulness and limitations of electroencephalography (EEG) in the search for Alzheimer's disease (AD) biomarkers. The main aim of this article is to review the evolution of the most used biomarkers in AD and the need for new peripheral and, ideally, non-invasive biomarkers. The characteristics of the EEG as a possible source for biomarkers will be revised, highlighting its advantages compared to the molecular markers available so far.

Ion-channel degeneracy: Multiple ion channels heterogeneously regulate intrinsic physiology of rat hippocampal granule cells

Degeneracy, the ability of multiple structural components to elicit the same characteristic functional properties, constitutes an elegant mechanism for achieving biological robustness. In this study, we sought electrophysiological signatures for the expression of ion-channel degeneracy in the emergence of intrinsic properties of rat hippocampal granule cells. We measured the impact of four different ion-channel subtypes-hyperpolarization-activated cyclic-nucleotide-gated (HCN), barium-sensitive inward rectifier potassium (Kir ), tertiapin-Q-sensitive inward rectifier potassium, and persistent sodium (NaP) channels-on 21 functional measurements employing pharmacological agents, and report electrophysiological data on two characteristic signatures for the expression of ion-channel degeneracy in granule cells. First, the blockade of a specific ion-channel subtype altered several, but not all, functional measurements. Furthermore, any given functional measurement was altered by the blockade of many, but not all, ion-channel subtypes. Second, the impact of blocking each ion-channel subtype manifested neuron-to-neuron variability in the quantum of changes in the electrophysiological measurements. Specifically, we found that blocking HCN or Ba-sensitive Kir channels enhanced action potential firing rate, but blockade of NaP channels reduced firing rate of granule cells. Subthreshold measures of granule cell intrinsic excitability (input resistance, temporal summation, and impedance amplitude) were enhanced by blockade of HCN or Ba-sensitive Kir channels, but were not significantly altered by NaP channel blockade. We confirmed that the HCN and Ba-sensitive Kir channels independently altered sub- and suprathreshold properties of granule cells through sequential application of pharmacological agents that blocked these channels. Finally, we found that none of the sub- or suprathreshold measurements of granule cells were significantly altered upon treatment with tertiapin-Q. Together, the heterogeneous many-to-many mapping between ion channels and single-neuron intrinsic properties emphasizes the need to account for ion-channel degeneracy in cellular- and network-scale physiology.

Membrane Resonance in Pyramidal and GABAergic Neurons of the Mouse Perirhinal Cortex

The perirhinal cortex (PRC) is a polymodal associative region of the temporal lobe that works as a gateway between cortical areas and hippocampus. In recent years, an increasing interest arose in the role played by the PRC in learning and memory processes, such as object recognition memory, in contrast with certain forms of hippocampus-dependent spatial and episodic memory. The integrative properties of the PRC should provide all necessary resources to select and enhance the information to be propagated to and from the hippocampus. Among these properties, we explore in this paper the ability of the PRC neurons to amplify the output voltage to current input at selected frequencies, known as membrane resonance. Within cerebral circuits the resonance of a neuron operates as a filter toward inputs signals at certain frequencies to coordinate network activity in the brain by affecting the rate of neuronal firing and the precision of spike timing. Furthermore, the ability of the PRC neurons to resonate could have a fundamental role in generating subthreshold oscillations and in the selection of cortical inputs directed to the hippocampus. Here, performing whole-cell patch-clamp recordings from perirhinal pyramidal neurons and GABAergic interneurons of GAD67-GFP⁺ mice, we found, for the first time, that the majority of PRC neurons are resonant at their resting potential, with a resonance frequency of 0.5–1.5 Hz at 23°C and of 1.5–2.8 Hz at 36°C. In the presence of ZD7288 (blocker of HCN channels) resonance was abolished in both pyramidal neurons and interneurons, suggesting that I_h current is critically involved in resonance generation. Otherwise, application of TTx (voltage-dependent Na⁺ channel blocker) attenuates the resonance in pyramidal neurons but not in interneurons, suggesting that only in pyramidal neurons the persistent sodium current has an amplifying effect. These experimental results have also been confirmed by a computational model. From a functional point of view, the resonance in the PRC would affect the reverberating activity between neocortex and hippocampus, especially during slow wave sleep, and could be involved in the redistribution and strengthening of memory representation in cortical regions.

