Position-theta-phase model of hippocampal place cell activity applied to quantification of running speed modulation of firing rate

Phase locking of hippocampal CA3 neurons to distal CA1 theta oscillations selectively predicts memory performance

How the coordination of neuronal spiking and brain rhythms between hippocampal subregions supports memory function remains elusive. We studied the interregional coordination of CA3 neuronal spiking with CA1 theta oscillations by recording electrophysiological signals along the proximodistal axis of the hippocampus in rats that were performing a high-memory-demand recognition memory task adapted from humans. We found that CA3 population spiking occurs preferentially at the peak of distal CA1 theta oscillations when memory was tested but only when previously encountered stimuli were presented. In addition, decoding analyses revealed that only population cell firing of proximal CA3 together with that of distal CA1 can predict performance at test in the present non-spatial task. Overall, our work demonstrates an important role for the synchronization of CA3 neuronal activity with CA1 theta oscillations during memory testing.

News without the buzz: reading out weak theta rhythms in the hippocampus

Local field potentials (LFPs) reflect the collective dynamics of neural populations, yet their exact relationship to neural codes remains unknown1. One notable exception is the theta rhythm of the rodent hippocampus, which seems to provide a reference clock to decode the animal's position from spatiotemporal patterns of neuronal spiking2 or LFPs3. But when the animal stops, theta becomes irregular4, potentially indicating the breakdown of temporal coding by neural populations. Here we show that no such breakdown occurs, introducing an artificial neural network that can recover position-tuned rhythmic patterns (pThetas) without relying on the more prominent theta rhythm as a reference clock. pTheta and theta preferentially correlate with place cell and interneuron spiking, respectively. When rats forage in an open field, pTheta is jointly tuned to position and head orientation, a property not seen in individual place cells but expected to emerge from place cell sequences5. Our work demonstrates that weak and intermittent oscillations, as seen in many brain regions and species, can carry behavioral information commensurate with population spike codes.

Algebraic approach to spike-time neural codes in the hippocampus

Although temporal coding through spike-time patterns has long been of interest in neuroscience, the specific structures that could be useful for spike-time codes remain highly unclear. Here, we introduce an analytical approach, using techniques from discrete mathematics, to study spike-time codes. As an initial example, we focus on the phenomenon of "phase precession" in the rodent hippocampus. During navigation and learning on a physical track, specific cells in a rodent's brain form a highly structured pattern relative to the oscillation of population activity in this region. Studies of phase precession largely focus on its role in precisely ordering spike times for synaptic plasticity, as the role of phase precession in memory formation is well established. Comparatively less attention has been paid to the fact that phase precession represents one of the best candidates for a spike-time neural code. The precise nature of this code remains an open question. Here, we derive an analytical expression for a function mapping points in physical space to complex-valued spikes by representing individual spike times as complex numbers. The properties of this function make explicit a specific relationship between past and future in spike patterns of the hippocampus. Importantly, this mathematical approach generalizes beyond the specific phenomenon studied here, providing a technique to study the neural codes within precise spike-time sequences found during sensory coding and motor behavior. We then introduce a spike-based decoding algorithm, based on this function, that successfully decodes a simulated animal's trajectory using only the animal's initial position and a pattern of spike times. This decoder is robust to noise in spike times and works on a timescale almost an order of magnitude shorter than typically used with decoders that work on average firing rate. These results illustrate the utility of a discrete approach, based on the structure and symmetries in spike patterns across finite sets of cells, to provide insight into the structure and function of neural systems.

Logarithmically scaled, gamma distributed neuronal spiking

Naturally log-scaled quantities abound in the nervous system. Distributions of these quantities have non-intuitive properties, which have implications for data analysis and the understanding of neural circuits. Here, we review the log-scaled statistics of neuronal spiking and the relevant analytical probability distributions. Recent work using log-scaling revealed that interspike intervals of forebrain neurons segregate into discrete modes reflecting spiking at different timescales and are each well-approximated by a gamma distribution. Each neuron spends most of the time in an irregular spiking 'ground state' with the longest intervals, which determines the mean firing rate of the neuron. Across the entire neuronal population, firing rates are log-scaled and well approximated by the gamma distribution, with a small number of highly active neurons and an overabundance of low rate neurons (the 'dark matter'). These results are intricately linked to a heterogeneous balanced operating regime, which confers upon neuronal circuits multiple computational advantages and has evolutionarily ancient origins.

