Cell assembly sequences arising from spike threshold adaptation keep track of time in the hippocampus

Integration and competition between space and time in the hippocampus

Episodic memory is organized in both spatial and temporal contexts. The hippocampus is crucial for episodic memory and has been demonstrated to encode spatial and temporal information. However, how the representations of space and time interact in the hippocampal memory system is still unclear. Here, we recorded the activity of hippocampal CA1 neurons in mice in a variety of one-dimensional navigation tasks while systematically varying the speed of the animals. For all tasks, we found neurons simultaneously represented space and elapsed time. There was a negative correlation between the preferred space and lap duration, e.g., the preferred spatial position shifted more toward the origin when the lap duration became longer. A similar relationship between the preferred time and traveled distance was also observed. The results strongly suggest a competitive and integrated representation of space-time by single hippocampal neurons, which may provide the neural basis for spatiotemporal contexts.

Self-avoidance dominates the selection of hippocampal replay

Spontaneous neural activity sequences are generated by the brain in the absence of external input 1-12 , yet how they are produced remains unknown. During immobility, hippocampal replay sequences depict spatial paths related to the animal's past experience or predicted future 13 . By recording from large ensembles of hippocampal place cells 14 in combination with optogenetic manipulation of cortical input in freely behaving rats, we show here that the selection of hippocampal replay is governed by a novel self-avoidance principle. Following movement cessation, replay of the animal's past path is strongly avoided, while replay of the future path predominates. Moreover, when the past and future paths overlap, early replays avoid both and depict entirely different trajectories. Further, replays avoid self-repetition, on a shorter timescale compared to the avoidance of previous behavioral trajectories. Eventually, several seconds into the stopping period, replay of the past trajectory dominates. This temporal organization contrasts with established and recent predictions 9,10,15,16 but is well-recapitulated by a symmetry-breaking attractor model of sequence generation in which individual neurons adapt their firing rates over time 26-35 . However, while the model is sufficient to produce avoidance of recently traversed or reactivated paths, it requires an additional excitatory input into recently activated cells to produce the later window of past-dominance. We performed optogenetic perturbations to demonstrate that this input is provided by medial entorhinal cortex, revealing its role in maintaining a memory of past experience that biases hippocampal replay. Together, these data provide specific evidence for how hippocampal replays are generated.

Neuronal activation sequences in lateral prefrontal cortex encode visuospatial working memory during virtual navigation

Working memory (WM) is the ability to maintain and manipulate information 'in mind'. The neural codes underlying WM have been a matter of debate. We simultaneously recorded the activity of hundreds of neurons in the lateral prefrontal cortex of male macaque monkeys during a visuospatial WM task that required navigation in a virtual 3D environment. Here, we demonstrate distinct neuronal activation sequences (NASs) that encode remembered target locations in the virtual environment. This NAS code outperformed the persistent firing code for remembered locations during the virtual reality task, but not during a classical WM task using stationary stimuli and constraining eye movements. Finally, blocking NMDA receptors using low doses of ketamine deteriorated the NAS code and behavioral performance selectively during the WM task. These results reveal the versatility and adaptability of neural codes supporting working memory function in the primate lateral prefrontal cortex.

Self-organization of modular activity in immature cortical networks

During development, cortical activity is organized into distributed modular patterns that are a precursor of the mature columnar functional architecture. Theoretically, such structured neural activity can emerge dynamically from local synaptic interactions through a recurrent network with effective local excitation with lateral inhibition (LE/LI) connectivity. Utilizing simultaneous widefield calcium imaging and optogenetics in juvenile ferret cortex prior to eye opening, we directly test several critical predictions of an LE/LI mechanism. We show that cortical networks transform uniform stimulations into diverse modular patterns exhibiting a characteristic spatial wavelength. Moreover, patterned optogenetic stimulation matching this wavelength selectively biases evoked activity patterns, while stimulation with varying wavelengths transforms activity towards this characteristic wavelength, revealing a dynamic compromise between input drive and the network's intrinsic tendency to organize activity. Furthermore, the structure of early spontaneous cortical activity - which is reflected in the developing representations of visual orientation - strongly overlaps that of uniform opto-evoked activity, suggesting a common underlying mechanism as a basis for the formation of orderly columnar maps underlying sensory representations in the brain.

Working memory features are embedded in hippocampal place fields

Hippocampal principal neurons display both spatial tuning properties and memory features. Whether this distinction corresponds to separate neuron types or a context-dependent continuum has been debated. We report here that the task-context ("splitter") feature is highly variable along both trial and spatial position axes. Neurons acquire or lose splitter features across trials even when place field features remain unaltered. Multiple place fields of the same neuron can individually encode both past or future run trajectories, implying that splitter fields are under the control of assembly activity. Place fields can be differentiated into subfields by the behavioral choice of the animal, and splitting within subfields evolves across trials. Interneurons also differentiate choices by integrating inputs from pyramidal cells. Finally, bilateral optogenetic inactivation of the medial entorhinal cortex reversibly decreases the fraction of splitter fields. Our findings suggest that place or splitter features are different manifestations of the same hippocampal computation.

Self-organization of modular activity in immature cortical networks

During development, cortical activity is organized into distributed modular patterns that are a precursor of the mature columnar functional architecture. Theoretically, such structured neural activity can emerge dynamically from local synaptic interactions through a recurrent network with effective local excitation with lateral inhibition (LE/LI) connectivity. Utilizing simultaneous widefield calcium imaging and optogenetics in juvenile ferret cortex prior to eye opening, we directly test several critical predictions of an LE/LI mechanism. We show that cortical networks transform uniform stimulations into diverse modular patterns exhibiting a characteristic spatial wavelength. Moreover, patterned optogenetic stimulation matching this wavelength selectively biases evoked activity patterns, while stimulation with varying wavelengths transforms activity towards this characteristic wavelength, revealing a dynamic compromise between input drive and the network's intrinsic tendency to organize activity. Furthermore, the structure of early spontaneous cortical activity - which is reflected in the developing representations of visual orientation - strongly overlaps that of uniform opto-evoked activity, suggesting a common underlying mechanism as a basis for the formation of orderly columnar maps underlying sensory representations in the brain.

Hippocampal sequences span experience relative to rewards

Hippocampal place cells fire in sequences that span spatial environments and non-spatial modalities, suggesting that hippocampal activity can anchor to the most behaviorally salient aspects of experience. As reward is a highly salient event, we hypothesized that sequences of hippocampal activity can anchor to rewards. To test this, we performed two-photon imaging of hippocampal CA1 neurons as mice navigated virtual environments with changing hidden reward locations. When the reward moved, the firing fields of a subpopulation of cells moved to the same relative position with respect to reward, constructing a sequence of reward-relative cells that spanned the entire task structure. The density of these reward-relative sequences increased with task experience as additional neurons were recruited to the reward-relative population. Conversely, a largely separate subpopulation maintained a spatially-based place code. These findings thus reveal separate hippocampal ensembles can flexibly encode multiple behaviorally salient reference frames, reflecting the structure of the experience.

