Loren M. Frank

Trajectory Encoding in the Hippocampus and Entorhinal Cortex

We recorded from single neurons in the hippocampus and entorhinal cortex (EC) of rats to investigate the role of these structures in navigation and memory representation. Our results revealed two novel phenomena: first, many cells in CA1 and the EC fired at significantly different rates when the animal was in the same position depending on where the animal had come from or where it was going. Second, cells in deep layers of the EC, the targets of hippocampal outputs, appeared to represent the similarities between locations on spatially distinct trajectories through the environment. Our findings suggest that the hippocampus represents the animals position in the context of a trajectory through space and that the EC represents regularities across different trajectories that could allow for generalization across experiences.

Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval

The hippocampus is required for the encoding, consolidation and retrieval of event memories. Although the neural mechanisms that underlie these processes are only partially understood, a series of recent papers point to awake memory replay as a potential contributor to both consolidation and retrieval. Replay is the sequential reactivation of hippocampal place cells that represent previously experienced behavioral trajectories and occurs frequently in the awake state, particularly during periods of relative immobility. Awake replay may reflect trajectories through either the current environment or previously visited environments that are spatially remote. The repetition of learned sequences on a compressed time scale is well suited to promote memory consolidation in distributed circuits beyond the hippocampus, suggesting that consolidation occurs in both the awake and sleeping animal. Moreover, sensory information can influence the content of awake replay, suggesting a role for awake replay in memory retrieval.

The time-rescaling theorem and its application to neural spike train data analysis

Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the models validity prior to using it to make inferences about a particular neural system. Assessing goodness-of-fit is a challenging problem for point process neural spike train models, especially for histogram-based models such as perstimulus time histograms (PSTH) and rate functions estimated by spike train smoothing. The time-rescaling theorem is a well-known result in probability theory, which states that any point process with an integrable conditional intensity function may be transformed into a Poisson process with unit rate. We describe how the theorem may be used to develop goodness-of-fit tests for both parametric and histogram-based point process models of neural spike trains. We apply these tests in two examples: a comparison of PSTH, inhomogeneous Poisson, and inhomogeneous Markov interval models of neural spike trains from the supplementary eye field of a macque monkey and a comparison of temporal and spatial smoothers, inhomogeneous Poisson, inhomogeneous gamma, and inhomogeneous inverse gaussian models of rat hippocampal place cell spiking activity. To help make the logic behind the time-rescaling theorem more accessible to researchers in neuroscience, we present a proof using only elementary probability theory arguments.We also show how the theorem may be used to simulate a general point process model of a spike train. Our paradigm makes it possible to compare parametric and histogram-based neural spike train models directly. These results suggest that the time-rescaling theorem can be a valuable tool for neural spike train data analysis.

Awake replay of remote experiences in the hippocampus

Hippocampal replay is thought to be essential for the consolidation of event memories in hippocampal-neocortical networks. Replay is present during both sleep and waking behavior, but although sleep replay involves the reactivation of stored representations in the absence of specific sensory inputs, awake replay is thought to depend on sensory input from the current environment. Here, we show that stored representations are reactivated during both waking and sleep replay. We found frequent awake replay of sequences of rat hippocampal place cells from a previous experience. This spatially remote replay was as common as local replay of the current environment and was more robust when the rat had recently been in motion than during extended periods of quiescence. Our results indicate that the hippocampus consistently replays past experiences during brief pauses in waking behavior, suggesting a role for waking replay in memory consolidation and retrieval.

Awake Hippocampal Sharp-Wave Ripples Support Spatial Memory

Spatial Memory Perturbation The hippocampus is important for learning and memory. However, it is not clear which patterns of neural activity in the hippocampus support specific mnemonic functions. Jadhav et al. (p. 1454, published online 3 May) developed a real-time analysis system to detect and selectively interrupt a certain type of hippocampal neuronal network event—sharp-wave ripples—during learning. In awake animals, loss of sharp-wave ripples and associated memory replay activity caused a learning deficit specific to spatial working memory but had no effect on reference memory. This learning deficit was present despite the preservation of place-field representations and replay activity during rest. The neuronal “replay” of past experience may allow animals to retrieve specific memories and use them to guide behavior. The hippocampus is critical for spatial learning and memory. Hippocampal neurons in awake animals exhibit place field activity that encodes current location, as well as sharp-wave ripple (SWR) activity during which representations based on past experiences are often replayed. The relationship between these patterns of activity and the memory functions of the hippocampus is poorly understood. We interrupted awake SWRs in animals learning a spatial alternation task. We observed a specific learning and performance deficit that persisted throughout training. This deficit was associated with awake SWR activity, as SWR interruption left place field activity and post-experience SWR reactivation intact. These results provide a link between awake SWRs and hippocampal memory processes, which suggests that awake replay of memory-related information during SWRs supports learning and memory-guided decision-making.