Computational models for state-dependent traveling waves in hippocampal formation

Hippocampal theta (4-10 Hz) oscillations have been identified as traveling waves in both rodents and humans. In freely foraging rodents, the theta traveling wave is a planar wave propagating from the dorsal to ventral hippocampus along the septotemporal axis. Motivated from experimental findings, we develop a spiking neural network of excitatory and inhibitory neurons to generate state-dependent hippocampal traveling waves to improve current mechanistic understanding of propagating waves. Model simulations demonstrate the necessary conditions for generating wave propagation and characterize the traveling wave properties with respect to model parameters, running speed and brain state of the animal. Networks with long-range inhibitory connections are more suitable than networks with long-range excitatory connections. We further generalize the spiking neural network to model traveling waves in the medial entorhinal cortex (MEC) and predict that traveling theta waves in the hippocampus and entorhinal cortex are in sink.

Sampling motion trajectories during hippocampal theta sequences

Efficient planning in complex environments requires that uncertainty associated with current inferences and possible consequences of forthcoming actions is represented. Representation of uncertainty has been established in sensory systems during simple perceptual decision making tasks but it remains unclear if complex cognitive computations such as planning and navigation are also supported by probabilistic neural representations. Here, we capitalized on gradually changing uncertainty along planned motion trajectories during hippocampal theta sequences to capture signatures of uncertainty representation in population responses. In contrast with prominent theories, we found no evidence of encoding parameters of probability distributions in the momentary population activity recorded in an open-field navigation task in rats. Instead, uncertainty was encoded sequentially by sampling motion trajectories randomly and efficiently in subsequent theta cycles from the distribution of potential trajectories. Our analysis is the first to demonstrate that the hippocampus is well equipped to contribute to optimal planning by representing uncertainty.

Inhibitory conductance controls place field dynamics in the hippocampus

Hippocampal place cells receive a disparate collection of excitatory and inhibitory currents that endow them with spatially selective discharges and rhythmic activity. Using a combination of in vivo intracellular and extracellular recordings with opto/chemogenetic manipulations and computational modeling, we investigate the influence of inhibitory and excitatory inputs on CA1 pyramidal cell responses. At the cell bodies, inhibition leads and is stronger than excitation across the entire theta cycle. Pyramidal neurons fire on the ascending phase of theta when released from inhibition. Computational models equipped with the observed conductances reproduce these dynamics. In these models, place field properties are favored when the increased excitation is coupled with a reduction of inhibition within the field. As predicted by our simulations, firing rate within place fields and phase locking to theta are impaired by DREADDs activation of interneurons. Our results indicate that decreased inhibitory conductance is critical for place field expression.

Valero et al. examine the influence of inhibition on place fields. They show that hippocampal neurons are dominated by inhibitory conductances during theta oscillations. A transient increase of excitation and drop of inhibition mediates place field emergence in simulations. Consistently, chemogenetic activation of interneurons deteriorates place cell properties in vivo.

GABAergic CA1 neurons are more stable following context changes than glutamatergic cells

The CA1 region of the hippocampus contains both glutamatergic pyramidal cells and GABAergic interneurons. Numerous reports have characterized glutamatergic CAMK2A cell activity, showing how these cells respond to environmental changes such as local cue rotation and context re-sizing. Additionally, the long-term stability of spatial encoding and turnover of these cells across days is also well-characterized. In contrast, these classic hippocampal experiments have never been conducted with CA1 GABAergic cells. Here, we use chronic calcium imaging of male and female mice to compare the neural activity of VGAT and CAMK2A cells during exploration of unaltered environments and also during exposure to contexts before and after rotating and changing the length of the context across multiple recording days. Intriguingly, compared to CAMK2A cells, VGAT cells showed decreased remapping induced by environmental changes, such as context rotations and contextual length resizing. However, GABAergic neurons were also less likely than glutamatergic neurons to remain active and exhibit consistent place coding across recording days. Interestingly, despite showing significant spatial remapping across days, GABAergic cells had stable speed encoding between days. Thus, compared to glutamatergic cells, spatial encoding of GABAergic cells is more stable during within-session environmental perturbations, but is less stable across days. These insights may be crucial in accurately modeling the features and constraints of hippocampal dynamics in spatial coding.

