Kamran Diba

Psilocybin is a psychedelic which has been shown to induce neural plasticity through activation of intracellular serotonergic 5-HT2A receptors. It also produces brain-wide changes in structural and functional connectivity and holds promise as a therapeutic compound for treating anxiety and depression. Despite links between psilocybin-induced plasticity, the psychedelic experience, and reduction in depressive symptoms, little is known about the effects of psilocybin on the function of the highly plastic hippocampus, a region crucial for memory whose dysfunction is linked to neural disorders such as depression and anxiety. In this study, we investigated the acute and lasting effects of psilocybin on rodent sharp-wave ripples (SWRs), transient high frequency oscillations observable in the hippocampal local field potential which are linked to memory consolidation. We found that a 10 mg/kg dose of psilocybin robustly decreased the peak SWR frequency and increased the duration of SWRs immediately following administration compared to control sessions the day before and after. Psilocybin also perturbed sleep architecture, resulting in a pronounced reduction in non-rapid eye movement (NREM) sleep which lasted for hours. Therefore, psilocybin could impact memory processing by modulating hippocampal SWRs.

Hippocampal replay is now considered to be a cornerstone of memory consolidation, yet the synaptic plasticity rules governing its dynamics remain elusive. Under the standard asymmetric Hebbian spike‐timing dependent plasticity (STDP) model, the same spike patterns that promote activity propagation along one direction of sequential activation undermine propagation in the reverse direction, compromising “bidirectional” replay. On the other hand, symmetric potentiation rules, as recently proposed for region CA3, risk corrupting the memory trace by saturating synaptic weights. Using Ecker et al.'s recurrent network model of place cells that spontaneously generate replays during ripples, we systematically investigated how different STDP plasticity rules modulate offline replays. We developed a classification framework to study the mechanisms relating different STDP kernels to key replay characteristics, including directionality, speed, and stability. Our results confirmed that symmetric potentiation rules during offline states saturate synapses, inducing rigid attractors that corrupt the memory trace, and that an asymmetric Hebbian STDP kernel induces strong biases in the directionality of replay, leading to rapid replay acceleration and replay degradation. Notably, we found that an asymmetric anti‐Hebbian STDP kernel preserves replay bi‐directionality and stabilizes replay speed. We further identified the negative timing component of the STDP rule as the primary driver of replay speed: potentiation causes deceleration, while depression causes acceleration. These findings provide a mechanistic explanation for empirically observed replay deceleration and suggest a role for anti‐Hebbian synaptic depression in stabilizing replay dynamics.

Slow waves are the main oscillatory activity during non-rapid eye movement sleep, playing a crucial role in synaptic homeostasis, memory consolidation, and cortical network regulation. In urethane-anesthetized rodents, frequently used in long-term potentiation experiments in vivo, brain activity alternates between a deactivated state, dominated by slow waves, and an activated state characterized by faster oscillations. However, the interplay between synaptic plasticity and slow wave dynamics within these fluctuating brain states remains unclear. Here, we investigated whether long-term potentiation in the hippocampal–medial prefrontal cortex (CA1-mPFC) circuit, critical for memory processing, modulates cortical slow wave dynamics during urethane anesthesia. Electrical stimulation of CA1 robustly evoked a transient (50 to 200 ms) event of population silence (down states) in the medial prefrontal cortex, which emerged after the typical evoked field post-synaptic potential, and was consistently associated with a slow waves. Importantly, long-term potentiation induction significantly enhanced the CA1-evoked medial prefrontal cortex down states and slowed spontaneous cortical slow wave oscillations, without altering the activated/deactivated brain state proportion. Our findings provide direct evidence of an interaction between synaptic plasticity and cortical slow wave dynamics, laying the groundwork for future studies on the underlying mechanisms of state-dependent synaptic homeostasis.

Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural pattern reactivation from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson gaussian-process latent variable model (P-GPLVM; Wu et al., 2017) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the learned latent space, limiting its ability for estimating the neural reactivation. Here, we extend the P-GPLVM to enable the latent variable inference of new data constrained by previously learned smoothness and mapping information. We also describe a principled approach for the constrained latent variable inference for temporally compressed patterns of activity, such as those found in population burst events during hippocampal sharp-wave ripples, as well as metrics for assessing the validity of neural pattern reactivation and inferring the encoded experience. Applying these approaches to hippocampal ensemble recordings during active maze exploration, we replicate the result that P-GPLVM learns a latent space encoding the animal’s position. We further demonstrate that this latent space can differentiate one maze context from another. By inferring the latent variables of new neural data during running, certain neural patterns are observed to reactivate, in accordance with the similarity of experiences encoded by its nearby neural trajectories in the training data manifold. Finally, reactivation of neural patterns can be estimated for neural activity during population burst events as well, allowing the identification for replay events of versatile behaviors and more general experiences. Thus, our extension of the P-GPLVM framework for unsupervised analysis of neural activity can be used to answer critical questions related to scientific discovery.

Synaptic excitation and inhibition are essential for neuronal communication. However, the variables that regulate synaptic excitation and inhibition in the intact brain remain largely unknown. Here, we examined how spike transmission and suppression between principal cells (PCs) and interneurons (INTs) are modulated by activity history, brain state, cell type, and somatic distance between presynaptic and postsynaptic neurons by applying cross‐correlogram analyses to datasets recorded from the dorsal hippocampus and medial entorhinal cortex (MEC) of 11 male behaving and sleeping Long Evans rats. The strength, temporal delay, and brain‐state dependency of the spike transmission and suppression depended on the subregions/layers. The spike transmission probability of PC–INT excitatory pairs that showed short‐term depression versus short‐term facilitation was higher in CA1 and lower in CA3. Likewise, the intersomatic distance affected the proportion of PC–INT excitatory pairs that showed short‐term depression and facilitation in the opposite manner in CA1 compared with CA3. The time constant of depression was longer, while that of facilitation was shorter in MEC than in CA1 and CA3. During sharp‐wave ripples, spike transmission showed a larger gain in the MEC than in CA1 and CA3. The intersomatic distance affected the spike transmission gain during sharp‐wave ripples differently in CA1 versus CA3. A subgroup of MEC layer 3 (EC3) INTs preferentially received excitatory inputs from and inhibited MEC layer 2 (EC2) PCs. The EC2 PC–EC3 INT excitatory pairs, most of which showed short‐term depression, exhibited higher spike transmission probabilities than the EC2 PC–EC2 INT and EC3 PC–EC3 INT excitatory pairs. EC2 putative stellate cells exhibited stronger spike transmission to and received weaker spike suppression from EC3 INTs than EC2 putative pyramidal cells. This study provides detailed comparisons of monosynaptic interaction dynamics in the hippocampal–entorhinal loop, which may help to elucidate circuit operations.

Memories benefit from sleep¹, and the reactivation and replay of waking experiences during hippocampal sharp-wave ripples (SWRs) are considered to be crucial for this process². However, little is known about how these patterns are impacted by sleep loss. Here we recorded CA1 neuronal activity over 12 h in rats across maze exploration, sleep and sleep deprivation, followed by recovery sleep. We found that SWRs showed sustained or higher rates during sleep deprivation but with lower power and higher frequency ripples. Pyramidal cells exhibited sustained firing during sleep deprivation and reduced firing during sleep, yet their firing rates were comparable during SWRs regardless of sleep state. Despite the robust firing and abundance of SWRs during sleep deprivation, we found that the reactivation and replay of neuronal firing patterns was diminished during these periods and, in some cases, completely abolished compared to ad libitum sleep. Reactivation partially rebounded after recovery sleep but failed to reach the levels found in natural sleep. These results delineate the adverse consequences of sleep loss on hippocampal function at the network level and reveal a dissociation between the many SWRs elicited during sleep deprivation and the few reactivations and replays that occur during these events.

Hippocampal representations that underlie spatial memory undergo continuous refinement following formation¹. Here, to track the spatial tuning of neurons dynamically during offline states, we used a new Bayesian learning approach based on the spike-triggered average decoded position in ensemble recordings from freely moving rats. Measuring these tunings, we found spatial representations within hippocampal sharp-wave ripples that were stable for hours during sleep and were strongly aligned with place fields initially observed during maze exploration. These representations were explained by a combination of factors that included preconfigured structure before maze exposure and representations that emerged during θ-oscillations and awake sharp-wave ripples while on the maze, revealing the contribution of these events in forming ensembles. Strikingly, the ripple representations during sleep predicted the future place fields of neurons during re-exposure to the maze, even when those fields deviated from previous place preferences. By contrast, we observed tunings with poor alignment to maze place fields during sleep and rest before maze exposure and in the later stages of sleep. In sum, the new decoding approach allowed us to infer and characterize the stability and retuning of place fields during offline periods, revealing the rapid emergence of representations following new exploration and the role of sleep in the representational dynamics of the hippocampus.

Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural state repetition from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson Gaussian-process latent variable model (P-GPLVM) (Wu et al., 2017) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the learned latent space, limiting its ability for estimating the internal state repetition. Here, we extend the P-GPLVM to enable the latent variable inference of new data constrained by previously learned smoothness and mapping information. We also describe a principled approach for the constrained latent variable inference for temporally-compressed patterns of activity, such as those found in population burst events (PBEs) during hippocampal sharp-wave ripples, as well as metrics for assessing whether the inferred new latent variables are congruent with a previously learned manifold in the latent space. Applying these approaches to hippocampal ensemble recordings during active maze exploration, we replicate the result that P-GPLVM learns a latent space encoding the animal’s position. We further demonstrate that this latent space can differentiate one maze context from another. By inferring the latent variables of new neural data during running, certain internal neural states are observed to repeat, which is in accordance with the similarity of experiences encoded by its nearby neural trajectories in the training data manifold. Finally, repetition of internal neural states can be estimated for neural activity during PBEs as well, allowing the identification for replay events of versatile behaviors and more general experiences. Thus, our extension of the P-GPLVM framework for unsupervised analysis of neural activity can be used to answer critical questions related to scientific discovery.