Distinct lines of research in both humans and animals point to a specific role of the hippocampus in both spatial and episodic memory function. The discovery of concept cells in the hippocampus and surrounding medial temporal lobe (MTL) regions suggests that the MTL maps physical and semantic spaces with a similar neural architecture. Here, we studied the emergence of such maps using MTL microwire recordings from 20 patients (9 female, 11 male) navigating a virtual environment featuring salient landmarks with established semantic meaning. We present several key findings. The array of local field potentials in the MTL contains sufficient information for above-chance decoding of subjects' instantaneous location in the environment. Closer examination revealed that as subjects gain experience with the environment the field potentials come to represent both the subjects' locations in virtual space and in high-dimensional semantic space. Similarly, we observe a learning effect on temporal sequence coding. Over time, field potentials come to represent future locations, even after controlling for spatial proximity. This predictive coding of future states, more so than the strength of spatial representations per se, is linked to variability in subjects' navigation performance. Our results thus support the conceptualization of the MTL as a memory space, representing both spatial- and nonspatial information to plan future actions and predict their outcomes.
- Award ID(s):
- 1633516
- NSF-PAR ID:
- 10288447
- Date Published:
- Journal Name:
- Brain and Neuroscience Advances
- Volume:
- 4
- ISSN:
- 2398-2128
- Page Range / eLocation ID:
- 239821282097287
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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SIGNIFICANCE STATEMENT Using rare microwire recordings, we studied the representation of spatial, semantic, and temporal information in the human MTL. Our findings demonstrate that subjects acquire a cognitive map that simultaneously represents the spatial and semantic relations between landmarks. We further show that the same learned representation is used to predict future states, implicating MTL cell assemblies as the building blocks of prospective memory functions. -
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Abstract The hippocampus and posterior parietal cortex are implicated in both episodic memory and encoding of position in an environment. In the present study, we examine the impact of locomotor behaviors associated with movement in both the horizontal and vertical dimensions on population activity patterns in these two brain structures. We utilized a five‐looped, squared spiral track containing stair segments, ramp segments, and flat segments. In addition to encoding locations along the full route, posterior parietal cortex population activity demonstrates strong pattern recurrence for similar action types at different locations in the environment. Additionally, posterior parietal and hippocampal neurons exhibit parallel modulation in the scale of representation that follows behavioral dynamics required for track traversal. These findings build on prior work examining spatial mapping in the vertical dimension and provide a better understanding of how a series of actions and visited locations can be coordinated in the generation of episodic memory.
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Rubin, Jonathan (Ed.)Theta and gamma oscillations in the hippocampus have been hypothesized to play a role in the encoding and retrieval of memories. Recently, it was shown that an intrinsic fast gamma mechanism in medial entorhinal cortex can be recruited by optogenetic stimulation at theta frequencies, which can persist with fast excitatory synaptic transmission blocked, suggesting a contribution of interneuronal network gamma (ING). We calibrated the passive and active properties of a 100-neuron model network to capture the range of passive properties and frequency/current relationships of experimentally recorded PV+ neurons in the medial entorhinal cortex (mEC). The strength and probabilities of chemical and electrical synapses were also calibrated using paired recordings, as were the kinetics and short-term depression (STD) of the chemical synapses. Gap junctions that contribute a noticeable fraction of the input resistance were required for synchrony with hyperpolarizing inhibition; these networks exhibited theta-nested high frequency oscillations similar to the putative ING observed experimentally in the optogenetically-driven PV-ChR2 mice. With STD included in the model, the network desynchronized at frequencies above ~200 Hz, so for sufficiently strong drive, fast oscillations were only observed before the peak of the theta. Because hyperpolarizing synapses provide a synchronizing drive that contributes to robustness in the presence of heterogeneity, synchronization decreases as the hyperpolarizing inhibition becomes weaker. In contrast, networks with shunting inhibition required non-physiological levels of gap junctions to synchronize using conduction delays within the measured range.more » « less
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Introduction Understanding the neural code has been one of the central aims of neuroscience research for decades. Spikes are commonly referred to as the units of information transfer, but multi-unit activity (MUA) recordings are routinely analyzed in aggregate forms such as binned spike counts, peri-stimulus time histograms, firing rates, or population codes. Various forms of averaging also occur in the brain, from the spatial averaging of spikes within dendritic trees to their temporal averaging through synaptic dynamics. However, how these forms of averaging are related to each other or to the spatial and temporal units of information representation within the neural code has remained poorly understood.
Materials and methods In this work we developed NeuroPixelHD, a symbolic hyperdimensional model of MUA, and used it to decode the spatial location and identity of static images shown to
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Discussion Our findings corroborate existing empirical practices of spatiotemporal binning and averaging in MUA data analysis, and provide a rigorous computational framework for optimizing the level of such aggregations. Our findings can also synthesize these empirical practices with existing knowledge of the various sources of biological averaging in the brain into a new theory of neural information processing in which the
unit of information varies dynamically based on neuronal signal and noise correlations across space and time. -
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/2398212820972871