Hippocampal Somatostatin Interneurons, Long-Term Synaptic Plasticity and Memory

A distinctive feature of the hippocampal structure is the diversity of inhibitory interneurons. These complex inhibitory interconnections largely contribute to the tight modulation of hippocampal circuitry, as well as to the formation and coordination of neuronal assemblies underlying learning and memory. Inhibitory interneurons provide more than a simple transitory inhibition of hippocampal principal cells (PCs). The synaptic plasticity of inhibitory neurons provides long-lasting changes in the hippocampal network and is a key component of memory formation. The dendrite targeting interneurons expressing the peptide somatostatin (SOM) are particularly interesting in this regard because they display unique long-lasting synaptic changes leading to metaplastic regulation of hippocampal networks. In this article, we examine the actions of the neuropeptide SOM on hippocampal cells, synaptic plasticity, learning, and memory. We address the different subtypes of hippocampal SOM interneurons. We describe the long-term synaptic plasticity that takes place at the excitatory synapses of SOM interneurons, its singular induction and expression mechanisms, as well as the consequences of these changes on the hippocampal network, learning, and memory. We also review evidence that astrocytes provide cell-specific dynamic regulation of inhibition of PC dendrites by SOM interneurons. Finally, we cover how, in mouse models of Alzheimer’s disease (AD), dysfunction of plasticity of SOM interneuron excitatory synapses may also contribute to cognitive impairments in brain disorders.

Impact of Perineuronal Nets on Electrophysiology of Parvalbumin Interneurons, Principal Neurons, and Brain Oscillations: A Review

Perineuronal nets (PNNs) are specialized extracellular matrix structures that surround specific neurons in the brain and spinal cord, appear during critical periods of development, and restrict plasticity during adulthood. Removal of PNNs can reinstate juvenile-like plasticity or, in cases of PNN removal during early developmental stages, PNN removal extends the critical plasticity period. PNNs surround mainly parvalbumin (PV)-containing, fast-spiking GABAergic interneurons in several brain regions. These inhibitory interneurons profoundly inhibit the network of surrounding neurons via their elaborate contacts with local pyramidal neurons, and they are key contributors to gamma oscillations generated across several brain regions. Among other functions, these gamma oscillations regulate plasticity associated with learning, decision making, attention, cognitive flexibility, and working memory. The detailed mechanisms by which PNN removal increases plasticity are only beginning to be understood. Here, we review the impact of PNN removal on several electrophysiological features of their underlying PV interneurons and nearby pyramidal neurons, including changes in intrinsic and synaptic membrane properties, brain oscillations, and how these changes may alter the integration of memory-related information. Additionally, we review how PNN removal affects plasticity-associated phenomena such as long-term potentiation (LTP), long-term depression (LTD), and paired-pulse ratio (PPR). The results are discussed in the context of the role of PV interneurons in circuit function and how PNN removal alters this function.

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.

Cortical hierarchy, dual counterstream architecture and the importance of top-down generative networks

Hierarchy is a major organizational principle of the cortex and underscores modern computational theories of cortical function. The local microcircuit amplifies long-distance inter-areal input, which show distance-dependent changes in their laminar profiles. Statistical modeling of these changes in laminar profiles demonstrates that inputs from multiple hierarchical levels to their target areas show remarkable consistency, allowing the construction of a cortical hierarchy based on a principle of hierarchical distance. The statistical modeling that is applied to structure can also be applied to laminar differences in the oscillatory coherence between areas thereby determining a functional hierarchy of the cortex. Close examination of the anatomy of inter-areal connectivity reveals a dual counterstream architecture with well-defined distance-dependent feedback and feedforward pathways in both the supra- and infragranular layers, suggesting a multiplicity of feedback pathways with well-defined functional properties. These findings are consistent with feedback connections providing a generative network involved in a wide range of cognitive functions. A dynamical model constrained by connectivity data sheds insight into the experimentally observed signatures of frequency-dependent Granger causality for feedforward versus feedback signaling. Concerted experiments capitalizing on recent technical advances and combining tract-tracing, high-resolution fMRI, optogenetics and mathematical modeling hold the promise of a much improved understanding of lamina-constrained mechanisms of neural computation and cognition. However, because inter-areal interactions involve cortical layers that have been the target of important evolutionary changes in the primate lineage, these investigations will need to include human and non-human primate comparisons.