A prefrontal cortex-brainstem neuronal projection that controls response to behavioural challenge

The prefrontal cortex (PFC) is thought to participate in high-level control of the generation of behaviours (including the decision to execute actions); indeed, imaging and lesion studies in human beings have revealed that PFC dysfunction can lead to either impulsive states with increased tendency to initiate action, or to amotivational states characterized by symptoms such as reduced activity, hopelessness and depressed mood. Considering the opposite valence of these two phenotypes as well as the broad complexity of other tasks attributed to PFC, we sought to elucidate the PFC circuitry that favours effortful behavioural responses to challenging situations. Here we develop and use a quantitative method for the continuous assessment and control of active response to a behavioural challenge, synchronized with single-unit electrophysiology and optogenetics in freely moving rats. In recording from the medial PFC (mPFC), we observed that many neurons were not simply movement-related in their spike-firing patterns but instead were selectively modulated from moment to moment, according to the animal’s decision to act in a challenging situation. Surprisingly, we next found that direct activation of principal neurons in the mPFC had no detectable causal effect on this behaviour. We tested whether this behaviour could be causally mediated by only a subclass of mPFC cells defined by specific downstream wiring. Indeed, by leveraging optogenetic projection-targeting to control cells with specific efferent wiring patterns, we found that selective activation of those mPFC cells projecting to the brainstem dorsal raphe nucleus (DRN), a serotonergic nucleus implicated in major depressive disorder, induced a profound, rapid and reversible effect on selection of the active behavioural state. These results may be of importance in understanding the neural circuitry underlying normal and pathological patterns of action selection and motivation in behaviour.

Optetrode: a multichannel readout for optogenetic control in freely moving mice

Recent advances in optogenetics have improved the precision with which defined circuit elements can be controlled optically in freely moving mammals; in particular, recombinase-dependent opsin viruses, used with a growing pool of transgenic mice expressing recombinases, allow manipulation of specific cell types. However, although optogenetic control has allowed neural circuits to be manipulated in increasingly powerful ways, combining optogenetic stimulation with simultaneous multichannel electrophysiological readout of isolated units in freely moving mice remains a challenge. We designed and validated the optetrode, a device that allows for colocalized multi-tetrode electrophysiological recording and optical stimulation in freely moving mice. Optetrode manufacture employs a unique optical fiber-centric coaxial design approach that yields a lightweight (2 g), compact and robust device that is suitable for behaving mice. This low-cost device is easy to construct (2.5 h to build without specialized equipment). We found that the drive design produced stable high-quality recordings and continued to do so for at least 6 weeks following implantation. We validated the optetrode by quantifying, for the first time, the response of cells in the medial prefrontal cortex to local optical excitation and inhibition, probing multiple different genetically defined classes of cells in the mouse during open field exploration.

Dynamic Analysis of Learning in Behavioral Experiments

Understanding how an animals ability to learn relates to neural activity or is altered by lesions, different attentional states, pharmacological interventions, or genetic manipulations are central questions in neuroscience. Although learning is a dynamic process, current analyses do not use dynamic estimation methods, require many trials across many animals to establish the occurrence of learning, and provide no consensus as how best to identify when learning has occurred. We develop a state-space model paradigm to characterize learning as the probability of a correct response as a function of trial number (learning curve). We compute the learning curve and its confidence intervals using a state-space smoothing algorithm and define the learning trial as the first trial on which there is reasonable certainty (>0.95) that a subject performs better than chance for the balance of the experiment. For a range of simulated learning experiments, the smoothing algorithm estimated learning curves with smaller mean integrated squared error and identified the learning trials with greater reliability than commonly used methods. The smoothing algorithm tracked easily the rapid learning of a monkey during a single session of an association learning experiment and identified learning 2 to 4 d earlier than accepted criteria for a rat in a 47 d procedural learning experiment. Our state-space paradigm estimates learning curves for single animals, gives a precise definition of learning, and suggests a coherent statistical framework for the design and analysis of learning experiments that could reduce the number of animals and trials per animal that these studies require.

Construction and analysis of non-Poisson stimulus-response models of neural spiking activity

A paradigm for constructing and analyzing non-Poisson stimulus-response models of neural spike train activity is presented. Inhomogeneous gamma (IG) and inverse Gaussian (IIG) probability models are constructed by generalizing the derivation of the inhomogeneous Poisson (IP) model from the exponential probability density. The resultant spike train models have Markov dependence. Quantile-quantile (Q-Q) plots and Kolmogorov-Smirnov (K-S) plots are developed based on the rate-rescaling theorem to assess model goodness-of-fit. The analysis also expresses the spike rate function of the neuron directly in terms of its interspike interval (ISI) distribution. The methods are illustrated with an analysis of 34 spike trains from rat CA1 hippocampal pyramidal neurons recorded while the animal executed a behavioral task. The stimulus in these experiments is the animals position in its environment and the response is the neural spiking activity. For all 34 pyramidal cells, the IG and IIG models gave better fits to the spike trains than the IP. The IG model more accurately described the frequency of longer ISIs, whereas the IIG model gave the best description of the burst frequency, i.e. ISIs < or = 20 ms. The findings suggest that bursts are a significant component of place cell spiking activity even when position and the background variable, theta phase, are taken into account. Unlike the Poisson model, the spatial and temporal rate maps of the IG and IIG models depend directly on the spiking history of the neurons. These rate maps are more physiologically plausible since the interaction between space and time determines local spiking propensity. While this statistical paradigm is being developed to study information encoding by rat hippocampal neurons, the framework should be applicable to stimulus-response experiments performed in other neural systems.

Rewarded Outcomes Enhance Reactivation of Experience in the Hippocampus

Remembering experiences that lead to reward is essential for survival. The hippocampus is required for forming and storing memories of events and places, but the mechanisms that associate specific experiences with rewarding outcomes are not understood. Event memory storage is thought to depend on the reactivation of previous experiences during hippocampal sharp wave ripples (SWRs). We used a sequence switching task that allowed us to examine the interaction between SWRs and reward. We compared SWR activity after animals traversed spatial trajectories and either received or did not receive a reward. Here, we show that rat hippocampal CA3 principal cells are significantly more active during SWRs following receipt of reward. This SWR activity was further enhanced during learning and reactivated coherent elements of the paths associated with the reward location. This enhanced reactivation in response to reward could be a mechanism to bind rewarding outcomes to the experiences that precede them.