Keeping time and rhythm by internal simulation of sensory stimuli and behavioral actions

Effective behavior often requires synchronizing our actions with changes in the environment. Rhythmic changes in the environment are easy to predict, and we can readily time our actions to them. Yet, how the brain encodes and maintains rhythms is not known. Here, we trained primates to internally maintain rhythms of different tempos and performed large-scale recordings of neuronal activity across the sensory-motor hierarchy. Results show that maintaining rhythms engages multiple brain areas, including visual, parietal, premotor, prefrontal, and hippocampal regions. Each recorded area displayed oscillations in firing rates and oscillations in broadband local field potential power that reflected the temporal and spatial characteristics of an internal metronome, which flexibly encoded fast, medium, and slow tempos. The presence of widespread metronome-related activity, in the absence of stimuli and motor activity, suggests that internal simulation of stimuli and actions underlies timekeeping and rhythm maintenance.

Distinct neural adaptations to time demand in the striatum and the hippocampus

How do neural codes adjust to track time across a range of resolutions, from milliseconds to multi-seconds, as a function of the temporal frequency at which events occur? To address this question, we studied time-modulated cells in the striatum and the hippocampus, while macaques categorized three nested intervals within the sub-second or the supra-second range (up to 1, 2, 4, or 8 s), thereby modifying the temporal resolution needed to solve the task. Time-modulated cells carried more information for intervals with explicit timing demand, than for any other interval. The striatum, particularly the caudate, supported the most accurate temporal prediction throughout all time ranges. Strikingly, its temporal readout adjusted non-linearly to the time range, suggesting that the striatal resolution shifted from a precise millisecond to a coarse multi-second range as a function of demand. This is in line with monkey's behavioral latencies, which indicated that they tracked time until 2 s but employed a coarse categorization strategy for durations beyond. By contrast, the hippocampus discriminated only the beginning from the end of intervals, regardless of the range. We propose that the hippocampus may provide an overall poor signal marking an event's beginning, whereas the striatum optimizes neural resources to process time throughout an interval adapting to the ongoing timing necessity.

Time for Memories

The ability to store information about the past to dynamically predict and prepare for the future is among the most fundamental tasks the brain performs. To date, the problems of understanding how the brain stores and organizes information about the past (memory) and how the brain represents and processes temporal information for adaptive behavior have generally been studied as distinct cognitive functions. This Symposium explores the inherent link between memory and temporal cognition, as well as the potential shared neural mechanisms between them. We suggest that working memory and implicit timing are interconnected and may share overlapping neural mechanisms. Additionally, we explore how temporal structure is encoded in associative and episodic memory and, conversely, the influences of episodic memory on subsequent temporal anticipation and the perception of time. We suggest that neural sequences provide a general computational motif that contributes to timing and working memory, as well as the spatiotemporal coding and recall of episodes.

Hippocampal sharp-wave ripples and their spike assembly content are regulated by the medial entorhinal cortex

Hippocampal sharp-wave ripples (SPW-Rs) are critical for memory consolidation and retrieval. The neuronal content of spiking during SPW-Rs is believed to be under the influence of neocortical inputs via the entorhinal cortex (EC). Optogenetic silencing of the medial EC (mEC) reduced the incidence of SPW-Rs with minor impacts on their magnitude or duration, similar to local CA1 silencing. The effect of mEC silencing on CA1 firing and field potentials was comparable to the effect of transient cortex-wide DOWN states of non-REM (NREM) sleep, implying that decreased SPW-R incidence in both cases is due to tonic disfacilitation of hippocampal circuits. The neuronal composition of CA1 pyramidal neurons during SPW-Rs was altered by mEC silencing but was restored immediately after silencing. We suggest that the mEC provides both tonic and transient influences on hippocampal network states by timing the occurrence of SPW-Rs and altering their neuronal content.

Voltage imaging reveals that hippocampal interneurons tune memory-encoding pyramidal sequences

Hippocampal spiking sequences encode and link behavioral information across time. How inhibition sculpts these sequences remains unknown. We performed longitudinal voltage imaging of CA1 parvalbumin- and somatostatin-expressing interneurons in mice during an odor-cued working memory task, before and after training. During this task, pyramidal odor-specific sequences encode the cue throughout a delay period. In contrast, most interneurons encoded odor delivery, but not odor identity, nor delay time. Population inhibition was stable across days, with constant field turnover, though some cells retained odor-responses for days. At odor onset, a brief, synchronous burst of parvalbumin cells was followed by widespread membrane hyperpolarization and then rebound theta-paced spiking, synchronized across cells. Two-photon calcium imaging revealed that most pyramidal cells were suppressed throughout the odor. Positive pyramidal odor-responses coincided with interneuronal rebound spiking; otherwise, they had weak odor-selectivity. Therefore, inhibition increases the signal-to-noise ratio of cue representations, which is crucial for entraining downstream targets.

A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells

Recent experimental observations have shown that the reactivation of hippocampal place cells (PC) during sleep or wakeful immobility depicts trajectories that can go around barriers and can flexibly adapt to a changing maze layout. However, existing computational models of replay fall short of generating such layout-conforming replay, restricting their usage to simple environments, like linear tracks or open fields. In this paper, we propose a computational model that generates layout-conforming replay and explains how such replay drives the learning of flexible navigation in a maze. First, we propose a Hebbian-like rule to learn the inter-PC synaptic strength during exploration. Then we use a continuous attractor network (CAN) with feedback inhibition to model the interaction among place cells and hippocampal interneurons. The activity bump of place cells drifts along paths in the maze, which models layout-conforming replay. During replay in sleep, the synaptic strengths from place cells to striatal medium spiny neurons (MSN) are learned by a novel dopamine-modulated three-factor rule to store place-reward associations. During goal-directed navigation, the CAN periodically generates replay trajectories from the animal's location for path planning, and the trajectory leading to a maximal MSN activity is followed by the animal. We have implemented our model into a high-fidelity virtual rat in the MuJoCo physics simulator. Extensive experiments have demonstrated that its superior flexibility during navigation in a maze is due to a continuous re-learning of inter-PC and PC-MSN synaptic strength.