Distributed Phase Oscillatory Excitation Efficiently Produces Attractors Using Spike-Timing-Dependent Plasticity

The brain is thought to represent information in the form of activity in distributed groups of neurons known as attractors. We show here that in a randomly connected network of simulated spiking neurons, periodic stimulation of neurons with distributed phase offsets, along with standard spike-timing-dependent plasticity (STDP), efficiently creates distributed attractors. These attractors may have a consistent ordered firing pattern or become irregular, depending on the conditions. We also show that when two such attractors are stimulated in sequence, the same STDP mechanism can create a directed association between them, forming the basis of an associative network. We find that for an STDP time constant of 20 ms, the dependence of the efficiency of attractor creation on the driving frequency has a broad peak centered around 8 Hz. Upon restimulation, the attractors self-oscillate, but with an oscillation frequency that is higher than the driving frequency, ranging from 10 to 100 Hz.

Preexisting hippocampal network dynamics constrain optogenetically induced place fields

Memory models often emphasize the need to encode novel patterns of neural activity imposed by sensory drive. Prior learning and innate architecture likely restrict neural plasticity, however. Here, we test how the incorporation of synthetic hippocampal signals is constrained by preexisting circuit dynamics. We optogenetically stimulated small groups of CA1 neurons as mice traversed a chosen segment of a linear track, mimicking the emergence of place fields. Stimulation induced persistent place field remapping in stimulated and non-stimulated neurons. The emergence of place fields could be predicted from sporadic firing in the new place field location and the temporal relationship to peer neurons before the optogenetic perturbation. Circuit modification was reflected by altered spike transmission between connected pyramidal cells and inhibitory interneurons, which persisted during post-experience sleep. We hypothesize that optogenetic perturbation unmasked sub-threshold place fields. Plasticity in recurrent/lateral inhibition may drive learning through the rapid association of existing states.

Lack of redundancy between electrophysiological measures of long-range neuronal communication

Background

Communication between brain areas has been implicated in a wide range of cognitive and emotive functions and is impaired in numerous mental disorders. In rodent models, various metrics have been used to quantify inter-regional neuronal communication. However, in individual studies, typically, only very few measures of coupling are reported and, hence, redundancy across such indicators is implicitly assumed.

Results

In order to test this assumption, we here comparatively assessed a broad range of directional and non-directional metrics like coherence, Weighted Phase Lag Index (wPLI), phase-locking value (PLV), pairwise phase consistency (PPC), parametric and non-parametric Granger causality (GC), partial directed coherence (PDC), directed transfer function (DTF), spike-phase coupling (SPC), cross-regional phase-amplitude coupling, amplitude cross-correlations and others. We applied these analyses to simultaneous field recordings from the prefrontal cortex and the ventral and dorsal hippocampus in the schizophrenia-related Gria1-knockout mouse model which displays a robust novelty-induced hyperconnectivity phenotype. Using the detectability of coupling deficits in Gria1^−/− mice and bivariate correlations within animals as criteria, we found that across such measures, there is a considerable lack of functional redundancy. Except for three pairwise correlations—PLV with PPC, PDC with DTF and parametric with non-parametric Granger causality—almost none of the analysed metrics consistently co-varied with any of the other measures across the three connections and two genotypes analysed. Notable exceptions to this were the correlation of coherence with PPC and PLV that was found in most cases, and partial correspondence between these three measures and Granger causality. Perhaps most surprisingly, partial directed coherence and Granger causality—sometimes regarded as equivalent measures of directed influence—diverged profoundly. Also, amplitude cross-correlation, spike-phase coupling and theta-gamma phase-amplitude coupling each yielded distinct results compared to all other metrics.