Parvalbumin and Somatostatin Interneurons Contribute to the Generation of Hippocampal Gamma Oscillations

γ-frequency oscillations (30-120 Hz) in cortical networks influence neuronal encoding and information transfer, and are disrupted in multiple brain disorders. While synaptic inhibition is important for synchronization across the γ-frequency range, the role of distinct interneuronal subtypes in slow (<60 Hz) and fast γ states remains unclear. Here, we used optogenetics to examine the involvement of parvalbumin-expressing (PV⁺) and somatostatin-expressing (SST⁺) interneurons in γ oscillations in the mouse hippocampal CA3 ex vivo, using animals of either sex. Disrupting either PV⁺ or SST⁺ interneuron activity, via either photoinhibition or photoexcitation, led to a decrease in the power of cholinergically induced slow γ oscillations. Furthermore, photoexcitation of SST⁺ interneurons induced fast γ oscillations, which depended on both synaptic excitation and inhibition. Our findings support a critical role for both PV⁺ and SST⁺ interneurons in slow hippocampal γ oscillations, and further suggest that intense activation of SST⁺ interneurons can enable the CA3 circuit to generate fast γ oscillations.

SIGNIFICANCE STATEMENT The generation of hippocampal γ oscillations depends on synchronized inhibition provided by GABAergic interneurons. Parvalbumin-expressing (PV⁺) interneurons are thought to play the key role in coordinating the spike timing of excitatory pyramidal neurons, but the role distinct inhibitory circuits in network synchronization remains unresolved. Here, we show, for the first time, that causal disruption of either PV⁺ or somatostatin-expressing (SST⁺) interneuron activity impairs the generation of slow γ oscillations in the ventral hippocampus ex vivo. We further show that SST⁺ interneuron activation along with general network excitation is sufficient to generate high-frequency γ oscillations in the same preparation. These results affirm a crucial role for both PV⁺ and SST⁺ interneurons in hippocampal γ oscillation generation.

A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation

The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neural system. Harnessing state-of-the-art information technology, we have developed a cloud-based toolset for identifying empirical evidence from the scientific literature pertaining to synaptic electrophysiology, for extracting the experimental data of interest, and for linking each entry to relevant text or figure excerpts. Mining more than 1,200 published journal articles, we have identified eight different signal modalities quantified by 90 different methods to measure synaptic amplitude, kinetics, and plasticity in hippocampal neurons. We have designed a data structure that both reflects the differences and maintains the existing relations among experimental modalities. Moreover, we mapped every annotated experiment to identified potential connections, that is, specific pairs of presynaptic and postsynaptic neuron types. To this aim, we leveraged Hippocampome.org, an open-access knowledge base of morphologically, electrophysiologically, and molecularly characterized neuron types in the rodent hippocampal formation. Specifically, we have implemented a computational pipeline to systematically translate neuron type properties into formal queries in order to find all compatible potential connections. With this system, we have collected nearly 40,000 synaptic data entities covering 88% of the 3,120 potential connections in Hippocampome.org. Correcting membrane potentials with respect to liquid junction potentials significantly reduced the difference between theoretical and experimental reversal potentials, thereby enabling the accurate conversion of all synaptic amplitudes to conductance. This data set allows for large-scale hypothesis testing of the general rules governing synaptic signals. To illustrate these applications, we confirmed several expected correlations between synaptic measurements and their covariates while suggesting previously unreported ones. We release all data open-source at Hippocampome.org in order to further research across disciplines.

Dissociation of somatostatin and parvalbumin interneurons circuit dysfunctions underlying hippocampal theta and gamma oscillations impaired by amyloid β oligomers in vivo