Frequency matters: how changes in hippocampal theta frequency can influence temporal coding, anxiety-reduction, and memory

Hippocampal theta frequency is a somewhat neglected topic relative to theta power, phase, coherence, and cross-frequency coupling. Accordingly, here we review and present new data on variation in hippocampal theta frequency, focusing on functional associations (temporal coding, anxiety reduction, learning, and memory). Taking the rodent hippocampal theta frequency to running-speed relationship as a model, we identify two doubly-dissociable frequency components: (a) the slope component of the theta frequency-to-stimulus-rate relationship ("theta slope"); and (b) its y-intercept frequency ("theta intercept"). We identify three tonic determinants of hippocampal theta frequency. (1) Hotter temperatures increase theta frequency, potentially consistent with time intervals being judged as shorter when hot. Initial evidence suggests this occurs via the "theta slope" component. (2) Anxiolytic drugs with widely-different post-synaptic and pre-synaptic primary targets share the effect of reducing the "theta intercept" component, supporting notions of a final common pathway in anxiety reduction involving the hippocampus. (3) Novelty reliably decreases, and familiarity increases, theta frequency, acting upon the "theta slope" component. The reliability of this latter finding, and the special status of novelty for learning, prompts us to propose a Novelty Elicits Slowing of Theta frequency (NEST) hypothesis, involving the following elements: (1) Theta frequency slowing in the hippocampal formation is a generalised response to novelty of different types and modalities; (2) Novelty-elicited theta slowing is a hippocampal-formation-wide adaptive response functioning to accommodate the additional need for learning entailed by novelty; (3) Lengthening the theta cycle enhances associativity; (4) Even part-cycle lengthening may boost associativity; and (5) Artificial theta stimulation aimed at enhancing learning should employ low-end theta frequencies.

Revealing human sensitivity to a latent temporal structure of changes

Precisely timed behavior and accurate time perception plays a critical role in our everyday lives, as our wellbeing and even survival can depend on well-timed decisions. Although the temporal structure of the world around us is essential for human decision making, we know surprisingly little about how representation of temporal structure of our everyday environment impacts decision making. How does the representation of temporal structure affect our ability to generate well-timed decisions? Here we address this question by using a well-established dynamic probabilistic learning task. Using computational modeling, we found that human subjects' beliefs about temporal structure are reflected in their choices to either exploit their current knowledge or to explore novel options. The model-based analysis illustrates a large within-group and within-subject heterogeneity. To explain these results, we propose a normative model for how temporal structure is used in decision making, based on the semi-Markov formalism in the active inference framework. We discuss potential key applications of the presented approach to the fields of cognitive phenotyping and computational psychiatry.

The neural bases for timing of durations

Durations are defined by a beginning and an end, and a major distinction is drawn between durations that start in the present and end in the future ('prospective timing') and durations that start in the past and end either in the past or the present ('retrospective timing'). Different psychological processes are thought to be engaged in each of these cases. The former is thought to engage a clock-like mechanism that accurately tracks the continuing passage of time, whereas the latter is thought to engage a reconstructive process that utilizes both temporal and non-temporal information from the memory of past events. We propose that, from a biological perspective, these two forms of duration 'estimation' are supported by computational processes that are both reliant on population state dynamics but are nevertheless distinct. Prospective timing is effectively carried out in a single step where the ongoing dynamics of population activity directly serve as the computation of duration, whereas retrospective timing is carried out in two steps: the initial generation of population state dynamics through the process of event segmentation and the subsequent computation of duration utilizing the memory of those dynamics.

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.

High-frequency oscillations and sequence generation in two-population models of hippocampal region CA1

Hippocampal sharp wave/ripple oscillations are a prominent pattern of collective activity, which consists of a strong overall increase of activity with superimposed (140 − 200 Hz) ripple oscillations. Despite its prominence and its experimentally demonstrated importance for memory consolidation, the mechanisms underlying its generation are to date not understood. Several models assume that recurrent networks of inhibitory cells alone can explain the generation and main characteristics of the ripple oscillations. Recent experiments, however, indicate that in addition to inhibitory basket cells, the pattern requires in vivo the activity of the local population of excitatory pyramidal cells. Here, we study a model for networks in the hippocampal region CA1 incorporating such a local excitatory population of pyramidal neurons. We start by investigating its ability to generate ripple oscillations using extensive simulations. Using biologically plausible parameters, we find that short pulses of external excitation triggering excitatory cell spiking are required for sharp/wave ripple generation with oscillation patterns similar to in vivo observations. Our model has plausible values for single neuron, synapse and connectivity parameters, random connectivity and no strong feedforward drive to the inhibitory population. Specifically, whereas temporally broad excitation can lead to high-frequency oscillations in the ripple range, sparse pyramidal cell activity is only obtained with pulse-like external CA3 excitation. Further simulations indicate that such short pulses could originate from dendritic spikes in the apical or basal dendrites of CA1 pyramidal cells, which are triggered by coincident spike arrivals from hippocampal region CA3. Finally we show that replay of sequences by pyramidal neurons and ripple oscillations can arise intrinsically in CA1 due to structured connectivity that gives rise to alternating excitatory pulse and inhibitory gap coding; the latter denotes phases of silence in specific basket cell groups, which induce selective disinhibition of groups of pyramidal neurons. This general mechanism for sequence generation leads to sparse pyramidal cell and dense basket cell spiking, does not rely on synfire chain-like feedforward excitation and may be relevant for other brain regions as well.

Flexible rerouting of hippocampal replay sequences around changing barriers in the absence of global place field remapping

Flexibility is a hallmark of memories that depend on the hippocampus. For navigating animals, flexibility is necessitated by environmental changes such as blocked paths and extinguished food sources. To better understand the neural basis of this flexibility, we recorded hippocampal replays in a spatial memory task where barriers as well as goals were moved between sessions to see whether replays could adapt to new spatial and reward contingencies. Strikingly, replays consistently depicted new goal-directed trajectories around each new barrier configuration and largely avoided barrier violations. Barrier-respecting replays were learned rapidly and did not rely on place cell remapping. These data distinguish sharply between place field responses, which were largely stable and remained tied to sensory cues, and replays, which changed flexibly to reflect the learned contingencies in the environment and suggest sequenced activations such as replay to be an important link between the hippocampus and flexible memory.

Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3

Hippocampal place cells are activated sequentially as an animal explores its environment. These activity sequences are internally recreated (‘replayed’), either in the same or reversed order, during bursts of activity (sharp wave-ripples [SWRs]) that occur in sleep and awake rest. SWR-associated replay is thought to be critical for the creation and maintenance of long-term memory. In order to identify the cellular and network mechanisms of SWRs and replay, we constructed and simulated a data-driven model of area CA3 of the hippocampus. Our results show that the chain-like structure of recurrent excitatory interactions established during learning not only determines the content of replay, but is essential for the generation of the SWRs as well. We find that bidirectional replay requires the interplay of the experimentally confirmed, temporally symmetric plasticity rule, and cellular adaptation. Our model provides a unifying framework for diverse phenomena involving hippocampal plasticity, representations, and dynamics, and suggests that the structured neural codes induced by learning may have greater influence over cortical network states than previously appreciated.

