Multiplexed representation of others in the hippocampal CA1 subfield of female mice.

Hippocampal place cells represent the position of a rodent within an environment. In addition, recent experiments show that the CA1 subfield of a passive observer also represents the position of a conspecific performing a spatial task. However, whether this representation is allocentric, egocentric or mixed is less clear. In this study we investigated the representation of others during free behavior and in a task where female mice learned to follow a conspecific for a reward. We found that most cells represent the position of others relative to self-position (social-vector cells) rather than to the environment, with a prevalence of purely egocentric coding modulated by context and mouse identity. Learning of a pursuit task improved the tuning of social-vector cells, but their number remained invariant. Collectively, our results suggest that the hippocampus flexibly codes the position of others in multiple coordinate systems, albeit favoring the self as a reference point.

Unique potential of immature adult-born neurons for the remodeling of CA3 spatial maps.

Mammalian hippocampal circuits undergo extensive remodeling through adult neurogenesis. While this process has been widely studied, the specific contribution of adult-born granule cells (aGCs) to spatial operations in the hippocampus remains unknown. Here, we show that optogenetic activation of 4-week-old (young) aGCs in free-foraging mice produces a non-reversible reconfiguration of spatial maps in proximal CA3 while rarely evoking neural activity. Stimulation of the same neuronal cohort on subsequent days recruits CA3 neurons with increased efficacy but fails to induce further remapping. In contrast, stimulation of 8-week-old (mature) aGCs can reliably activate CA3 cells but produces no alterations in spatial maps. Our results reveal a unique role of young aGCs in remodeling CA3 representations, a potential that can be depleted and is lost with maturation. This ability could contribute to generate orthogonalized downstream codes supporting pattern separation.

Selective connectivity enhances storage capacity in attractor models of memory function.

Autoassociative neural networks provide a simple model of how memories can be stored through Hebbian synaptic plasticity as retrievable patterns of neural activity. Although progress has been made along the last decades in understanding the biological implementation of autoassociative networks, their modest theoretical storage capacity has remained a major constraint. While most previous approaches utilize randomly connected networks, here we explore the possibility of optimizing network performance by selective connectivity between neurons, that could be implemented in the brain through creation and pruning of synaptic connections. We show through numerical simulations that a reconfiguration of the connectivity matrix can improve the storage capacity of autoassociative networks up to one order of magnitude compared to randomly connected networks, either by reducing the noise or by making it reinforce the signal. Our results indicate that the signal-reinforcement scenario is not only the best performing but also the most adequate for brain-like highly diluted connectivity. In this scenario, the optimized network tends to select synapses characterized by a high consensus across stored patterns. We also introduced an online algorithm in which the network modifies its connectivity while learning new patterns. We observed that, similarly to what happens in the human brain, creation of connections dominated in an initial stage, followed by a stage characterized by pruning, leading to an equilibrium state that was independent of the initial connectivity of the network. Our results suggest that selective connectivity could be a key component to make attractor networks in the brain viable in terms of storage capacity.

Spatial maps and oscillations in the healthy hippocampus of Octodon degus, a natural model of sporadic Alzheimer's disease.

The Octodon degus is a South American rodent that is receiving increased attention as a potential model of aging and sporadic late-onset Alzheimer's disease (AD). Impairments in spatial memory tasks in Octodon degus have been reported in relation to either advanced AD-like disease or hippocampal lesion, opening the way to investigate how the function of hippocampal networks affects behavior across AD stages. However, no characterization of hippocampal electrophysiology exists in this species. Here we describe in young, healthy specimens the activity of neurons and local field potential rhythms during spatial navigation tasks with and without objects. Our findings show similarities between the Octodon degus and laboratory rodents. First, place cells with characteristics similar to those found in rats and mice exist in the CA1 subfield of the Octodon degus. Second, the introduction of objects elicits novelty-related exploration and an increase in activity of CA1 cells, with location specific and unspecific components. Third, oscillations of the local field potential are organized according to their spectral content into bands similar to those found in laboratory rodents. These results suggest a common framework of underlying mechanisms, opening the way to future studies of hippocampal dysfunction in this species associated to aging and disease.

Relative Humidity Predicts Day-to-Day Variations in COVID-19 Cases in the City of Buenos Aires.

