Querying hippocampal replay with subcortical inputs

During sleep, the hippocampus recapitulates neuronal patterns corresponding to behavioral trajectories during previous experiences. This hippocampal replay supports the formation of long-term memories. Yet, whether replay originates within the hippocampal circuitry or is initiated by extrahippocampal inputs is unknown. Here, I review recent findings regarding the organization of neuronal activity upstream to the hippocampus, in the head-direction (HD) and grid cell networks, and its relationship to replay. I argue that hippocampal activity at the onset of replay is under the influence of signals from primary spatial areas. In turn, hippocampal replay resets the HD network activity to select a new direction for the next replay event. This reciprocal interaction between the HD network and the hippocampus may be essential in grounding meaning to hippocampal activity, specifically by training decoders of hippocampal sequences. Neuronal dynamics in thalamo-hippocampal loops may thus be instrumental for memory processes during sleep.Canadian Institutes of Health ResearchIsrael Science FoundationAzrieli Foundatio

Medial prefrontal cortex population activity is plastic irrespective of learning

The prefrontal cortex is thought to learn the relationships between actions and their outcomes. But little is known about what changes to population activity in prefrontal cortex are specific to learning these relationships. Here we characterise the plasticity of population activity in the medial prefrontal cortex of male rats learning rules on a Y-maze. First, we show that the population always changes its patterns of joint activity between the periods of sleep either side of a training session on the maze, irrespective of successful rule learning during training. Next, by comparing the structure of population activity in sleep and training, we show that this population plasticity differs between learning and non-learning sessions. In learning sessions, the changes in population activity in post-training sleep incorporate the changes to the population activity during training on the maze. In non-learning sessions, the changes in sleep and training are unrelated. Finally, we show evidence that the non-learning and learning forms of population plasticity are driven by different neuron-level changes, with the non-learning form entirely accounted for by independent changes to the excitability of individual neurons, and the learning form also including changes to firing rate couplings between neurons. Collectively, our results suggest two different forms of population plasticity in prefrontal cortex during the learning of action-outcome relationships, one a persistent change in population activity structure decoupled from overt rule-learning, the other a directional change driven by feedback during behaviour

An ensemble code in medial prefrontal cortex links prior events to outcomes during learning

The prefrontal cortex is implicated in learning the rules of an environment through trial and error. But it is unclear how such learning is related to the prefrontal cortex’s role in short-term memory. Here we ask if the encoding of short-term memory in prefrontal cortex is used by rats learning decision rules in a Y-maze task. We find that a similar pattern of neural ensemble activity is selectively recalled after reinforcement for a correct decision. This reinforcement-selective recall only reliably occurs immediately before the abrupt behavioural transitions indicating successful learning of the current rule, and fades quickly thereafter. We could simultaneously decode multiple, retrospective task events from the ensemble activity, suggesting the recalled ensemble activity has multiplexed encoding of prior events. Our results suggest that successful trial-and-error learning is dependent on reinforcement tagging the relevant features of the environment to maintain in prefrontal cortex short-term memory

Coherent Theta Oscillations and Reorganization of Spike Timing in the Hippocampal- Prefrontal Network upon Learning

To study the interplay between hippocampus and medial prefrontal cortex (Pfc) and its importance for learning and memory consolidation, we measured the coherence in theta oscillations between these two structures in rats learning new rules on a Y maze. Coherence peaked at the choice point, most strongly after task rule acquisition. Simultaneously, Pfc pyramidal neurons reorganized their phase, concentrating at hippocampal theta trough, and synchronous cell assemblies emerged. This synchronous state may result from increased inhibition exerted by interneurons on pyramidal cells, as measured by cross-correlation, and could be modulated by dopamine: we found similar hippocampal-Pfc theta coherence increases and neuronal phase shifts following local administration of dopamine in Pfc of anesthetized rats. Pfc cell assemblies emerging during high coherence were preferentially replayed during subsequent sleep, concurrent with hippocampal sharp waves. Thus, hippocampal/prefrontal coherence could lead to synchronization of reward predicting activity in prefrontal networks, tagging it for subsequent memory consolidation.European Commission (Contract FP6-IST 027819)European Commission (Contract FP6-IST-027140)European Commission (Contract FP6-IST-027017

Sequential Reinstatement of Neocortical Activity during Slow Oscillations Depends on Cells’ Global Activity

During Slow Wave Sleep (SWS), cortical activity is dominated by endogenous processes modulated by slow oscillations (0.1–1 Hz): cell ensembles fluctuate between states of sustained activity (UP states) and silent epochs (DOWN states). We investigate here the temporal structure of ensemble activity during UP states by means of multiple single unit recordings in the prefrontal cortex of naturally sleeping rats. As previously shown, the firing rate of each PFC cell peaks at a distinct time lag after the DOWN/UP transition in a consistent order. We show here that, conversely, the latency of the first spike after the UP state onset depends primarily on the session-averaged firing rates of cells (which can be considered as an indirect measure of their intrinsic excitability). This latency can be explained by a simple homogeneous process (Poisson model) of cell firing, with sleep averaged firing rates employed as parameters. Thus, at DOWN/UP transitions, neurons are affected both by a slow process, possibly originating in the cortical network, modulating the time course of firing for each cell, and by a fast, relatively stereotyped reinstatement of activity, related mostly to global activity levels