Neurophysiology of Remembering

By linking the past with the future, our memories define our sense of identity. Because human memory engages the conscious realm, its examination has historically been approached from language and introspection and proceeded largely along separate parallel paths in humans and other animals. Here, we first highlight the achievements and limitations of this mind-based approach and make the case for a new brain-based understanding of declarative memory with a focus on hippocampal physiology. Next, we discuss the interleaved nature and common physiological mechanisms of navigation in real and mental spacetime. We suggest that a distinguishing feature of memory types is whether they subserve actions for single or multiple uses. Finally, in contrast to the persisting view of the mind as a highly plastic blank slate ready for the world to make its imprint, we hypothesize that neuronal networks are endowed with a reservoir of neural trajectories, and the challenge faced by the brain is how to select and match preexisting neuronal trajectories with events in the world.

Sequential Attractors in Combinatorial Threshold-Linear Networks

Sequences of neural activity arise in many brain areas, including cortex, hippocampus, and central pattern generator circuits that underlie rhythmic behaviors like locomotion. While network architectures supporting sequence generation vary considerably, a common feature is an abundance of inhibition. In this work, we focus on architectures that support sequential activity in recurrently connected networks with inhibition-dominated dynamics. Specifically, we study emergent sequences in a special family of threshold-linear networks, called combinatorial threshold-linear networks (CTLNs), whose connectivity matrices are defined from directed graphs. Such networks naturally give rise to an abundance of sequences whose dynamics are tightly connected to the underlying graph. We find that architectures based on generalizations of cycle graphs produce limit cycle attractors that can be activated to generate transient or persistent (repeating) sequences. Each architecture type gives rise to an infinite family of graphs that can be built from arbitrary component subgraphs. Moreover, we prove a number of graph rules for the corresponding CTLNs in each family. The graph rules allow us to strongly constrain, and in some cases fully determine, the fixed points of the network in terms of the fixed points of the component subnetworks. Finally, we also show how the structure of certain architectures gives insight into the sequential dynamics of the corresponding attractor.

Common and Distinct Roles of Frontal Midline Theta and Occipital Alpha Oscillations in Coding Temporal Intervals and Spatial Distances

Judging how far something is and how long it takes to get there is critical to memory and navigation. Yet, the neural codes for spatial and temporal information remain unclear, particularly the involvement of neural oscillations in maintaining such codes. To address these issues, we designed an immersive virtual reality environment containing teleporters that displace participants to a different location after entry. Upon exiting the teleporters, participants made judgments from two given options regarding either the distance they had traveled (spatial distance condition) or the duration they had spent inside the teleporters (temporal duration condition). We wirelessly recorded scalp EEG while participants navigated in the virtual environment by physically walking on an omnidirectional treadmill and traveling through teleporters. An exploratory analysis revealed significantly higher alpha and beta power for short-distance versus long-distance traversals, whereas the contrast also revealed significantly higher frontal midline delta-theta-alpha power and global beta power increases for short versus long temporal duration teleportation. Analyses of occipital alpha instantaneous frequencies revealed their sensitivity for both spatial distances and temporal durations, suggesting a novel and common mechanism for both spatial and temporal coding. We further examined the resolution of distance and temporal coding by classifying discretized distance bins and 250-msec time bins based on multivariate patterns of 2- to 30-Hz power spectra, finding evidence that oscillations code fine-scale time and distance information. Together, these findings support partially independent coding schemes for spatial and temporal information, suggesting that low-frequency oscillations play important roles in coding both space and time.

How development sculpts hippocampal circuits and function

In mammals, the selective transformation of transient experience into stored memory occurs in the hippocampus, which develops representations of specific events in the context in which they occur. In this review, we focus on the development of hippocampal circuits and the self-organized dynamics embedded within them since the latter critically support the role of the hippocampus in learning and memory. We first discuss evidence that adult hippocampal cells and circuits are sculpted by development as early as during embryonic neurogenesis. We argue that these primary developmental programs provide a scaffold onto which later experience of the external world can be grafted. Next, we review the different sequences in the development of hippocampal cells and circuits at anatomical and functional levels. We cover a period extending from neurogenesis and migration to the appearance of phenotypic diversity within hippocampal cells, and their wiring into functional networks. We describe the progressive emergence of network dynamics in the hippocampus, from sensorimotor-driven early sharp waves to sequences of place cells tracking relational information. We outline the critical turn points and discontinuities in that developmental journey, and close by formulating open questions. We propose that rewinding the process of hippocampal development helps understand the main organization principles of memory circuits.

Crucial role for CA2 inputs in the sequential organization of CA1 time cells supporting memory

There is considerable evidence for hippocampal time cells that briefly activate in succession to represent the temporal structure of memories. Previous studies have shown that time cells can be disrupted while leaving place cells intact, indicating that spatial and temporal information can be coded in parallel. However, the circuits in which spatial and temporal information are coded have not been clearly identified. Here we investigated temporal and spatial coding by dorsal hippocampal CA1 (dCA1) neurons in mice trained on a classic spatial working-memory task. On each trial, the mice approached the same choice point on a maze but were trained to alternate between traversing one of two distinct spatial routes (spatial coding phase). In between trials, there was a 10-s mnemonic delay during which the mouse continuously ran in a fixed location (temporal coding phase). Using cell-type-specific optogenetic methods, we found that inhibiting dorsal CA2 (dCA2) inputs into dCA1 degraded time cell coding during the mnemonic delay and impaired the mouse's subsequent memory-guided choice. Conversely, inhibiting dCA2 inputs during the spatial coding phase had a negligible effect on place cell activity in dCA1 and no effect on behavior. Collectively, our work demonstrates that spatial and temporal coding in dCA1 is largely segregated with respect to the dCA2-dCA1 circuit and suggests that CA2 plays a critical role in representing the flow of time in memory within the hippocampal network.