Accumulation of amyloid β oligomers (AβO) in Alzheimer’s disease (AD) impairs hippocampal theta and gamma oscillations. These oscillations are important in memory functions and depend on distinct subtypes of hippocampal interneurons such as somatostatin-positive (SST) and parvalbumin-positive (PV) interneurons. Here, we investigated whether AβO causes dysfunctions in SST and PV interneurons by optogenetically manipulating them during theta and gamma oscillations in vivo in AβO-injected SST-Cre or PV-Cre mice. Hippocampal in vivo multi-electrode recordings revealed that optogenetic activation of channelrhodopsin-2 (ChR2)-expressing SST and PV interneurons in AβO-injected mice selectively restored AβO-induced reduction of the peak power of theta and gamma oscillations, respectively, and resynchronized CA1 pyramidal cell (PC) spikes. Moreover, SST and PV interneuron spike phases were resynchronized relative to theta and gamma oscillations, respectively. Whole-cell voltage-clamp recordings in CA1 PC in ex vivo hippocampal slices from AβO-injected mice revealed that optogenetic activation of SST and PV interneurons enhanced spontaneous inhibitory postsynaptic currents (IPSCs) selectively at theta and gamma frequencies, respectively. Furthermore, analyses of the stimulus–response curve, paired-pulse ratio, and short-term plasticity of SST and PV interneuron-evoked IPSCs ex vivo showed that AβO increased the initial GABA release probability to depress SST/PV interneuron’s inhibitory input to CA1 PC selectively at theta and gamma frequencies, respectively. Our results reveal frequency-specific and interneuron subtype-specific presynaptic dysfunctions of SST and PV interneurons’ input to CA1 PC as the synaptic mechanisms underlying AβO-induced impairments of hippocampal network oscillations and identify them as potential therapeutic targets for restoring hippocampal network oscillations in early AD.

Gradients in the mammalian cerebellar cortex enable Fourier-like transformation and improve storing capacity

Cerebellar granule cells (GCs) make up the majority of all neurons in the vertebrate brain, but heterogeneities among GCs and potential functional consequences are poorly understood. Here, we identified unexpected gradients in the biophysical properties of GCs in mice. GCs closer to the white matter (inner-zone GCs) had higher firing thresholds and could sustain firing with larger current inputs than GCs closer to the Purkinje cell layer (outer-zone GCs). Dynamic Clamp experiments showed that inner- and outer-zone GCs preferentially respond to high- and low-frequency mossy fiber inputs, respectively, enabling dispersion of the mossy fiber input into its frequency components as performed by a Fourier transformation. Furthermore, inner-zone GCs have faster axonal conduction velocity and elicit faster synaptic potentials in Purkinje cells. Neuronal network modeling revealed that these gradients improve spike-timing precision of Purkinje cells and decrease the number of GCs required to learn spike-sequences. Thus, our study uncovers biophysical gradients in the cerebellar cortex enabling a Fourier-like transformation of mossy fiber inputs.

Excitatory/Inhibitory Responses Shape Coherent Neuronal Dynamics Driven by Optogenetic Stimulation in the Primate Brain

Coherent neuronal dynamics play an important role in complex cognitive functions. Optogenetic stimulation promises to provide new ways to test the functional significance of coherent neural activity. However, the mechanisms by which optogenetic stimulation drives coherent dynamics remain unclear, especially in the nonhuman primate brain. Here, we perform computational modeling and experiments to study the mechanisms of optogenetic-stimulation-driven coherent neuronal dynamics in three male nonhuman primates. Neural responses arise from stimulation-evoked, temporally dynamic excitatory (E) and inhibitory (I) activity. Spiking activity is more likely to occur during E/I imbalances. Thus the relative difference in the driven E and I responses precisely controls spike timing by forming a brief time interval of increased spiking likelihood. Experimental results agree with parameter-dependent predictions from the computational models. These results demonstrate that optogenetic stimulation driven coherent neuronal dynamics are governed by the temporal properties of E/I activity. Transient imbalances in excitatory and inhibitory activity may provide a general mechanism for generating coherent neuronal dynamics without the need for an oscillatory generator.SIGNIFICANCE STATEMENT We examine how coherent neuronal dynamics arise from optogenetic stimulation in the primate brain. Using computational models and experiments, we demonstrate that coherent spiking and local field potential activity is generated by stimulation-evoked responses of excitatory and inhibitory activity in networks, extending the growing literature on neuronal dynamics. These responses create brief time intervals of increased spiking tendency and are consistent with previous observations in the literature that balanced excitation and inhibition controls spike timing, suggesting that optogenetic-stimulation-driven coherence may arise from intrinsic E/I balance. Most importantly, our results are obtained in nonhuman primates and thus will play a leading role in driving the use of causal manipulations with optogenetic tools to study higher cognitive functions in the primate brain.