Conclusions

Our analysis highlights the difficulty of quantifying real correlates of inter-regional information transfer, underscores the need to assess multiple coupling measures and provides some guidelines which metrics to choose for a comprehensive, yet non-redundant characterization of functional connectivity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-00950-4.

Place recognition with deep superpixel features for brain-inspired navigation

Navigation in primates is generally supported by cognitive maps. Such a map endows an animal with navigational planning capabilities. Numerous methods have been proposed to mimic these natural navigation capabilities in artificial systems. Based on self-navigation and learning strategies in animals, we propose in this work a place recognition strategy for brain-inspired navigation. First, a place recognition algorithm structure based on convolutional neural networks (CNNs) is introduced, which can be applied in the field of intelligent navigation. Second, sufficient images are captured at each landmark and then stored as a reference image library. Simple linear iterative clustering (SLIC) is used to segment each image into superpixels with multi-scale viewpoint-invariant landmarks. Third, highly representative appearance-independent features are extracted from these landmarks through CNNs. In addition, spatial pyramid pooling (SPP) layers are introduced to generate a fixed-length CNN representation, regardless of the image size. This representation boosts the quality of the extracted landmark features. The proposed SLIC-SPP-CNN place recognition algorithm is evaluated on one collected dataset and two public datasets with viewpoint and appearance variations.

Altered Hippocampal Place Cell Representation and Theta Rhythmicity following Moderate Prenatal Alcohol Exposure

Prenatal alcohol exposure (PAE) leads to profound deficits in spatial memory and synaptic and cellular alterations to the hippocampus that last into adulthood. Neurons in the hippocampus called place cells discharge as an animal enters specific places in an environment, establish distinct ensemble codes for familiar and novel places, and are modulated by local theta rhythms. Spatial memory is thought to critically depend on the integrity of hippocampal place cell firing. Therefore, we tested the hypothesis that hippocampal place cell firing is impaired after PAE by performing in vivo recordings from the hippocampi (CA1 and CA3) of moderate PAE and control adult rats. Our results show that hippocampal CA3 neurons from PAE rats have reduced spatial tuning. Second, CA1 and CA3 neurons from PAE rats are less likely to orthogonalize their firing between directions of travel on a linear track and between changes in contextual stimuli in an open arena compared to control neurons. Lastly, reductions in the number of hippocampal place cells exhibiting significant theta rhythmicity and phase precession were observed, which may suggest changes to hippocampal microcircuit function. Together, the reduced spatial tuning and sensitivity to contextual changes provide a neural systems-level mechanism to explain spatial memory impairment after moderate PAE.

Cell type, sub-region, and layer-specific speed representation in the hippocampal-entorhinal circuit

It has been hypothesised that speed information, encoded by ‘speed cells’, is important for updating spatial representation in the hippocampus and entorhinal cortex to reflect ongoing self-movement during locomotion. However, systematic characterisation of speed representation is still lacking. In this study, we compared the speed representation of distinct cell types across sub-regions/layers in the dorsal hippocampus and medial entorhinal cortex of rats during exploration. Our results indicate that the preferred theta phases of individual neurons are correlated with positive/negative speed modulation and a temporal shift of speed representation in a sub-region/layer and cell type-dependent manner. Most speed cells located in entorhinal cortex layer 2 represented speed prospectively, whereas those in the CA1 and entorhinal cortex layers 3 and 5 represented speed retrospectively. In entorhinal cortex layer 2, putative CA1-projecting pyramidal cells, but not putative dentate gyrus/CA3-projecting stellate cells, represented speed prospectively. Among the hippocampal interneurons, approximately one-third of putative dendrite-targeting (somatostatin-expressing) interneurons, but only a negligible fraction of putative soma-targeting (parvalbumin-expressing) interneurons, showed negative speed modulation. Putative parvalbumin-expressing CA1 interneurons and somatostatin-expressing CA3 interneurons represented speed more retrospectively than parvalbumin-expressing CA3 interneurons. These findings indicate that speed representation in the hippocampal-entorhinal circuit is cell-type, pathway, and theta-phase dependent.