Uncovering temporal structure in hippocampal output patterns.

Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. PBE activity has historically been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed in rats during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with a spatial map of the environment, and can even decode animals' positions during behavior. Moreover, we demonstrate the model can be used to identify hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory

Activity-dependence of synaptic vesicle dynamics

The proper function of synapses relies on efficient recycling of synaptic vesicles. The small size of synaptic boutons has hampered efforts to define the dynamical states of vesicles during recycling. Moreover, whether vesicle motion during recycling is regulated by neural activity remains largely unknown. We combined nanoscale-resolution tracking of individual synaptic vesicles in cultured hippocampal neurons from rats of both sexes with advanced motion analyses to demonstrate that the majority of recently endocytosed vesicles undergo sequences of transient dynamical states including epochs of directed, diffusional, and stalled motion. We observed that vesicle motion is modulated in an activity-dependent manner, with dynamical changes apparent in ∼20% of observed boutons. Within this subpopulation of boutons, 35% of observed vesicles exhibited acceleration and 65% exhibited deceleration, accompanied by corresponding changes in directed motion. Individual vesicles observed in the remaining ∼80% of boutons did not exhibit apparent dynamical changes in response to stimulation. More quantitative transient motion analyses revealed that the overall reduction of vesicle mobility, and specifically of the directed motion component, is the predominant activity-evoked change across the entire bouton population. Activity-dependent modulation of vesicle mobility may represent an important mechanism controlling vesicle availability and neurotransmitter release.SIGNIFICANCE STATEMENTMechanisms governing synaptic vesicle dynamics during recycling remain poorly understood. Using nanoscale resolution tracking of individual synaptic vesicles in hippocampal synapses and advanced motion analysis tools we demonstrate that synaptic vesicles undergo complex sets of dynamical states that include epochs of directed, diffusive, and stalled motion. Most importantly, our analyses revealed that vesicle motion is modulated in an activity-dependent manner apparent as the reduction in overall vesicle mobility in response to stimulation. These results define the vesicle dynamical states during recycling and reveal their activity-dependent modulation. Our study thus provides fundamental new insights into the principles governing synaptic function

Decoding information in the human hippocampus: a user's guide

Multi-voxel pattern analysis (MVPA), or 'decoding', of fMRI activity has gained popularity in the neuroimaging community in recent years. MVPA differs from standard fMRI analyses by focusing on whether information relating to specific stimuli is encoded in patterns of activity across multiple voxels. If a stimulus can be predicted, or decoded, solely from the pattern of fMRI activity, it must mean there is information about that stimulus represented in the brain region where the pattern across voxels was identified. This ability to examine the representation of information relating to specific stimuli (e.g., memories) in particular brain areas makes MVPA an especially suitable method for investigating memory representations in brain structures such as the hippocampus. This approach could open up new opportunities to examine hippocampal representations in terms of their content, and how they might change over time, with aging, and pathology. Here we consider published MVPA studies that specifically focused on the hippocampus, and use them to illustrate the kinds of novel questions that can be addressed using MVPA. We then discuss some of the conceptual and methodological challenges that can arise when implementing MVPA in this context. Overall, we hope to highlight the potential utility of MVPA, when appropriately deployed, and provide some initial guidance to those considering MVPA as a means to investigate the hippocampus

Statistical Physics and Representations in Real and Artificial Neural Networks

This document presents the material of two lectures on statistical physics and neural representations, delivered by one of us (R.M.) at the Fundamental Problems in Statistical Physics XIV summer school in July 2017. In a first part, we consider the neural representations of space (maps) in the hippocampus. We introduce an extension of the Hopfield model, able to store multiple spatial maps as continuous, finite-dimensional attractors. The phase diagram and dynamical properties of the model are analyzed. We then show how spatial representations can be dynamically decoded using an effective Ising model capturing the correlation structure in the neural data, and compare applications to data obtained from hippocampal multi-electrode recordings and by (sub)sampling our attractor model. In a second part, we focus on the problem of learning data representations in machine learning, in particular with artificial neural networks. We start by introducing data representations through some illustrations. We then analyze two important algorithms, Principal Component Analysis and Restricted Boltzmann Machines, with tools from statistical physics

Topological Schemas of Memory Spaces

Hippocampal cognitive map---a neuronal representation of the spatial environment---is broadly discussed in the computational neuroscience literature for decades. More recent studies point out that hippocampus plays a major role in producing yet another cognitive framework that incorporates not only spatial, but also nonspatial memories---the memory space. However, unlike cognitive maps, memory spaces have been barely studied from a theoretical perspective. Here we propose an approach for modeling hippocampal memory spaces as an epiphenomenon of neuronal spiking activity. First, we suggest that the memory space may be viewed as a finite topological space---a hypothesis that allows treating both spatial and nonspatial aspects of hippocampal function on equal footing. We then model the topological properties of the memory space to demonstrate that this concept naturally incorporates the notion of a cognitive map. Lastly, we suggest a formal description of the memory consolidation process and point out a connection between the proposed model of the memory spaces to the so-called Morris' schemas, which emerge as the most compact representation of the memory structure.Comment: 24 pages, 8 Figures, 1 Suppl. Figur