Memory reactivation: replaying events past, present and future

During both active navigation and rest, temporally precise sequences of activity are produced across the hippocampal circuit. Recent findings suggest that pre-existing network dynamics strongly bias such sequences in all behavioral states. However, considerable evidence also demonstrates a critical role for experience in shaping the specific content of hippocampal sequences. For example, blocking synaptic plasticity during exploration of a novel environment virtually abolishes coherent re-expression of those behavioral sequences in subsequent rest. In addition, prior to goal-directed navigation, both ripple- and theta-based sequences display spatial trajectories which are heavily biased to proceed from the animal’s current location to a recently learned goal. Importantly, the encoding of the goal location in hippocampal sequences appears to emerge as a consequence of experience-dependent learning, with no goal-directed bias early in the behavioral task.  While these findings do not rule out the possibility that preconfigured circuit connectivity can impact ‘offline’ sequences or influence how the hippocampus encodes experience, they highlight the importance of experience in further shaping the network to produce specific neuronal sequences that facilitate flexible behavior.

Cortical neuronal ensembles form rapid internal representations about the external world through the interplay between internally-generated spontaneous neuronal dynamics and externally-driven activity about the specific sensory dynamics of the world. A stronger network reliance on the recruitment of internally-generated neuronal activity would greatly enhance the speed and efficiency of encoding at the cost of limiting its capacity to the combinatorial size of the fixed repertoire of internal neuronal motifs. On the other hand, a reduced dependency on an internal network model of the world would increase the flexibility and capacity for encoding independent representations that would instead require multiple exposures and additional time to create stable novel representations. Here, the authors reveal the existence of a generative predictive model for the statistical regularities of the contextual space in the hippocampus and define the principles underlying its use in navigation and its update by inferred intrinsic-unlikely neuronal functional connectivity and selective plasticity. These generative predictive codes rely on functional connectivity within and between high-repeat, short motifs of sequential neurons called ‘tuplets’. They propose that network organization into neuronal tuplets segments the extended temporal sequences into shorter modules, analogous to segmentation of words into syllables. This novel neuronal organizational principle could vastly expand the generative capacity of the network by rapidly combining neuronal tuplets into multiple independent extended temporal sequences. This would enable the hippocampal network to achieve high-speed and high-capacity encoding at the same time, as required by the cognitive functions it supports.

Experience-related sequences of spiking activity in the hippocampus can be segregated into two groups. The first are those occurring during theta oscillations, which appear during locomotion and active exploration as well as in REM sleep. The second are those occurring during sharp-wave ripples, during pauses in exploratory activity and during slow-wave sleep. Whereas these groups provide consistent and meaningful divisions in brain-behaviour coupling in rats and mice, in anthropoid species like macaques and humans, the distinctions are less clear. We will describe our observations of sharp-wave ripples during active visual search in macaques, as well as in offline periods including sleep. The appearance of sharp-wave ripples in place of and superimposed within theta epochs suggests a possible expanded role for ripple-driven relative to theta-driven sequences during recall and decision making in primates.

Sharp-wave ripples are complex neurophysiological events recorded along the trisynaptic hippocampal circuit during slow-wave sleep and awake immobility. During these events, cognitively relevant sequences are triggered retrospectively or prospectively, and in the forward or reverse order as defined by experience. They could reflect either pre-configured sequences, learned sequences or an option space to inform subsequent decisions. How can different sequences emerge from directional connectivity is unknown. Emerging data now suggest the entorhinal-hippocampal circuit is organized in parallel loops along CA1 sublayers. It is discussed whether by incorporating cell-type specific physiological and computational mechanisms converging on deep and superficial CA1 sublayers could help to unveil the organization of sequences during sharp-wave ripples.

For more than 20 years, research in my laboratory has focused on investigating the behavioural determinants, neuronal substrates and neurophysiological correlates of motor skill learning and consolidation. During this presentation, I will review some of our work focusing on motor sequence learning (MSL) and will discuss our studies showing that the consolidation of this form of memory trace depends upon greater functional integration of the cortico-striatal system and non-rapid eye movement (N-REM) sleep spindle activity measured during the night following the initial training session. More specifically, I will describe the results of our simultaneous fMRI/EEG recording experiment, which show that: a) the cortico-striatal network recruited during MSL is reactivated during sleep, time-locked to spindles, b) such a reactivation of the memory trace is followed by reorganization of the neural representation toward a subcortically-dominant consolidated trace during the post-training night, and c) sleep spindles promote skill consolidation by locally reactivating and functionally binding task-relevant cortical and subcortical regions including the striatum.