The Sync-Fire/deSync model: Modelling the reactivation of dynamic memories from cortical alpha oscillations

We propose a neural network model to explore how humans can learn and accurately retrieve temporal sequences, such as melodies, movies, or other dynamic content. We identify target memories by their neural oscillatory signatures, as shown in recent human episodic memory paradigms. Our model comprises three plausible components for the binding of temporal content, where each component imposes unique limitations on the encoding and representation of that content. A cortical component actively represents sequences through the disruption of an intrinsically generated alpha rhythm, where a desynchronisation marks information-rich operations as the literature predicts. A binding component converts each event into a discrete index, enabling repetitions through a sparse encoding of events. A timing component - consisting of an oscillatory "ticking clock" made up of hierarchical synfire chains - discretely indexes a moment in time. By encoding the absolute timing between discretised events, we show how one can use cortical desynchronisations to dynamically detect unique temporal signatures as they are reactivated in the brain. We validate this model by simulating a series of events where sequences are uniquely identifiable by analysing phasic information, as several recent EEG/MEG studies have shown. As such, we show how one can encode and retrieve complete episodic memories where the quality of such memories is modulated by the following: alpha gate keepers to content representation; binding limitations that induce a blink in temporal perception; and nested oscillations that provide preferential learning phases in order to temporally sequence events.

Parcels and particles: Markov blankets in the brain

At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions-and analyses-of distributed brain responses: namely, functional segregation and integration. There are currently two main approaches to characterizing functional integration. The first is a mechanistic modeling of connectomics in terms of directed effective connectivity that mediates neuronal message passing and dynamics on neuronal circuits. The second phenomenological approach usually characterizes undirected functional connectivity (i.e., measurable correlations), in terms of intrinsic brain networks, self-organized criticality, dynamical instability, and so on. This paper describes a treatment of effective connectivity that speaks to the emergence of intrinsic brain networks and critical dynamics. It is predicated on the notion of Markov blankets that play a fundamental role in the self-organization of far from equilibrium systems. Using the apparatus of the renormalization group, we show that much of the phenomenology found in network neuroscience is an emergent property of a particular partition of neuronal states, over progressively coarser scales. As such, it offers a way of linking dynamics on directed graphs to the phenomenology of intrinsic brain networks.

Differential Emergence and Stability of Sensory and Temporal Representations in Context-Specific Hippocampal Sequences

Hippocampal spiking sequences encode external stimuli and spatiotemporal intervals, linking sequential experiences in memory, but the dynamics controlling the emergence and stability of such diverse representations remain unclear. Using two-photon calcium imaging in CA1 while mice performed an olfactory working-memory task, we recorded stimulus-specific sequences of "odor-cells" encoding olfactory stimuli followed by "time-cells" encoding time points in the ensuing delay. Odor-cells were reliably activated and retained stable fields during changes in trial structure and across days. Time-cells exhibited sparse and dynamic fields that remapped in both cases. During task training, but not in untrained task exposure, time-cell ensembles increased in size, whereas odor-cell numbers remained stable. Over days, sequences drifted to new populations with cell activity progressively converging to a field and then diverging from it. Therefore, CA1 employs distinct regimes to encode external cues versus their variable temporal relationships, which may be necessary to construct maps of sequential experiences.

Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi

Neuromorphic hardware has several promising advantages compared to von Neumann architectures and is highly interesting for robot control. However, despite the high speed and energy efficiency of neuromorphic computing, algorithms utilizing this hardware in control scenarios are still rare. One problem is the transition from fast spiking activity on the hardware, which acts on a timescale of a few milliseconds, to a control-relevant timescale on the order of hundreds of milliseconds. Another problem is the execution of complex trajectories, which requires spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics must be adequately robust against noise. In this study we exploit a recently developed biologically-inspired spiking neural network model, the so-called anisotropic network. We identified and transferred the core principles of the anisotropic network to neuromorphic hardware using Intel's neuromorphic research chip Loihi and validated the system on trajectories from a motor-control task performed by a robot arm. We developed a network architecture including the anisotropic network and a pooling layer which allows fast spike read-out from the chip and performs an inherent regularization. With this, we show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity, each representing a robotic action, and that the patterns allow the generation of multidimensional trajectories on control-relevant timescales. Taken together, our study presents a new algorithm that allows the generation of complex robotic movements as a building block for robotic control using state of the art neuromorphic hardware.

Time as the fourth dimension in the hippocampus

Experiences of animal and human beings are structured by the continuity of space and time coupled with the uni-directionality of time. In addition to its pivotal position in spatial processing and navigation, the hippocampal system also plays a central, multiform role in several types of temporal processing. These include timing and sequence learning, at scales ranging from meso-scales of seconds to macro-scales of minutes, hours, days and beyond, encompassing the classical functions of short term memory, working memory, long term memory, and episodic memories (comprised of information about when, what, and where). This review article highlights the principal findings and behavioral contexts of experiments in rats showing: 1) timing: tracking time during delays by hippocampal 'time cells' and during free behavior by hippocampal-afferent lateral entorhinal cortex ramping cells; 2) 'online' sequence processing: activity coding sequences of events during active behavior; 3) 'off-line' sequence replay: during quiescence or sleep, orderly reactivation of neuronal assemblies coding awake sequences. Studies in humans show neurophysiological correlates of episodic memory comparable to awake replay. Neural mechanisms are discussed, including ion channel properties, plateau and ramping potentials, oscillations of excitation and inhibition of population activity, bursts of high amplitude discharges (sharp wave ripples), as well as short and long term synaptic modifications among and within cell assemblies. Specifically conceived neural network models will suggest processes supporting the emergence of scalar properties (Weber's law), and include different classes of feedforward and recurrent network models, with intrinsic hippocampal coding for 'transitions' (sequencing of events or places).

Navigating Through Time: A Spatial Navigation Perspective on How the Brain May Encode Time

Interval timing, which operates on timescales of seconds to minutes, is distributed across multiple brain regions and may use distinct circuit mechanisms as compared to millisecond timing and circadian rhythms. However, its study has proven difficult, as timing on this scale is deeply entangled with other behaviors. Several circuit and cellular mechanisms could generate sequential or ramping activity patterns that carry timing information. Here we propose that a productive approach is to draw parallels between interval timing and spatial navigation, where direct analogies can be made between the variables of interest and the mathematical operations necessitated. Along with designing experiments that isolate or disambiguate timing behavior from other variables, new techniques will facilitate studies that directly address the neural mechanisms that are responsible for interval timing.

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neurons may be used to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory spiking neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity.

From space to time: Spatial inhomogeneities lead to the emergence of spatiotemporal sequences in spiking neuronal networks

Spatio-temporal sequences of neuronal activity are observed in many brain regions in a variety of tasks and are thought to form the basis of meaningful behavior. However, mechanisms by which a neuronal network can generate spatio-temporal activity sequences have remained obscure. Existing models are biologically untenable because they either require manual embedding of a feedforward network within a random network or supervised learning to train the connectivity of a network to generate sequences. Here, we propose a biologically plausible, generative rule to create spatio-temporal activity sequences in a network of spiking neurons with distance-dependent connectivity. We show that the emergence of spatio-temporal activity sequences requires: (1) individual neurons preferentially project a small fraction of their axons in a specific direction, and (2) the preferential projection direction of neighboring neurons is similar. Thus, an anisotropic but correlated connectivity of neuron groups suffices to generate spatio-temporal activity sequences in an otherwise random neuronal network model.

Velocity coupling of grid cell modules enables stable embedding of a low dimensional variable in a high dimensional neural attractor

Grid cells in the medial entorhinal cortex (MEC) encode position using a distributed representation across multiple neural populations (modules), each possessing a distinct spatial scale. The modular structure of the representation confers the grid cell neural code with large capacity. Yet, the modularity poses significant challenges for the neural circuitry that maintains the representation, and updates it based on self motion. Small incompatible drifts in different modules, driven by noise, can rapidly lead to large, abrupt shifts in the represented position, resulting in catastrophic readout errors. Here, we propose a theoretical model of coupled modules. The coupling suppresses incompatible drifts, allowing for a stable embedding of a two-dimensional variable (position) in a higher dimensional neural attractor, while preserving the large capacity. We propose that coupling of this type may be implemented by recurrent synaptic connectivity within the MEC with a relatively simple and biologically plausible structure.

Cellular and Network Mechanisms May Generate Sparse Coding of Sequential Object Encounters in Hippocampal-Like Circuits

The localization of distinct landmarks plays a crucial role in encoding new spatial memories. In mammals, this function is performed by hippocampal neurons that sparsely encode an animal’s location relative to surrounding objects. Similarly, the dorsolateral pallium (DL) is essential for spatial learning in teleost fish. The DL of weakly electric gymnotiform fish receives both electrosensory and visual input from the preglomerular nucleus (PG), which has been hypothesized to encode the temporal sequence of electrosensory or visual landmark/food encounters. Here, we show that DL neurons in the Apteronotid fish and in the Carassius auratus (goldfish) have a hyperpolarized resting membrane potential (RMP) combined with a high and dynamic spike threshold that increases following each spike. Current-evoked spikes in DL cells are followed by a strong small-conductance calcium-activated potassium channel (SK)-mediated after-hyperpolarizing potential (AHP). Together, these properties prevent high frequency and continuous spiking. The resulting sparseness of discharge and dynamic threshold suggest that DL neurons meet theoretical requirements for generating spatial memory engrams by decoding the landmark/food encounter sequences encoded by PG neurons. Thus, DL neurons in teleost fish may provide a promising, simple system to study the core cell and network mechanisms underlying spatial memory.

Episodic memory: Neuronal codes for what, where, and when

Episodic memory is defined as the ability to recall events in a spatiotemporal context. Formation of such memories is critically dependent on the hippocampal formation and its inputs from the entorhinal cortex. To be able to support the formation of episodic memories, entorhinal cortex and hippocampal formation should contain a neuronal code that follows several requirements. First, the code should include information about position of the agent ("where"), sequence of events ("when"), and the content of the experience itself ("what"). Second, the code should arise instantly thereby being able to support memory formation of one-shot experiences. For successful encoding and to avoid interference between memories during recall, variations in location, time, or in content of experience should result in unique ensemble activity. Finally, the code should capture several different resolutions of experience so that the necessary details relevant for future memory-based predictions will be stored. We review how neuronal codes in entorhinal cortex and hippocampus follow these requirements and argue that during formation of episodic memories entorhinal cortex provides hippocampus with instant information about ongoing experience. Such information originates from (a) spatially modulated neurons in medial entorhinal cortex, including grid cells, which provide a stable and universal positional metric of the environment; (b) a continuously varying signal in lateral entorhinal cortex providing a code for the temporal progression of events; and (c) entorhinal neurons coding the content of experiences exemplified by object-coding and odor-selective neurons. During formation of episodic memories, information from these systems are thought to be encoded as unique sequential ensemble activity in hippocampus, thereby encoding associations between the content of an event and its spatial and temporal contexts. Upon exposure to parts of the encoded stimuli, activity in these ensembles can be reinstated, leading to reactivation of the encoded activity pattern and memory recollection.

How Memory Conforms to Brain Development

Nature exhibits countless examples of adaptive networks, whose topology evolves constantly coupled with the activity due to its function. The brain is an illustrative example of a system in which a dynamic complex network develops by the generation and pruning of synaptic contacts between neurons while memories are acquired and consolidated. Here, we consider a recently proposed brain developing model to study how mechanisms responsible for the evolution of brain structure affect and are affected by memory storage processes. Following recent experimental observations, we assume that the basic rules for adding and removing synapses depend on local synaptic currents at the respective neurons in addition to global mechanisms depending on the mean connectivity. In this way a feedback loop between "form" and "function" spontaneously emerges that influences the ability of the system to optimally store and retrieve sensory information in patterns of brain activity or memories. In particular, we report here that, as a consequence of such a feedback-loop, oscillations in the activity of the system among the memorized patterns can occur, depending on parameters, reminding mind dynamical processes. Such oscillations have their origin in the destabilization of memory attractors due to the pruning dynamics, which induces a kind of structural disorder or noise in the system at a long-term scale. This constantly modifies the synaptic disorder induced by the interference among the many patterns of activity memorized in the system. Such new intriguing oscillatory behavior is to be associated only to long-term synaptic mechanisms during the network evolution dynamics, and it does not depend on short-term synaptic processes, as assumed in other studies, that are not present in our model.

Temporal signals underlying a cognitive process in the dorsal premotor cortex

During discrimination between two sequential vibrotactile stimulus patterns, the primate dorsal premotor cortex (DPC) neurons exhibit a complex repertoire of coding dynamics associated with the working memory, comparison, and decision components of this task. In addition, these neurons and neurons with no coding responses show complex strong fluctuations in their firing rate associated with the temporal sequence of task events. Here, to make sense of this temporal complexity, we extracted the temporal signals that were latent in the population. We found a strong link between the individual and population response, suggesting a common neural substrate. Notably, in contrast to coding dynamics, these time-dependent responses were unaffected during error trials. However, in a nondemanding task in which monkeys did not require discrimination for reward, these time-dependent signals were largely reduced and changed. These results suggest that temporal dynamics in DPC reflect the underlying cognitive processes of this task.

