Time Scale Behavior Research Articles

A revised view of the role of CaMKII in learning and memory.

The Ca2+/calmodulin (CaM)-dependent protein kinase II (CaMKII) plays a fundamental role in learning and possibly also in memory. However, current mechanistic models require fundamental revision. CaMKII autophosphorylation at Thr286 (pThr286) does not provide the molecular basis for long-term memory, as long believed. Instead, pThr286 mediates the signal processing required for induction of several distinct forms of synaptic plasticity, including Hebbian long-term potentiation and depression and non-Hebbian behavioral timescale synaptic plasticity. We discuss (i) the molecular computations by which CaMKII supports these diverse plasticity mechanisms, (ii) alternative CaMKII mechanisms that may contribute to the maintenance phase of LTP and (iii) the relationship of these mechanisms to behavioral learning and memory.

Mechanisms of memory-supporting neuronal dynamics in hippocampal area CA3

Hippocampal CA3 is central to memory formation and retrieval. Although various network mechanisms have been proposed, direct evidence is lacking. Using intracellular Vm recordings and optogenetic manipulations in behaving mice, we found that CA3 place-field activity is produced by a symmetric form of behavioral timescale synaptic plasticity (BTSP) at recurrent synapses among CA3 pyramidal neurons but not at synapses from the dentate gyrus (DG). Additional manipulations revealed that excitatory input from the entorhinal cortex (EC) but not the DG was required to update place cell activity based on the animal's movement. These data were captured by a computational model that used BTSP and an external updating input to produce attractor dynamics under online learning conditions. Theoretical analyses further highlight the superior memory storage capacity of such networks, especially when dealing with correlated input patterns. This evidence elucidates the cellular and circuit mechanisms of learning and memory formation in the hippocampus.

One-shot entorhinal maps enable flexible navigation in novel environments

Animals must navigate changing environments to find food, shelter or mates. In mammals, grid cells in the medial entorhinal cortex construct a neural spatial map of the external environment1–5. However, how grid cell firing patterns rapidly adapt to novel or changing environmental features on a timescale relevant to behaviour remains unknown. Here, by recording over 15,000 grid cells in mice navigating virtual environments, we tracked the real-time state of the grid cell network. This allowed us to observe and predict how altering environmental features influenced grid cell firing patterns on a nearly instantaneous timescale. We found evidence that visual landmarks provide inputs to fixed points in the grid cell network. This resulted in stable grid cell firing patterns in novel and altered environments after a single exposure. Fixed visual landmark inputs also influenced the grid cell network such that altering landmarks induced distortions in grid cell firing patterns. Such distortions could be predicted by a computational model with a fixed landmark to grid cell network architecture. Finally, a medial entorhinal cortex-dependent task revealed that although grid cell firing patterns are distorted by landmark changes, behaviour can adapt via a downstream region implementing behavioural timescale synaptic plasticity6. Overall, our findings reveal how the navigational system of the brain constructs spatial maps that balance rapidity and accuracy. Fixed connections between landmarks and grid cells enable the brain to quickly generate stable spatial maps, essential for navigation in novel or changing environments. Conversely, plasticity in regions downstream from grid cells allows the spatial maps of the brain to more accurately mirror the external spatial environment. More generally, these findings raise the possibility of a broader neural principle: by allocating fixed and plastic connectivity across different networks, the brain can solve problems requiring both rapidity and representational accuracy.

Dynamic changes of media prefrontal cortex astrocytic activity in response to negative stimuli in male mice

Astrocytes play significant roles in regulating the central stress response. Chronic stress impairs the structure and function of astrocytes in many brain regions such as media prefrontal cortex (mPFC) in multiple neuropsychiatric conditions, but the astrocytic dynamics on the timescale of behavior remains unclear. Here, we recorded mPFC astrocytic activity in freely behaving mice and found that astrocytes are activated immediately by different aversive stimuli. Astrocyte specific GCaMP6s calcium indicator were virally expressed in mPFC astrocytes and fiber photometry experiments revealed that astrocytes are activated by tail-restraint (TRT), foot shock (FS), open arm exploration, stressor of height, predator odor and social defeat (SD) stress. ΔF/F analyses demonstrated that an unpredictable stimulus such as elevated platform stress (EPS) at the initial encounter induced the most intense and rapid changes in astrocytic calcium activity, while a predictable 2,5-dihydro-2,4,5-trimethylthiazoline (TMT) stimulus resulted in the weakest response with a longer peak latency. In TRT, FS or SD test, a somatic stimulus led to higher average calcium activity level and faster average peak latency in repeated trails. Similar to TMT stimulus, astrocytic calcium activity in elevated plus maze (EPM) test exhibited a smaller average change in amplitude and the longest peak latency during open arm exploration. Moreover, astrocytic calcium activity exhibited different changes across behavioral states in SD tests. Our findings show that mPFC astrocytes exhibit distinct patterns of calcium activity in response to various negative stimuli, indicating that the dynamic activity of astrocytes may reflect the stress-related behavioral state under different stimulus conditions.