Reciprocal feature encoding by cortical excitatory and inhibitory neurons

In the cortex, the interplay between excitation and inhibition determines the fidelity of neuronal representations. However, while the receptive fields of excitatory neurons are often fine-tuned to the encoded features, the principles governing the tuning of inhibitory neurons are still elusive. We addressed this problem by recording populations of neurons in the postsubiculum (PoSub), a cortical area where the receptive fields of most excitatory neurons correspond to a specific head-direction (HD). In contrast to PoSub-HD cells, the tuning of fast-spiking (FS) cells, the largest class of cortical inhibitory neurons, was broad and heterogeneous. However, we found that PoSub-FS cell tuning curves were often fine-tuned in the spatial frequency domain, which resulted in various radial symmetries in their HD tuning. In addition, the average frequency spectrum of PoSub-FS cell populations was virtually indistinguishable from that of PoSub-HD cells but different from that of the upstream thalamic HD cells, suggesting that this population cotuning in the frequency domain has a local origin. Two observations corroborated this hypothesis. First, PoSub-FS cell tuning was independent of upstream thalamic inputs. Second, PoSub-FS cell tuning was tightly coupled to PoSub-HD cell activity even during sleep. Together, these findings provide evidence that the resolution of neuronal tuning is an intrinsic property of local cortical networks, shared by both excitatory and inhibitory cell populations. We hypothesize that this reciprocal feature encoding supports two parallel streams of information processing in thalamocortical networks.Canadian Institutes of Health ResearchIsrael Science FoundationAzrieli FoundationEMBO Long-Term Postdoctoral FellowshipSir Henry Wellcome Fellowship (A.J.D.

Neurodata Without Borders: Creating a Common Data Format for Neurophysiology

The Neurodata Without Borders (NWB) initiative promotes data standardization in neuroscience to increase research reproducibility and opportunities. In the first NWB pilot project, neurophysiologists and software developers produced a common data format for recordings and metadata of cellular electrophysiology and optical imaging experiments. The format specification, application programming interfaces, and sample datasets have been released

Principal component analysis of ensemble recordings reveals cell assemblies at high temporal resolution

Simultaneous recordings of many single neurons reveals unique insights into network processing spanning the timescale from single spikes to global oscillations. Neurons dynamically self-organize in subgroups of coactivated elements referred to as cell assemblies. Furthermore, these cell assemblies are reactivated, or replayed, preferentially during subsequent rest or sleep episodes, a proposed mechanism for memory trace consolidation. Here we employ Principal Component Analysis to isolate such patterns of neural activity. In addition, a measure is developed to quantify the similarity of instantaneous activity with a template pattern, and we derive theoretical distributions for the null hypothesis of no correlation between spike trains, allowing one to evaluate the statistical significance of instantaneous coactivations. Hence, when applied in an epoch different from the one where the patterns were identified, (e.g. subsequent sleep) this measure allows to identify times and intensities of reactivation. The distribution of this measure provides information on the dynamics of reactivation events: in sleep these occur as transients rather than as a continuous process

A mechanism for learning with sleep spindles

Spindles are ubiquitous oscillations during non-rapid eye movement (NREM) sleep. A growing body of evidence points to a possible link with learning and memory, and the underlying mechanisms are now starting to be unveiled. Specifically, spindles are associated with increased dendritic activity and high intracellular calcium levels, a situation favourable to plasticity, as well as with control of spiking output by feed-forward inhibition. During spindles, thalamocortical networks become unresponsive to inputs, thus potentially preventing interference between memory-related internal information processing and extrinsic signals. At the system level, spindles are co-modulated with other major NREM oscillations, including hippocampal sharp wave-ripples (SWRs) and neocortical slow waves, both previously shown to be associated with learning and memory. The sequential occurrence of reactivation at the time of SWRs followed by neuronal plasticity-promoting spindles is a possible mechanism to explain NREM sleep-dependent consolidation of memories

Influence of sleep on the hippocampo-prefrontal network (involvement in memory consolidation and learning)

L'hippocampe et le cortex préfrontal sont deux aires cérébrales essentielles dans l'apprentissage et l'établissement de mémoires à long terme. Le sommeil serait une période déterminante pour la consolidation mnésique. En particulier, il a été montré que dans ces structures, les ensembles de neurones rejouent pendant le sommeil les schémas d'activité ayant eu lieu pendant l'éveil précédent. Nous avons enregistré de grands ensembles de neurones dans le CPF ainsi que l'activité de potentiel de champ local dans l'hippocampe, alors que l'animal devait apprendre différentes tâches comportementales ainsi que les phases de sommeil qui suivaient et qui précédaient cette période d'éveil. Pendant l'apprentissage, les neurones du CPF s'organisaient en assemblées cellulaires, c'est-à-dire des sous-ensembles présentant des corrélation comportementales communes et tendant à décharger de façon synchrone. Lorsque l'apprentissage était fini (l'animal présentait alors une performance optimale) les deux structures présentaient une forte co-modulation oscillatoire, suggérant une synchronisation des assemblées dans les deux structures. Pendant le sommeil suivant, ces assemblées se réactivaient ensemble. Nous avons donc mis en evidence un mécanisme neurophysiologique permettant de marquer les assemblées du CPF corrélées aux comportements les plus optimaux afin qu'elles soient spécifiquement consolidées pendant les périodes de sommeil suivantes.PARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF