Bursting Neurons in the Hippocampal Formation Encode Features of LFP Rhythms

Deep learning based decoding of single local field potential events

How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. Thus, we here demonstrate that an unsupervised machine learning approach can be used to extract meaningful information from electro-physiological recordings on a single-trial basis. We use an auto-encoder network to reduce the dimensions of single local field potential (LFP) events to create interpretable clusters of different neural activity patterns. Strikingly, certain LFP shapes correspond to latency differences in different recording channels. Hence, LFP shapes can be used to determine the direction of information flux in the cerebral cortex. Furthermore, after clustering, we decoded the cluster centroids to reverse-engineer the underlying prototypical LFP event shapes. To evaluate our approach, we applied it to both extra-cellular neural recordings in rodents, and intra-cranial EEG recordings in humans. Finally, we find that single channel LFP event shapes during spontaneous activity sample from the realm of possible stimulus evoked event shapes. A finding which so far has only been demonstrated for multi-channel population coding.

SWCNTs/PEDOT:PSS nanocomposites-modified microelectrode arrays for revealing locking relations between burst and local field potential in cultured cortical networks

Burst and local field potential (LFP) are fundamental components of brain activity, representing fast and slow rhythms, respectively. Understanding the intricate relationship between burst and LFP is crucial for deciphering the underlying mechanisms of brain dynamics. In this study, we fabricated high-performance microelectrode arrays (MEAs) using the SWCNTs/PEDOT:PSS nanocomposites, which exhibited favorable electrical properties (low impedance: 12.8 ± 2.44 kΩ) and minimal phase delay (-11.96 ± 1.64°). These MEAs enabled precise exploration of the burst-LFP interaction in cultured cortical networks. After a 14-day period of culture, we used the MEAs to monitor electrophysiological activities and revealed a time-locking relationship between burst and LFP, indicating the maturation of the neural network. To further investigate this relationship, we modulated burst firing patterns by treating the neural culture with increasing concentrations of glycine. The results indicated that glycine effectively altered burst firing patterns, with both duration and spike count increasing as the concentration rose. This was accompanied by an enhanced level of time-locking between burst and LFP but a decrease in synchrony among neurons. This study not only highlighted the pivotal role of SWCNTs/PEDOT:PSS-modified MEAs in elucidating the interaction between burst and LFP, bridging the gap between slow and fast brain rhythms in vitro but also provides valuable insights into the potential therapeutic strategies targeting neurological disorders associated with abnormal rhythm generation.

The QBIT theory of consciousness: Entropy and qualia

The QBIT theory is a recently introduced multi-disciplinary approach to the problem of consciousness. One of the main axioms of the theory is that when information-theoretic certainty of an observer about a stimulus goes beyond a certain threshold, the observer becomes conscious of that stimulus. This axiom could provide an explanation for how the brain generates consciousness.In short, the QBIT theory suggests that the brain generates consciousness by reducing the entropy of its internal representations below a critical threshold. This paper explains how the brain gradually minimizes the entropy of its internal representations and consequently generate minimum-entropy representations (also known as conscious representations or qualia). The paper also explores the consequences of this entropy-minimization process in the context of quantum information theory.

Fentanyl effects on respiratory neuron activity in the dorsolateral pons

Opioids suppress breathing through actions in the brainstem, including respiratory-related areas of the dorsolateral pons, which contain multiple phenotypes of respiratory patterned neurons. The discharge identity of dorsolateral pontine neurons that are impacted by opioids is unknown. To address this, single neuronal units were recorded in the dorsolateral pons of arterially perfused in situ rat preparations that were perfused with an apneic concentration of the opioid agonist fentanyl, followed by the opioid antagonist naloxone (NLX). Dorsolateral pontine neurons were categorized based on respiratory-associated discharge patterns, which were differentially affected by fentanyl. Inspiratory neurons and a subset of inspiratory/expiratory phase-spanning neurons were either silenced or had reduced firing frequency during fentanyl-induced apnea, which was reversed upon administration of naloxone. In contrast, the majority of expiratory neurons continued to fire tonically during fentanyl-induced apnea, albeit with reduced firing frequency. In addition, pontine late-inspiratory and postinspiratory neuronal activity were absent from apneustic-like breaths during the transition to fentanyl-induced apnea and the naloxone-mediated transition to recovery. Thus, opioid-induced deficits in respiratory patterning may occur due to reduced activity of pontine inspiratory neurons, whereas apnea occurs with loss of all phasic pontine activity and sustained tonic expiratory neuron activity.NEW & NOTEWORTHY Opioids can suppress breathing via actions throughout the brainstem, including the dorsolateral pons. The respiratory phenotype of dorsolateral pontine neurons inhibited by opioids is unknown. Here, we describe the effect of the highly potent opioid fentanyl on the firing activity of these dorsolateral pontine neurons. Inspiratory neurons were largely silenced by fentanyl, whereas expiratory neurons were not. We provide a framework whereby this differential sensitivity to fentanyl can contribute to respiratory pattern deficits and apnea.

Topography in the Bursting Dynamics of Entorhinal Neurons

Medial entorhinal cortex contains neural substrates for representing space. These substrates include grid cells that fire in repeating locations and increase in scale progressively along the dorsal-to-ventral entorhinal axis, with the physical distance between grid firing nodes increasing from tens of centimeters to several meters in rodents. Whether the temporal scale of grid cell spiking dynamics shows a similar dorsal-to-ventral organization remains unknown. Here, we report the presence of a dorsal-to-ventral gradient in the temporal spiking dynamics of grid cells in behaving mice. This gradient in bursting supports the emergence of a dorsal grid cell population with a high signal-to-noise ratio. In vitro recordings combined with a computational model point to a role for gradients in non-inactivating sodium conductances in supporting the bursting gradient in vivo. Taken together, these results reveal a complementary organization in the temporal and intrinsic properties of entorhinal cells.

In Vivo Neuroelectrophysiological Monitoring of Atomically Precise Au25 Clusters at an Ultrahigh Injected Dose

Atomically precise Au₂₅(SG)₁₈ clusters have shown great promise in near-infrared II cerebrovascular imaging, X-ray imaging, and cancer radiotherapy due to their high atomic number, unique molecular-like electronic structure, and renal clearable properties. Therefore, it is important to study the in vivo toxicity of Au₂₅ clusters. Unfortunately, previous toxicological investigations focused on low injected doses (<100 mg kg^-1) and routine research methods, such as blood chemistry and biochemistry, which cannot reflect neurotoxicity or tiny changes in neural activity. In this work, in vivo neuroelectrophysiology of Au₂₅ clusters at ultrahigh injected doses (200, 300, and 500 mg kg^-1) was investigated. Local field potential showed that the Au₂₅-treated mice showed a spike in delta rhythm and moved to lower frequency over time. The power spectrum showed a 38.3% reduction in the peak value at 10 h post-injection of Au₂₅ clusters compared with 3 h post-injection, which gradually became close to the normal level, indicating no permanent damage to the nervous system. Moreover, no significant structural changes were found in both neurons and glial cells at the histological level. These results of in vivo neuroelectrophysiology will encourage scientists to make more exciting discoveries on nervous system diseases by employing Au₂₅ clusters even at ultrahigh injected doses.

Optogenetic "low-theta" pacing of the septohippocampal circuit is sufficient for spatial goal finding and is influenced by behavioral state and cognitive demand

Hippocampal theta oscillations show prominent changes in frequency and amplitude depending on behavioral state or cognitive demands. How these dynamic changes in theta oscillations contribute to the spatial and temporal organization of hippocampal cells, and ultimately behavior, remain unclear. We used low-theta frequency optogenetic stimulation to pace coordination of cellular and network activity between the medial septum (MS) and hippocampus during baseline and MS stimulation while rats were at rest or performing a spatial accuracy task with a visible or hidden goal zone. Hippocampal receptivity to pan-neuronal septal stimulation at low-theta frequency was primarily determined by speed and secondarily by task demands. Competition between artificial and endogenous field potentials at theta frequency attenuated hippocampal phase preference relative to local theta, but the spike-timing activity of hippocampal pyramidal cells was effectively driven by artificial septal output, particularly during the hidden goal task. Notwithstanding temporal reorganization by artificial theta stimulation, place field properties were unchanged and alterations to spatial behavior were limited to goal zone approximation. Our results indicate that even a low-theta frequency timing signal in the septohippocampal circuit is sufficient for spatial goal finding behavior. The results also advance a mechanistic understanding of how endogenous or artificial somatodendritic timing signals relate to displacement computations during navigation and spatial memory.

Anatomy and Function of the Primate Entorhinal Cortex

The entorhinal cortex (EC) is a critical element of the hippocampal formation located within the medial temporal lobe (MTL) in primates. The EC has historically received attention for being the primary mediator of cortical information going into and coming from the hippocampus proper. In this review, we highlight the significance of the EC as a major player in memory processing, along with other associated structures in the primate MTL. The complex, convergent topographies of cortical and subcortical input to the EC, combined with short-range intrinsic connectivity and the selective targeting of EC efferents to the hippocampus, provide evidence for subregional specialization and integration of information beyond what would be expected if this structure were a simple conduit of information for the hippocampus. Lesion studies of the EC provide evidence implicating this region as critical for memory and the flexible use of complex relational associations between experienced events. The physiology of this structure's constituent principal cells mirrors the complexity of its anatomy. EC neurons respond preferentially to aspects of memory-dependent paradigms including object, place, and time. EC neurons also show striking spatial representations as primates explore visual space, similar to those identified in rodents navigating physical space. In this review, we highlight the great strides that have been made toward furthering our understanding of the primate EC, and we identify paths forward for future experiments to provide additional insight into the role of this structure in learning and memory.