Possible links between the transmission of COVID-19 and meteorology have been investigated by comparing positive cases across geographical regions or seasons. Little is known, however, about the degree to which environmental conditions modulate the daily dynamics of COVID-19 spread at a given location. One reason for this is that individual waves of the disease typically rise and decay too sharply, making it hard to isolate the contribution of meteorological cycles. To overcome this shortage, we here present a case study of the first wave of the outbreak in the city of Buenos Aires, which had a slow evolution of the caseload extending along most of 2020. We found that humidity plays a prominent role in modulating the variation of COVID-19 positive cases through a negative-slope linear relationship, with an optimal lag of 9 days between the meteorological observation and the positive case report. This relationship is specific to winter months, when relative humidity predicts up to half of the variance in positive case count. Our results provide a tool to anticipate possible local surges in COVID-19 cases after events of low humidity. More generally, they add to accumulating evidence pointing to dry air as a facilitator of COVID-19 transmission.

Frequency of theta rhythm is controlled by acceleration, but not speed, in running rats.

The theta rhythm organizes neural activity across hippocampus and entorhinal cortex. A role for theta oscillations in spatial navigation is supported by half a century of research reporting that theta frequency encodes running speed linearly so that displacement can be estimated through theta frequency integration. We show that this relationship is an artifact caused by the fact that the speed of freely moving animals could not be systematically disentangled from acceleration. Using an experimental procedure that clamps running speed at pre-set values, we find that the theta frequency of local field potentials and spike activity is linearly related to positive acceleration, but not negative acceleration or speed. The modulation by positive-only acceleration makes rhythmic activity at theta frequency unfit as a code to compute displacement or any other kinematic variable. Temporally precise variations in theta frequency may instead serve as a mechanism for speeding up entorhinal-hippocampal computations during accelerated movement.

A Brainstem Locomotor Circuit Drives the Activity of Speed Cells in the Medial Entorhinal Cortex.

Locomotion activates an array of sensory inputs that may help build the self-position map of the medial entorhinal cortex (MEC). In this map, speed-coding neurons are thought to dynamically update representations of the animal's position. A possible origin for the entorhinal speed signal is the mesencephalic locomotor region (MLR), which is critically involved in the activation of locomotor programs. Here, we describe, in rats, a circuit connecting the pedunculopontine tegmental nucleus (PPN) of the MLR to the MEC via the horizontal limb of the diagonal band of Broca (HDB). At each level of this pathway, locomotion speed is linearly encoded in neuronal firing rates. Optogenetic activation of PPN cells drives locomotion and modulates activity of speed-modulated neurons in HDB and MEC. Our results provide evidence for a pathway by which brainstem speed signals can reach cortical structures implicated in navigation and higher-order dynamic representations of space.

Information-Theoretical Analysis of the Neural Code in the Rodent Temporal Lobe.

In the study of the neural code, information-theoretical methods have the advantage of making no assumptions about the probabilistic mapping between stimuli and responses. In the sensory domain, several methods have been developed to quantify the amount of information encoded in neural activity, without necessarily identifying the specific stimulus or response features that instantiate the code. As a proof of concept, here we extend those methods to the encoding of kinematic information in a navigating rodent. We estimate the information encoded in two well-characterized codes, mediated by the firing rate of neurons, and by the phase-of-firing with respect to the theta-filtered local field potential. In addition, we also consider a novel code, mediated by the delta-filtered local field potential. We find that all three codes transmit significant amounts of kinematic information, and informative neurons tend to employ a combination of codes. Cells tend to encode conjunctions of kinematic features, so that most of the informative neurons fall outside the traditional cell types employed to classify spatially-selective units. We conclude that a broad perspective on the candidate stimulus and response features expands the repertoire of strategies with which kinematic information is encoded.

Bursting Neurons in the Hippocampal Formation Encode Features of LFP Rhythms.

Burst spike patterns are common in regions of the hippocampal formation such as the subiculum and medial entorhinal cortex (MEC). Neurons in these areas are immersed in extracellular electrical potential fluctuations often recorded as the local field potential (LFP). LFP rhythms within different frequency bands are linked to different behavioral states. For example, delta rhythms are often associated with slow-wave sleep, inactivity and anesthesia; whereas theta rhythms are prominent during awake exploratory behavior and REM sleep. Recent evidence suggests that bursting neurons in the hippocampal formation can encode LFP features. We explored this hypothesis using a two-compartment model of a bursting pyramidal neuron driven by time-varying input signals containing spectral peaks at either delta or theta rhythms. The model predicted a neural code in which bursts represented the instantaneous value, phase, slope and amplitude of the driving signal both in their timing and size (spike number). To verify whether this code is employed in vivo, we examined electrophysiological recordings from the subiculum of anesthetized rats and the MEC of a behaving rat containing prevalent delta or theta rhythms, respectively. In both areas, we found bursting cells that encoded information about the instantaneous voltage, phase, slope and/or amplitude of the dominant LFP rhythm with essentially the same neural code as the simulated neurons. A fraction of the cells encoded part of the information in burst size, in agreement with model predictions. These results provide in-vivo evidence that the output of bursting neurons in the mammalian brain is tuned to features of the LFP.

Speed cells in the medial entorhinal cortex.

Grid cells in the medial entorhinal cortex have spatial firing fields that repeat periodically in a hexagonal pattern. When animals move, activity is translated between grid cells in accordance with the animal's displacement in the environment. For this translation to occur, grid cells must have continuous access to information about instantaneous running speed. However, a powerful entorhinal speed signal has not been identified. Here we show that running speed is represented in the firing rate of a ubiquitous but functionally dedicated population of entorhinal neurons distinct from other cell populations of the local circuit, such as grid, head-direction and border cells. These 'speed cells' are characterized by a context-invariant positive, linear response to running speed, and share with grid cells a prospective bias of ∼50-80 ms. Our observations point to speed cells as a key component of the dynamic representation of self-location in the medial entorhinal cortex.

Dynamic role of adult-born dentate granule cells in memory processing.

Throughout the adult life of all mammals including humans, new neurons are incorporated to the dentate gyrus of the hippocampus. During a critical window that lasts about two weeks, adult-born immature neurons are more excitable and plastic than mature ones, and they respond to a wider range of inputs. In apparent contradiction, new neurons have been shown to be crucial to solve behavioral tasks that involve the discrimination of very similar situations, which would instead require high input specificity. We propose that immature neurons are initially unspecific because their task is to identify novel elements inside a high dimensional input space. With maturation, they would specialize to represent details of these novel inputs, favoring discrimination.

Delayed coupling to feedback inhibition during a critical period for the integration of adult-born granule cells.

Developing granule cells (GCs) of the adult dentate gyrus undergo a critical period of enhanced activity and synaptic plasticity before becoming mature. The impact of developing GCs on the activity of preexisting dentate circuits remains unknown. Here we combine optogenetics, acute slice electrophysiology, and in vivo chemogenetics to activate GCs at different stages of maturation to study the recruitment of local target networks. We show that immature (4-week-old) GCs can efficiently drive distal CA3 targets but poorly activate proximal interneurons responsible for feedback inhibition (FBI). As new GCs transition toward maturity, they reliably recruit GABAergic feedback loops that restrict spiking of neighbor GCs, a mechanism that would promote sparse coding. Such inhibitory loop impinges only weakly in new cohorts of young GCs. A computational model reveals that the delayed coupling of new GCs to FBI could be crucial to achieve a fine-grain representation of novel inputs in the dentate gyrus.

Grid maps for spaceflight, anyone? They are for free!

We show that, given extensive exploration of a three-dimensional volume, grid units can form with the approximate periodicity of a face-centered cubic crystal, as the spontaneous product of a self-organizing process at the single unit level, driven solely by firing rate adaptation.

Grid alignment in entorhinal cortex.