Heterogeneities in intrinsic excitability and frequency-dependent response properties of granule cells across the blades of the rat dentate gyrus

The dentate gyrus (DG), the input gate to the hippocampus proper, is anatomically segregated into three different sectors, namely, the suprapyramidal blade, the crest region, and the infrapyramidal blade. Although there are well-established differences between these sectors in terms of neuronal morphology, connectivity patterns, and activity levels, differences in electrophysiological properties of granule cells within these sectors have remained unexplored. Here, employing somatic whole cell patch-clamp recordings from the rat DG, we demonstrate that granule cells in these sectors manifest considerable heterogeneities in their intrinsic excitability, temporal summation, action potential characteristics, and frequency-dependent response properties. Across sectors, these neurons showed positive temporal summation of their responses to inputs mimicking excitatory postsynaptic currents and showed little to no sag in their voltage responses to pulse currents. Consistently, the impedance amplitude profile manifested low-pass characteristics and the impedance phase profile lacked positive phase values at all measured frequencies and voltages and for all sectors. Granule cells in all sectors exhibited class I excitability, with broadly linear firing rate profiles, and granule cells in the crest region fired significantly fewer action potentials compared with those in the infrapyramidal blade. Finally, we found weak pairwise correlations across the 18 different measurements obtained individually from each of the three sectors, providing evidence that these measurements are indeed reporting distinct aspects of neuronal physiology. Together, our analyses show that granule cells act as integrators of afferent information and emphasize the need to account for the considerable physiological heterogeneities in assessing their roles in information encoding and processing.NEW & NOTEWORTHY We employed whole cell patch-clamp recordings from granule cells in the three subregions of the rat dentate gyrus to demonstrate considerable heterogeneities in their intrinsic excitability, temporal summation, action potential characteristics, and frequency-dependent response properties. Across sectors, granule cells did not express membrane potential resonance, and their impedance profiles lacked inductive phase leads at all measured frequencies. Our analyses also show that granule cells manifest class I excitability characteristics, categorizing them as integrators of afferent information.

A Distinct Class of Bursting Neurons with Strong Gamma Synchronization and Stimulus Selectivity in Monkey V1

Cortical computation depends on interactions between excitatory and inhibitory neurons. The contributions of distinct neuron types to sensory processing and network synchronization in primate visual cortex remain largely undetermined. We show that in awake monkey V1, there exists a distinct cell type (››30% of neurons) that has narrow-waveform (NW) action potentials and high spontaneous discharge rates and fires in high-frequency bursts. These neurons are more stimulus selective and phase locked to 30- to 80-Hz gamma oscillations than other neuron types. Unlike other neuron types, their gamma-phase locking is highly predictive of orientation tuning. We find evidence for strong rhythmic inhibition in these neurons, suggesting that they interact with interneurons to act as excitatory pacemakers for the V1 gamma rhythm. We did not find a similar class of NW bursting neurons in L2-L4 of mouse V1. Given its properties, this class of NW bursting neurons should be pivotal for the encoding and transmission of stimulus information.

Generating Brain Waves, the Power of Astrocytes

Synchronization of neuronal activity in the brain underlies the emergence of neuronal oscillations termed “brain waves”, which serve various physiological functions and correlate with different behavioral states. It has been postulated that at least ten distinct mechanisms are involved in the formulation of these brain waves, including variations in the concentration of extracellular neurotransmitters and ions, as well as changes in cellular excitability. In this mini review we highlight the contribution of astrocytes, a subtype of glia, in the formation and modulation of brain waves mainly due to their close association with synapses that allows their bidirectional interaction with neurons, and their syncytium-like activity via gap junctions that facilitate communication to distal brain regions through Ca²⁺ waves. These capabilities allow astrocytes to regulate neuronal excitability via glutamate uptake, gliotransmission and tight control of the extracellular K⁺ levels via a process termed K⁺ clearance. Spatio-temporal synchrony of activity across neuronal and astrocytic networks, both locally and distributed across cortical regions, underpins brain states and thereby behavioral states, and it is becoming apparent that astrocytes play an important role in the development and maintenance of neural activity underlying these complex behavioral states.

Degeneracy in hippocampal physiology and plasticity

Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis that point to the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross-interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding-homeostasis system in accomplishing its tasks in an input- and state-dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals.