Odor Concentration Change Coding in the Olfactory Bulb

Dynamical changes in the environment strongly impact our perception. Likewise, sensory systems preferentially represent stimulus changes, enhancing temporal contrast. In olfaction, odor concentration changes across consecutive inhalations (ΔC_t) can guide odor source localization, yet the neural representation of ΔC_t has not been studied in vertebrates. We have found that, in the mouse olfactory bulb, a subset of mitral/tufted (M/T) cells represents ΔC_t, enhancing the contrast between different concentrations. These concentration change responses are direction selective: they respond either to increments or decrements of concentration, reminiscent of ON and OFF selectivity in the retina. This contrast enhancement scales with the magnitude, but not the duration of the concentration step. Further, ΔC_t can be read out from the total spike count per sniff, unlike odor identity and intensity, which are represented by fast temporal spike patterns. Our results demonstrate that a subset of M/T cells represents ΔC_t, providing a signal that may instruct navigational decisions in downstream olfactory circuits.

Temporal encoding strategies result in boosts to final free recall performance comparable to spatial ones

The method of loci is a highly effective mnemonic that recruits existing salient memory for spatial locations and uses the information as a scaffold for remembering a list of items (Yates, 1966). One possible account for the effectiveness of the spatial method of loci comes from the perspective that it utilizes evolutionarily preserved mechanisms for spatial navigation within the hippocampus (Maguire et al. in Proceedings of the National Academy of Sciences, 97(8), 4398-4403, 2000; O'Keefe & Nadel, 1978; Rodriguez et al. in Brain Research Bulletin, 57(3), 499-503, 2002). Recently, though, neurons representing temporal information have also been described within the hippocampus (Eichenbaum in Nature Reviews Neuroscience, 15(11), 732-744, 2014; Itskov, Curto, Pastalkova, & Buzsáki in The Journal of Neuroscience, 31(8), 2828-2834, 2011; MacDonald, Lepage, Eden, & Eichenbaum in Neuron, 71(4), 737-749, 2011; Mankin et al. in Proceedings of the National Academy of Sciences, 109(47), 19462-19467, 2012; Meck, Church, & Matell in Behavioral Neuroscience, 127(5), 642, 2013), challenging the primacy of spatial-based functions to hippocampal processing. Given the presence of both spatial and temporal coding mechanisms within the hippocampus, we predicted that primarily temporal encoding strategies might also enhance memory. In two different experiments, we asked participants to learn lists of unrelated nouns using the (spatial) method of loci (i.e., the layout of their home as the organizing feature) or using two novel temporal methods (i.e., autobiographical memories or using the steps to making a sandwich). Participants' final free recall performance showed comparable boosts to the method of loci for both temporal encoding strategies, with all three scaffolding approaches demonstrating performance well above uninstructed free recall. Our findings suggest that primarily temporal representations can be used effectively to boost memory performance, comparable to spatial methods, with some caveats related to the relative ease with which participants appear to master the spatial versus temporal methods.

A model of temporal scaling correctly predicts that motor timing improves with speed

Timing is fundamental to complex motor behaviors: from tying a knot to playing the piano. A general feature of motor timing is temporal scaling: the ability to produce motor patterns at different speeds. One theory of temporal processing proposes that the brain encodes time in dynamic patterns of neural activity (population clocks), here we first examine whether recurrent neural network (RNN) models can account for temporal scaling. Appropriately trained RNNs exhibit temporal scaling over a range similar to that of humans and capture a signature of motor timing, Weber's law, but predict that temporal precision improves at faster speeds. Human psychophysics experiments confirm this prediction: the variability of responses in absolute time are lower at faster speeds. These results establish that RNNs can account for temporal scaling and suggest a novel psychophysical principle: the Weber-Speed effect.

Space and Time: The Hippocampus as a Sequence Generator

Neural computations are often compared to instrument-measured distance or duration, and such relationships are interpreted by a human observer. However, neural circuits do not depend on human-made instruments but perform computations relative to an internally defined rate-of-change. While neuronal correlations with external measures, such as distance or duration, can be observed in spike rates or other measures of neuronal activity, what matters for the brain is how such activity patterns are utilized by downstream neural observers. We suggest that hippocampal operations can be described by the sequential activity of neuronal assemblies and their internally defined rate of change without resorting to the concept of space or time.

The memory for time and space differentially engages the proximal and distal parts of the hippocampal subfields CA1 and CA3

A well-accepted model of episodic memory involves the processing of spatial and non-spatial information by segregated pathways and their association within the hippocampus. However, these pathways project to distinct proximodistal levels of the hippocampus. Moreover, spatial and non-spatial subnetworks segregated along this axis have been recently described using memory tasks with either a spatial or a non-spatial salient dimension. Here, we tested whether the concept of segregated subnetworks and the traditional model are reconcilable by studying whether activity within CA1 and CA3 remains segregated when both dimensions are salient, as is the case for episodes. Simultaneously, we investigated whether temporal or spatial information bound to objects recruits similar subnetworks as items or locations per se, respectively. To do so, we studied the correlations between brain activity and spatial and/or temporal discrimination ratios in proximal and distal CA1 and CA3 by detecting Arc RNA in mice. We report a robust proximodistal segregation in CA1 for temporal information processing and in both CA1 and CA3 for spatial information processing. Our results suggest that the traditional model of episodic memory and the concept of segregated networks are reconcilable, to a large extent and put forward distal CA1 as a possible “home” location for time cells.

Departing from the most influential model of episodic memory (the two-streams hypothesis), we have recently proposed a new concept of information processing in the hippocampus according to which “what” one remembers and “where” it happens might be processed by distinct subnetworks segregated along the proximodistal axis of the hippocampus, a brain region tied to memory function, instead of being systematically integrated at this level. Here, we focused on the processing of temporal and/or spatial information in the proximal and distal parts of CA1 and CA3 in mice to test whether the two concepts are reconcilable. To do so, we used an imaging method with cellular resolution based on the detection of the RNA of the Immediate Early Gene (IEG) Arc, which is tied to synaptic plasticity and memory demands, and correlated imaging results with memory performance. Our data confirm the existence of subnetworks segregated along the proximodistal axis of CA1 and CA3 that preferentially process spatial and non-spatial information and suggest a key involvement of distal CA1 in temporal information processing. In addition, they show that the two models are complementary to a large extent and posit the “segregated” model as a viable alternative for the two-streams hypothesis.

Sequential Firing Codes for Time in Rodent Medial Prefrontal Cortex

A subset of hippocampal neurons, known as "time cells" fire sequentially for circumscribed periods of time within a delay interval. We investigated whether medial prefrontal cortex (mPFC) also contains time cells and whether their qualitative properties differ from those in the hippocampus and striatum. We studied the firing correlates of neurons in the rodent mPFC during a temporal discrimination task. On each trial, the animals waited for a few seconds in the stem of a T-maze. A subpopulation of units fired in a sequence consistently across trials for a circumscribed period during the delay interval. These sequentially activated time cells showed temporal accuracy that decreased as time passed as measured by both the width of their firing fields and the number of cells that fired at a particular part of the interval. The firing dynamics of the time cells was significantly better explained with the elapse of time than with the animals' position and velocity. The findings observed here in the mPFC are consistent with those previously reported in the hippocampus and striatum, suggesting that the sequentially activated time cells are not specific to these areas, but are part of a common representational motif across regions.

