Single-Trial Phase Precession in the Hippocampus

During the crossing of the place field of a pyramidal cell in the rat hippocampus, the firing phase of the cell decreases with respect to the local theta rhythm. This phase precession is usually studied on the basis of data in which many place field traversals are pooled together. Here we study properties of phase precession in single trials. We found that single-trial and pooled-trial phase precession were different with respect to phase-position correlation, phase-time correlation, and phase range. Whereas pooled-trial phase precession may span 360°, the most frequent single-trial phase range was only ∼180°. In pooled trials, the correlation between phase and position (r = −0.58) was stronger than the correlation between phase and time (r = −0.27), whereas in single trials these correlations (r = −0.61 for both) were not significantly different. Next, we demonstrated that phase precession exhibited a large trial-to-trial variability. Overall, only a small fraction of the trial-to-trial variability in measures of phase precession (e.g., slope or offset) could be explained by other single-trial properties (such as running speed or firing rate), whereas the larger part of the variability remains to be explained. Finally, we found that surrogate single trials, created by randomly drawing spikes from the pooled data, are not equivalent to experimental single trials: pooling over trials therefore changes basic measures of phase precession. These findings indicate that single trials may be better suited for encoding temporally structured events than is suggested by the pooled data

Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity

We study the storage and retrieval of phase-coded patterns as stable dynamical attractors in recurrent neural networks, for both an analog and a integrate-and-fire spiking model. The synaptic strength is determined by a learning rule based on spike-time-dependent plasticity, with an asymmetric time window depending on the relative timing between pre- and post-synaptic activity. We store multiple patterns and study the network capacity. For the analog model, we find that the network capacity scales linearly with the network size, and that both capacity and the oscillation frequency of the retrieval state depend on the asymmetry of the learning time window. In addition to fully-connected networks, we study sparse networks, where each neuron is connected only to a small number z << N of other neurons. Connections can be short range, between neighboring neurons placed on a regular lattice, or long range, between randomly chosen pairs of neurons. We find that a small fraction of long range connections is able to amplify the capacity of the network. This imply that a small-world-network topology is optimal, as a compromise between the cost of long range connections and the capacity increase. Also in the spiking integrate and fire model the crucial result of storing and retrieval of multiple phase-coded patterns is observed. The capacity of the fully-connected spiking network is investigated, together with the relation between oscillation frequency of retrieval state and window asymmetry

The medial entorhinal cortex is necessary for temporal organization of hippocampal neuronal activity.

The superficial layers of the medial entorhinal cortex (MEC) are a major input to the hippocampus. The high proportion of spatially modulated cells, including grid cells and border cells, in these layers suggests that MEC inputs are critical for the representation of space in the hippocampus. However, selective manipulations of the MEC do not completely abolish hippocampal spatial firing. To determine whether other hippocampal firing characteristics depend more critically on MEC inputs, we recorded from hippocampal CA1 cells in rats with MEC lesions. Theta phase precession was substantially disrupted, even during periods of stable spatial firing. Our findings indicate that MEC inputs to the hippocampus are required for the temporal organization of hippocampal firing patterns and suggest that cognitive functions that depend on precise neuronal sequences in the hippocampal theta cycle are particularly dependent on the MEC

Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network.

Grid cells fire in sequences that represent rapid trajectories in space. During locomotion, theta sequences encode sweeps in position starting slightly behind the animal and ending ahead of it. During quiescence and slow wave sleep, bouts of synchronized activity represent long trajectories called replays, which are well-established in place cells and have been recently reported in grid cells. Theta sequences and replay are hypothesized to facilitate many cognitive functions, but their underlying mechanisms are unknown. One mechanism proposed for grid cell formation is the continuous attractor network. We demonstrate that this established architecture naturally produces theta sequences and replay as distinct consequences of modulating external input. Driving inhibitory interneurons at the theta frequency causes attractor bumps to oscillate in speed and size, which gives rise to theta sequences and phase precession, respectively. Decreasing input drive to all neurons produces traveling wavefronts of activity that are decoded as replays

State-dependencies of learning across brain scales

Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous “spontaneous” shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly

The role of the medial entorhinal cortex in spatial and temporal coding

The hippocampus (HIPP) is the core of a memory system crucial for the formation of new episodic (unique event) memories in humans and episodic-like memories (for what, where and when) in rodents. Its prevalent role in the formation of memories is thought to rely on a variety of specialized neural network computations: It is for example believed that hippocampal networks associate information about different aspects of an experience (such as a particular event and the place at which the event occurred) into a coherent memory trace. In order to prevent interference between memories that are similar (such as two different experiences within the same place) each memory is assigned a neural code that is highly distinct from those for previously acquired memories. Finally, hippocampal networks are thought to fuse memories for individual fragments of an experience into a temporally structured sequence which represent an episode. Information about different aspects of an experience reaches the HIPP via the entorhinal cortex (EC), which is its major cortical input structure. Electrophysiological single-unit recordings in behaving rodents revealed that in particular the medial division of the EC (MEC) contains a variety of cell types that are specialized in the representation of spatial and self-motion information. It is therefore believed that input from the MEC supports the spatial component of memory processing in the HIPP. Here, we tested the long-standing hypothesis that hippocampal spatial coding relies on input from the MEC. This was achieved by performing extensive, bilateral excitotoxic lesions of the MEC and placing electrode arrays into the CA1 pyramidal cell layer of the HIPP. Hippocampal neural computations were assessed by recording extracellular action potentials (APs) from individual neurons as rats explored open field environments. The firing patterns of hippocampal neurons are known to correlate with the rat’s behavior, in that each cell fires APs at restricted proportions of the environment, forming spatial receptive fields (so-called place fields). The spatial precision and organization of those place fields was examined in control and MEC-lesioned rats. We found that hippocampal neurons retained their spatial selectivity after MEC lesions, even though the precision and stability of the hippocampal spatial code were reduced. The ability to form distinct spatial representation for different environments was entirely intact in MEC-lesioned rats. Contrary to most contemporary theories of hippocampo-entorhinal function, our findings suggest that the MEC is not the only determinant of hippocampal spatial computations and that sources lacking sophisticated spatial firing, such as the lateral division of the entorhinal cortex (LEC) and local hippocampal network computations are sufficient to support this function. Following the finding that spatial firing was partly preserved in MEC-lesioned rats, we tested whether the MEC is necessary for the temporal organization of spike timing within the place field. Hippocampal place cells that are activated along the rat’s trajectory through space are thought to be linked into synaptically connected neuronal sequences via a mechanisms referred to as hippocampal theta phase precession (hTPP). Theta phase precession reflects the temporal distribution of APs within each place field with reference to the local field potential (LFP) oscillation at theta frequency (4 to 10 Hz). We found that hTPP was strongly disrupted in MEC-lesioned rats, demonstrating that the MEC is necessary for the temporal organization of hippocampal spatial firing. Cognitive functions that rely on sequentially activated place cells are thus likely to rely on the MEC. In summary, the presented data demonstrate that the contribution of the MEC to hippocampal spatial coding is less predominant than postulated by contemporary theories of hippocampo-entorhinal function. In addition, the findings suggest that the MEC, which is widely considered a spatial processing center of the brain, supports memory through the temporal organization of hippocampal spatial firing