The critical role of persistent sodium current in hippocampal gamma oscillations

Gamma network oscillations in the brain are fast rhythmic network oscillations in the gamma frequency range (~30-100 Hz), playing key roles in the hippocampus for learning, memory, and spatial processing. There is evidence indicating that GABAergic interneurons, including parvalbumin-expressing basket cells (PVBCs), contribute to cortical gamma oscillations through synaptic interactions with excitatory cells. However, the molecular, cellular, and circuit underpinnings underlying generation and maintenance of cortical gamma oscillations are largely elusive. Recent studies demonstrated that intrinsic and synaptic properties of GABAergic interneurons and excitatory cells are regulated by a slowly inactivating or non-inactivating sodium current (i.e., persistent sodium current, I_NaP), suggesting that I_NaP is involved in gamma oscillations. Here, we tested whether I_NaP plays a role in hippocampal gamma oscillations using pharmacological, optogenetic, and electrophysiological approaches. We found that I_NaP blockers, phenytoin (40 μM and 100 μM) and riluzole (10 μM), reduced gamma oscillations induced by optogenetic stimulation of CaMKII-expressing cells in CA1 networks. Whole-cell patch-clamp recordings further demonstrated that phenytoin (100 μM) reduced I_NaP and firing frequencies in both PVBCs and pyramidal cells without altering threshold and amplitude of action potentials, but increased rheobase in both cell types. These results suggest that I_NaP in pyramidal cells and PVBCs is required for hippocampal gamma oscillations, supporting a pyramidal-interneuron network gamma model. Phenytoin-mediated modulation of hippocampal gamma oscillations may be a mechanism underlying its anticonvulsant efficacy, as well as its contribution to cognitive impairments in epilepsy patients.

Frequency-dependent responses of neuronal models to oscillatory inputs in current versus voltage clamp

Action potential generation in neurons depends on a membrane potential threshold and therefore on how subthreshold inputs influence this voltage. In oscillatory networks, for example, many neuron types have been shown to produce membrane potential ([Formula: see text]) resonance: a maximum subthreshold response to oscillatory inputs at a nonzero frequency. Resonance is usually measured by recording [Formula: see text] in response to a sinusoidal current ([Formula: see text]), applied at different frequencies (f), an experimental setting known as current clamp (I-clamp). Several recent studies, however, use the voltage clamp (V-clamp) method to control [Formula: see text] with a sinusoidal input at different frequencies [[Formula: see text]] and measure the total membrane current ([Formula: see text]). The two methods obey systems of differential equations of different dimensionality, and while I-clamp provides a measure of electrical impedance [[Formula: see text]], V-clamp measures admittance [[Formula: see text]]. We analyze the relationship between these two measurement techniques. We show that, despite different dimensionality, in linear systems the two measures are equivalent: [Formula: see text]. However, nonlinear model neurons produce different values for Z and [Formula: see text]. In particular, nonlinearities in the voltage equation produce a much larger difference between these two quantities than those in equations of recovery variables that describe activation and inactivation kinetics. Neurons are inherently nonlinear, and notably, with ionic currents that amplify resonance, the voltage clamp technique severely underestimates the current clamp response. We demonstrate this difference experimentally using the PD neurons in the crab stomatogastric ganglion. These findings are instructive for researchers who explore cellular mechanisms of neuronal oscillations.

Frequency-dependent entrainment of striatal fast-spiking interneurons

Striatal fast-spiking interneurons (FSIs) fire in variable-length runs of action potentials at 20-200 spikes/s separated by pauses. In vivo, or with fluctuating applied current, both runs and pauses become briefer and more variable. During runs, spikes are entrained specifically to gamma-frequency components of the input fluctuations. We stimulated parvalbumin-expressing striatal FSIs in mouse brain slices with broadband noise currents added to direct current steps and measured spike entrainment across all frequencies. As the constant current level was increased, FSIs produced longer runs and showed sharper frequency tuning, with best entrainment at the stimulus frequency matching their intrarun firing rate. We separated the contributions of previous spikes from that of the fluctuating stimulus, revealing a strong contribution of previous action potentials to gamma-frequency entrainment. In contrast, after subtraction of the effect inherited from the previous spike, the remaining stimulus contribution to spike generation was less sharply tuned, showing a larger contribution of lower frequencies. The frequency specificity of entrainment within a run was reproduced with a phase resetting model based on experimentally measured phase resetting curves of the same FSIs. In the model, broadly tuned phase entrainment for the first spike in a run evolved into sharply tuned gamma entrainment over the next few spikes. The data and modeling results indicate that for FSIs firing in brief runs and pauses firing within runs is entrained by gamma-frequency components of the input, whereas the onset timing of runs may be sensitive to a wider range of stimulus frequency components.NEW & NOTEWORTHY Specific types of neurons entrain their spikes to particular oscillation frequencies in their synaptic input. This entrainment is commonly understood in terms of the subthreshold voltage response, but how this translates to spiking is not clear. We show that in striatal fast-spiking interneurons, entrainment to gamma-frequency input depends on rhythmic spike runs and is explained by the phase resetting curve, whereas run initiation can be triggered by a broad range of input frequencies.