Compressed Timeline of Recent Experience in Monkey Lateral Prefrontal Cortex

Cognitive theories suggest that working memory maintains not only the identity of recently presented stimuli but also a sense of the elapsed time since the stimuli were presented. Previous studies of the neural underpinnings of working memory have focused on sustained firing, which can account for maintenance of the stimulus identity, but not for representation of the elapsed time. We analyzed single-unit recordings from the lateral prefrontal cortex of macaque monkeys during performance of a delayed match-to-category task. Each sample stimulus triggered a consistent sequence of neurons, with each neuron in the sequence firing during a circumscribed period. These sequences of neurons encoded both stimulus identity and elapsed time. The encoding of elapsed time became less precise as the sample stimulus receded into the past. These findings suggest that working memory includes a compressed timeline of what happened when, consistent with long-standing cognitive theories of human memory.

Low-Dimensional and Monotonic Preparatory Activity in Mouse Anterior Lateral Motor Cortex

Neurons in multiple brain regions fire trains of action potentials anticipating specific movements, but this "preparatory activity" has not been systematically compared across behavioral tasks. We compared preparatory activity in auditory and tactile delayed-response tasks in male mice. Skilled, directional licking was the motor output. The anterior lateral motor cortex (ALM) is necessary for motor planning in both tasks. Multiple features of ALM preparatory activity during the delay epoch were similar across tasks. First, most neurons showed direction-selective activity and spatially intermingled neurons were selective for either movement direction. Second, many cells showed mixed coding of sensory stimulus and licking direction, with a bias toward licking direction. Third, delay activity was monotonic and low-dimensional. Fourth, pairs of neurons with similar direction selectivity showed high spike-count correlations. Our study forms the foundation to analyze the neural circuit mechanisms underlying preparatory activity in a genetically tractable model organism.SIGNIFICANCE STATEMENT Short-term memories link events separated in time. Neurons in the frontal cortex fire trains of action potentials anticipating specific movements, often seconds before the movement. This "preparatory activity" has been observed in multiple brain regions, but has rarely been compared systematically across behavioral tasks in the same brain region. To identify common features of preparatory activity, we developed and compared preparatory activity in auditory and tactile delayed-response tasks in mice. The same cortical area is necessary for both tasks. Multiple features of preparatory activity, measured with high-density silicon probes, were similar across tasks. We find that preparatory activity is low-dimensional and monotonic. Our study forms a foundation for analyzing the circuit mechanisms underlying preparatory activity in a genetically tractable model organism.

Encoding Time in Feedforward Trajectories of a Recurrent Neural Network Model

Brain activity evolves through time, creating trajectories of activity that underlie sensorimotor processing, behavior, and learning and memory. Therefore, understanding the temporal nature of neural dynamics is essential to understanding brain function and behavior. In vivo studies have demonstrated that sequential transient activation of neurons can encode time. However, it remains unclear whether these patterns emerge from feedforward network architectures or from recurrent networks and, furthermore, what role network structure plays in timing. We address these issues using a recurrent neural network (RNN) model with distinct populations of excitatory and inhibitory units. Consistent with experimental data, a single RNN could autonomously produce multiple functionally feedforward trajectories, thus potentially encoding multiple timed motor patterns lasting up to several seconds. Importantly, the model accounted for Weber's law, a hallmark of timing behavior. Analysis of network connectivity revealed that efficiency-a measure of network interconnectedness-decreased as the number of stored trajectories increased. Additionally, the balance of excitation (E) and inhibition (I) shifted toward excitation during each unit's activation time, generating the prediction that observed sequential activity relies on dynamic control of the E/I balance. Our results establish for the first time that the same RNN can generate multiple functionally feedforward patterns of activity as a result of dynamic shifts in the E/I balance imposed by the connectome of the RNN. We conclude that recurrent network architectures account for sequential neural activity, as well as for a fundamental signature of timing behavior: Weber's law.

Dendrites, deep learning, and sequences in the hippocampus

The hippocampus places us both in time and space. It does so over remarkably large spans: milliseconds to years, and centimeters to kilometers. This works for sensory representations, for memory, and for behavioral context. How does it fit in such wide ranges of time and space scales, and keep order among the many dimensions of stimulus context? A key organizing principle for a wide sweep of scales and stimulus dimensions is that of order in time, or sequences. Sequences of neuronal activity are ubiquitous in sensory processing, in motor control, in planning actions, and in memory. Against this strong evidence for the phenomenon, there are currently more models than definite experiments about how the brain generates ordered activity. The flip side of sequence generation is discrimination. Discrimination of sequences has been extensively studied at the behavioral, systems, and modeling level, but again physiological mechanisms are fewer. It is against this backdrop that I discuss two recent developments in neural sequence computation, that at face value share little beyond the label "neural." These are dendritic sequence discrimination, and deep learning. One derives from channel physiology and molecular signaling, the other from applied neural network theory - apparently extreme ends of the spectrum of neural circuit detail. I suggest that each of these topics has deep lessons about the possible mechanisms, scales, and capabilities of hippocampal sequence computation.

Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model

Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, we propose a model of spatial learning and sequence generation as interdependent processes, integrating cortical contextual coding, synaptic plasticity and neuromodulatory mechanisms into a map-based approach. Following goal learning, sequential activity emerges from continuous attractor network dynamics biased by goal memory inputs. We apply Bayesian decoding on the resulting spike trains, allowing a direct comparison with experimental data. Simulations show that this model (1) explains the generation of never-experienced sequence trajectories in familiar environments, without requiring virtual self-motion signals, (2) accounts for the bias in place-cell sequences toward goal locations, (3) highlights their utility in flexible route planning, and (4) provides specific testable predictions.

The Persistence of Memory: How the Brain Encodes Time in Memory

Time and memory are inextricably linked, but it is far from clear how event durations and temporal sequences are encoded in memory. In this review, we focus on resource allocation models of working memory which suggest that memory resources can be flexibly distributed amongst several items such that the precision of working memory decreases with the number of items to be encoded. This type of model is consistent with human performance in working memory tasks based on visual, auditory as well as temporal stimulus patterns. At the neural-network level, we focus on excitatory-inhibitory oscillatary processes that are able to encode both interval timing and working memory in a coupled excitatory-inhibitory network. This modification of the striatal beat-frequency model of interval timing shows how memories for multiple time intervals are represented by neural oscillations and can also be used to explain the mechanisms of resource allocation in working memory.