Synapses are the structures in the brain that form and store memories through the processes of synaptic plasticity. Sleep is an essential process that supports learning and memory by acting on synapses through poorly understood molecular mechanisms. Moreover, sleep disruption is commonly associated with neurodevelopmental and neurodegenerative diseases, and likely contributes to cognitive impairment in these conditions. Using biochemistry, proteomics and imaging in mice we show that during sleep, synapses undergo widespread alterations in composition and signaling. Consistent with the sleep homeostasis hypothesis which posits that part of the restorative benefits of sleep is mediated by broad weakening of synaptic connections we observe that sleep is associated with removal and dephosphorylation of synaptic AMPA-type glutamate receptors. These changes are driven by the immediate early gene Homer1a, and signaling from group I metabotropic glutamate receptors mGluR1/5. The changes that occur at synapses during sleep and the molecular requirement for Homer1a are highly reminiscent of homeostatic scaling-down in cultured neurons, further supporting the sleep homeostasis hypothesis. Our ongoing studies attempt to further dissect the molecular mechanisms that act on synapses during sleep, with a focus on synaptic signaling molecules related to mGluR1/5. We are applying this work to understanding how sleep disruption may contribute to cognitive impairments in autism spectrum disorder, and Alzheimer’s disease. Therapeutic strategies designed to enhance the restorative benefits of sleep will be discussed.

The synaptic homeostasis hypothesis (SHY) proposes that sleep is an essential process needed by the brain to maintain the total amount of synaptic strength under control. SHY predicts that by the end of a waking day the synaptic connections of many neural circuits undergo a net increase in synaptic strength due to ongoing learning, which is mainly mediated by synaptic potentiation. Stronger synapses require more energy and supplies and are prone to saturation, creating the need for synaptic renormalization. Such renormalization should mainly occur during sleep when the brain is disconnected from the environment and neural circuits can be broadly reactivated off-line to undergo a systematic and yet specific synaptic down-selection. In short, according to SHY, sleep is the price to pay for waking plasticity to avoid runaway potentiation, decreased signal-to-noise ratio, and impaired learning due to saturation. The talk will cover the rationale underlying this hypothesis and summarize electrophysiological, molecular and ultrastructural studies in flies, rodents and humans that confirmed SHY’s main predictions, including the recent observation, obtained using serial block face scanning electron microscopy, that most synapses in mouse primary motor and sensory cortices grow after wake and shrink after sleep. Unpublished ultrastructural data obtained in the CA1 region of the hippocampus and in the cortex of mouse pups will also be presented.

Sleep states are thought to have a strong homeostatic role in neocortical circuits. Previous work has shown that neocortical firing rates are modulated over sleep-wake cycles in a manner consistent with homeostasis: firing rates on average rise during waking periods and drop during sleep periods. In this work, prefrontal cortical neuronal populations in rats were recorded with silicon probe arrays to obtain large numbers of recorded neurons simultaneously to allow us to examine network-wide effects of natural sleep and wake cycles. Results from these high-channel count recordings revealed that neurons of differing overall firing rates are regulated differentially during sleep sessions with only the highest-firing rate cells showing consistent drops in firing rate with sleep, while low firing-rate neurons either did not show a net change in activity or even showed increased firing rates by some metrics. The overall variance of the network activity dropped over sleep as a result of this differential effect, pointing to a prominent homogenization effect of sleep and homeostasis. In addition these recordings revealed that high firing rate neurons tended to fire earlier than low firing rate neurons during repetitive (1 hertz) network UP state activations. This latter finding leads to the hypothesis that spike timing dependent plasticity (STDP) rules may combine with repeated sequences of high-rate then low-rate cells to create the differential effect on total drive to high versus low firing rate cells. We therefore suggest that sequences of network activity during sleep may serve not just a mnemonic function by may drive network homeostasis.

Experience-dependent synaptic plasticity is critical for encoding and storing information. How experience-induced synaptic plasticity is distributed and maintained in neuronal networks remains unclear. Dendritic spines are the postsynaptic sites of most excitatory synapses in the mammalian brain. Using in vivo two-photon microscopy, we examined the effects of motor learning and chronic pain on dendritic spine remodeling in the mouse cortex. We show that new spines are formed on different sets of dendritic branches of layer 5 pyramidal neurons in the motor cortex in response to different motor learning tasks. This branch-specific formation of dendritic spines facilitates the maintenance of new spines when multiple tasks are learned. In addition, chronic pain induces dendritic spine formation and elimination on a subset of dendritic branches of layer 5 pyramidal neurons in the primary somatosensory cortex. Notably, cortical neurons that are active during motor training or responsive to nociceptive stimuli exhibit a substantial increase in calcium activity during subsequent non-rapid eye movement sleep. Blockade of neuronal activity during sleep reduces experience-dependent dendritic spine plasticity and alleviates chronic pain. Together, these findings suggest that experience and subsequent sleep lead to dendritic branch-specific synaptic remodeling, thereby contributing to memory storage in neuronal circuits.