Emergence of Mixed Mode Oscillations in Random Networks of Diverse Excitable Neurons: The Role of Neighbors and Electrical Coupling

In this paper, we focus on the emergence of diverse neuronal oscillations arising in a mixed population of neurons with different excitability properties. These properties produce mixed mode oscillations (MMOs) characterized by the combination of large amplitudes and alternate subthreshold or small amplitude oscillations. Considering the biophysically plausible, Izhikevich neuron model, we demonstrate that various MMOs, including MMBOs (mixed mode bursting oscillations) and synchronized tonic spiking appear in a randomly connected network of neurons, where a fraction of them is in a quiescent (silent) state and the rest in self-oscillatory (firing) states. We show that MMOs and other patterns of neural activity depend on the number of oscillatory neighbors of quiescent nodes and on electrical coupling strengths. Our results are verified by constructing a reduced-order network model and supported by systematic bifurcation diagrams as well as for a small-world network. Our results suggest that, for weak couplings, MMOs appear due to the de-synchronization of a large number of quiescent neurons in the networks. The quiescent neurons together with the firing neurons produce high frequency oscillations and bursting activity. The overarching goal is to uncover a favorable network architecture and suitable parameter spaces where Izhikevich model neurons generate diverse responses ranging from MMOs to tonic spiking.

Sharp-wave ripple features in macaques depend on behavioral state and cell-type specific firing

Sharp-wave ripples (SWRs) are spontaneous, synchronized neural population events in the hippocampus widely thought to play a role in memory consolidation and retrieval. They occur predominantly in sleep and quiet immobility, and in primates, they also appear during active visual exploration. Typical measures of SWRs in behaving rats include changes in the rate of occurrence, or in the incidence of specific neural ensemble activity contained within the categorical SWR event. Much less is known about the relevance of spatiotemporal SWR features, though they may index underlying activity of specific cell types including ensemble-specific internally generated sequences. Furthermore, changes in SWR features during active exploratory states are unknown. In this study, we recorded hippocampal local-field potentials and single-units during periods of quiescence and as macaques performed a memory-guided visual search task. We observed that (a) ripples during quiescence have greater amplitudes and larger postripple waves (PRW) compared to those in task epochs, and (b) during "remembered" trials, ripples have larger amplitudes than during "forgotten" trials, with no change in duration or PRWs. We further found that spiking activity influences SWR features as a function of cell type and ripple timing. As expected, larger ripple amplitudes were associated with putative pyramidal or putative basket interneuron (IN) activity, even when the spikes in question exceed the duration of the ripple. In contrast, the PRW was attenuated with activity from low firing rate cells and enhanced with activity from high firing rate cells, with putative IN spikes during ripples leading to the most prominent PRW peaks. The selective changes in SWR features as a function of time window, cell type, and cognitive/vigilance states suggest that this mesoscopic field event can offer additional information about the local network and animal's state than would be appreciated from SWR event rates alone.

Information-Theoretical Analysis of the Neural Code in the Rodent Temporal Lobe

In the study of the neural code, information-theoretical methods have the advantage of making no assumptions about the probabilistic mapping between stimuli and responses. In the sensory domain, several methods have been developed to quantify the amount of information encoded in neural activity, without necessarily identifying the specific stimulus or response features that instantiate the code. As a proof of concept, here we extend those methods to the encoding of kinematic information in a navigating rodent. We estimate the information encoded in two well-characterized codes, mediated by the firing rate of neurons, and by the phase-of-firing with respect to the theta-filtered local field potential. In addition, we also consider a novel code, mediated by the delta-filtered local field potential. We find that all three codes transmit significant amounts of kinematic information, and informative neurons tend to employ a combination of codes. Cells tend to encode conjunctions of kinematic features, so that most of the informative neurons fall outside the traditional cell types employed to classify spatially-selective units. We conclude that a broad perspective on the candidate stimulus and response features expands the repertoire of strategies with which kinematic information is encoded.

Neural Coding With Bursts-Current State and Future Perspectives

Neuronal action potentials or spikes provide a long-range, noise-resistant means of communication between neurons. As point processes single spikes contain little information in themselves, i.e., outside the context of spikes from other neurons. Moreover, they may fail to cross a synapse. A burst, which consists of a short, high frequency train of spikes, will more reliably cross a synapse, increasing the likelihood of eliciting a postsynaptic spike, depending on the specific short-term plasticity at that synapse. Both the number and the temporal pattern of spikes in a burst provide a coding space that lies within the temporal integration realm of single neurons. Bursts have been observed in many species, including the non-mammalian, and in brain regions that range from subcortical to cortical. Despite their widespread presence and potential relevance, the uncertainties of how to classify bursts seems to have limited the research into the coding possibilities for bursts. The present series of research articles provides new insights into the relevance and interpretation of bursts across different neural circuits, and new methods for their analysis. Here, we provide a succinct introduction to the history of burst coding and an overview of recent work on this topic.