Human cerebrospinal fluid promotes spontaneous gamma oscillations in the hippocampus in vitro

Gamma oscillations (30-80 Hz) are fast network activity patterns frequently linked to cognition. They are commonly studied in hippocampal brain slices in vitro, where they can be evoked via pharmacological activation of various receptor families. One limitation of this approach is that neuronal activity is studied in a highly artificial extracellular fluid environment, as provided by artificial cerebrospinal fluid (aCSF). Here, we examine the influence of human cerebrospinal fluid (hCSF) on kainate-evoked and spontaneous gamma oscillations in mouse hippocampus. We show that hCSF, as compared to aCSF of matched electrolyte and glucose composition, increases the power of kainate-evoked gamma oscillations and induces spontaneous gamma activity in areas CA3 and CA1 that is reversed by washout. Bath application of atropine entirely abolished hCSF-induced gamma oscillations, indicating critical contribution from muscarinic acetylcholine receptor-mediated signaling. In separate whole-cell patch clamp recordings from rat hippocampus, hCSF increased theta resonance frequency and strength in pyramidal cells along with enhancement of h-current (I_h ) amplitude. We found no evidence of intrinsic gamma frequency resonance at baseline (aCSF) among fast-spiking interneurons, and this was not altered by hCSF. However, hCSF increased the excitability of fast-spiking interneurons, which likely contributed to gamma rhythmogenesis. Our findings show that hCSF promotes network gamma oscillations in the hippocampus in vitro and suggest that neuromodulators distributed in CSF could have significant influence on neuronal network activity in vivo.

A diversity of interneurons and Hebbian plasticity facilitate rapid compressible learning in the hippocampus

The hippocampus is able to rapidly learn incoming information, even if that information is only observed once. Furthermore, this information can be replayed in a compressed format in either forward or reverse modes during sharp wave-ripples (SPW-Rs). We leveraged state-of-the-art techniques in training recurrent spiking networks to demonstrate how primarily interneuron networks can achieve the following: (1) generate internal theta sequences to bind externally elicited spikes in the presence of inhibition from the medial septum; (2) compress learned spike sequences in the form of a SPW-R when septal inhibition is removed; (3) generate and refine high-frequency assemblies during SPW-R-mediated compression; and (4) regulate the inter-SPW interval timing between SPW-Rs in ripple clusters. From the fast timescale of neurons to the slow timescale of behaviors, interneuron networks serve as the scaffolding for one-shot learning by replaying, reversing, refining, and regulating spike sequences.

Membrane potential resonance in non-oscillatory neurons interacts with synaptic connectivity to produce network oscillations

Several neuron types have been shown to exhibit (subthreshold) membrane potential resonance (MPR), defined as the occurrence of a peak in their voltage amplitude response to oscillatory input currents at a preferred (resonant) frequency. MPR has been investigated both experimentally and theoretically. However, whether MPR is simply an epiphenomenon or it plays a functional role for the generation of neuronal network oscillations and how the latent time scales present in individual, non-oscillatory cells affect the properties of the oscillatory networks in which they are embedded are open questions. We address these issues by investigating a minimal network model consisting of (i) a non-oscillatory linear resonator (band-pass filter) with 2D dynamics, (ii) a passive cell (low-pass filter) with 1D linear dynamics, and (iii) nonlinear graded synaptic connections (excitatory or inhibitory) with instantaneous dynamics. We demonstrate that (i) the network oscillations crucially depend on the presence of MPR in the resonator, (ii) they are amplified by the network connectivity, (iii) they develop relaxation oscillations for high enough levels of mutual inhibition/excitation, and (iv) the network frequency monotonically depends on the resonators resonant frequency. We explain these phenomena using a reduced adapted version of the classical phase-plane analysis that helps uncovering the type of effective network nonlinearities that contribute to the generation of network oscillations. We extend our results to networks having cells with 2D dynamics. Our results have direct implications for network models of firing rate type and other biological oscillatory networks (e.g, biochemical, genetic).