The spatial responses of many of the cells recorded in all layers of rodent medial entorhinal cortex (mEC) show mutually aligned grid patterns. Recent experimental findings have shown that grids can often be better described as elliptical rather than purely circular and that, beyond the mutual alignment of their grid axes, ellipses tend to also orient their long axis along preferred directions. Are grid alignment and ellipse orientation aspects of the same phenomenon? Does the grid alignment result from single-unit mechanisms or does it require network interactions? We address these issues by refining a single-unit adaptation model of grid formation, to describe specifically the spontaneous emergence of conjunctive grid-by-head-direction cells in layers III, V, and VI of mEC. We find that tight alignment can be produced by recurrent collateral interactions, but this requires head-direction (HD) modulation. Through a competitive learning process driven by spatial inputs, grid fields then form already aligned, and with randomly distributed spatial phases. In addition, we find that the self-organization process is influenced by any anisotropy in the behavior of the simulated rat. The common grid alignment often orients along preferred running directions (RDs), as induced in a square environment. When speed anisotropy is present in exploration behavior, the shape of individual grids is distorted toward an ellipsoid arrangement. Speed anisotropy orients the long ellipse axis along the fast direction. Speed anisotropy on its own also tends to align grids, even without collaterals, but the alignment is seen to be loose. Finally, the alignment of spatial grid fields in multiple environments shows that the network expresses the same set of grid fields across environments, modulo a coherent rotation and translation. Thus, an efficient metric encoding of space may emerge through spontaneous pattern formation at the single-unit level, but it is coherent, hence context-invariant, if aided by collateral interactions.

Representation of geometric borders in the entorhinal cortex.

We report the existence of an entorhinal cell type that fires when an animal is close to the borders of the proximal environment. The orientation-specific edge-apposing activity of these "border cells" is maintained when the environment is stretched and during testing in enclosures of different size and shape in different rooms. Border cells are relatively sparse, making up less than 10% of the local cell population, but can be found in all layers of the medial entorhinal cortex as well as the adjacent parasubiculum, often intermingled with head-direction cells and grid cells. Border cells may be instrumental in planning trajectories and anchoring grid fields and place fields to a geometric reference frame.

The emergence of grid cells: Intelligent design or just adaptation?

Individual medial entorhinal cortex (mEC) 'grid' cells provide a representation of space that appears to be essentially invariant across environments, modulo simple transformations, in contrast to multiple, rapidly acquired hippocampal maps; it may therefore be established gradually during rodent development. We explore with a simplified mathematical model the possibility that the self-organization of multiple grid fields into a triangular grid pattern may be a single-cell process, driven by firing rate adaptation and slowly varying spatial inputs. A simple analytical derivation indicates that triangular grids are favored asymptotic states of the self-organizing system, and computer simulations confirm that such states are indeed reached during a model learning process, provided it is sufficiently slow to effectively average out fluctuations. The interactions among local ensembles of grid units serve solely to stabilize a common grid orientation. Spatial information, in the real mEC network, may be provided by any combination of feedforward cortical afferents and feedback hippocampal projections from place cells, since either input alone is likely sufficient to yield grid fields.

Place cells, grid cells, and the brain's spatial representation system.

More than three decades of research have demonstrated a role for hippocampal place cells in representation of the spatial environment in the brain. New studies have shown that place cells are part of a broader circuit for dynamic representation of self-location. A key component of this network is the entorhinal grid cells, which, by virtue of their tessellating firing fields, may provide the elements of a path integration-based neural map. Here we review how place cells and grid cells may form the basis for quantitative spatiotemporal representation of places, routes, and associated experiences during behavior and in memory. Because these cell types have some of the most conspicuous behavioral correlates among neurons in nonsensory cortical systems, and because their spatial firing structure reflects computations internally in the system, studies of entorhinal-hippocampal representations may offer considerable insight into general principles of cortical network dynamics.

Uninformative memories will prevail: the storage of correlated representations and its consequences.

Autoassociative networks were proposed in the 80's as simplified models of memory function in the brain, using recurrent connectivity with Hebbian plasticity to store patterns of neural activity that can be later recalled. This type of computation has been suggested to take place in the CA3 region of the hippocampus and at several levels in the cortex. One of the weaknesses of these models is their apparent inability to store correlated patterns of activity. We show, however, that a small and biologically plausible modification in the "learning rule" (associating to each neuron a plasticity threshold that reflects its popularity) enables the network to handle correlations. We study the stability properties of the resulting memories (in terms of their resistance to the damage of neurons or synapses), finding a novel property of autoassociative networks: not all memories are equally robust, and the most informative are also the most sensitive to damage. We relate these results to category-specific effects in semantic memory patients, where concepts related to "non-living things" are usually more resistant to brain damage than those related to "living things," a phenomenon suspected to be rooted in the correlation between representations of concepts in the cortex.