Non-spatial hippocampal behavioral timescale synaptic plasticity during working memory is gated by entorhinal inputs.

Behavioral timescale synaptic plasticity (BTSP) is a form of synaptic potentiation where the occurrence of a single large plateau potential in CA1 hippocampal neurons leads to the formation of reliable place fields during spatial learning tasks. We asked whether BTSP could also be a plasticity mechanism for generation of non-spatial responses in the hippocampus and what roles the medial and lateral entorhinal cortex (MEC and LEC) play in driving non-spatial BTSP. By performing simultaneous calcium imaging of dorsal CA1 neurons and chemogenetic inhibition of LEC or MEC while mice performed an olfactory working memory task, we discovered BTSP-like events which formed stable odor-specific fields. Critically, the success rate of calcium events generating a significant odor-field increased with event amplitude, and large events exhibited asymmetrical formation with the newly formed odor-fields preceding the timepoint of their induction event. We found that MEC and LEC play distinct roles in modulating BTSP: MEC inhibition reduced the frequency of large calcium events, while LEC inhibition reduced the success rate of odor-field generation. Using two-photon calcium imaging of LEC and MEC temporammonic axons projecting to CA1, we found that LEC projections to CA1 were strongly odor selective even early in task learning, while MEC projection odor-selectivity increased with task learning but remained weaker than LEC. Finally, we found that LEC and MEC inhibition both slowed representational drift of odor representations in CA1 across 48 hours. Altogether, odor-specific information from LEC and strong odor-timed activity from MEC are crucial for driving BTSP in CA1, which is a synaptic plasticity mechanism for generation of both spatial and non-spatial responses in the hippocampus that may play a role in explaining representational drift and one-shot learning of non-spatial information.

Learning, Fast and Slow: Single- and Many-Shot Learning in the Hippocampus.

The hippocampus is critical for memory and spatial navigation. The ability to map novel environments, as well as more abstract conceptual relationships, is fundamental to the cognitive flexibility that humans and other animals require to survive in a dynamic world. In this review, we survey recent advances in our understanding of how this flexibility is implemented anatomically and functionally by hippocampal circuitry, during both active exploration (online) and rest (offline). We discuss the advantages and limitations of spike timing-dependent plasticity and the more recently discovered behavioral timescale synaptic plasticity in supporting distinct learning modes in the hippocampus. Finally, we suggest complementary roles for these plasticity types in explaining many-shot and single-shot learning in the hippocampus and discuss how these rules could work together to support the learning of cognitive maps.

Emergent facilitation and glassy dynamics in supercooled liquids

In supercooled liquids, dynamical facilitation refers to a phenomenon where microscopic motion begets further motion nearby, resulting in spatially heterogeneous dynamics. This is central to the glassy relaxation dynamics of such liquids, which show super-Arrhenius growth of relaxation timescales with decreasing temperature. Despite the importance of dynamical facilitation, there is no theoretical understanding of how facilitation emerges and impacts relaxation dynamics. Here, we present a theory that explains the microscopic origins of dynamical facilitation. We show that dynamics proceeds by localized bond-exchange events, also known as excitations, resulting in the accumulation of elastic stresses with which new excitations can interact. At low temperatures, these elastic interactions dominate and facilitate the creation of new excitations near prior excitations. Using the theory of linear elasticity and Markov processes, we simulate a model, which reproduces multiple aspects of glassy dynamics observed in experiments and molecular simulations, including the stretched exponential decay of relaxation functions, the super-Arrhenius behavior of relaxation timescales as well as their two-dimensional finite-size effects. The model also predicts the subdiffusive behavior of the mean squared displacement (MSD) on short, intermediate timescales. Furthermore, we derive the phonon contributions to diffusion and relaxation, which when combined with the excitation contributions produce the two-step relaxation processes, and the ballistic-subdiffusive-diffusive crossover MSD behaviors commonly found in supercooled liquids.

Dopamine transients follow a striatal gradient of reward time horizons

Animals make predictions to guide their behavior and update those predictions through experience. Transient increases in dopamine (DA) are thought to be critical signals for updating predictions. However, it is unclear how this mechanism handles a wide range of behavioral timescales—from seconds or less (for example, if singing a song) to potentially hours or more (for example, if hunting for food). Here we report that DA transients in distinct rat striatal subregions convey prediction errors based on distinct time horizons. DA dynamics systematically accelerated from ventral to dorsomedial to dorsolateral striatum, in the tempo of spontaneous fluctuations, the temporal integration of prior rewards and the discounting of future rewards. This spectrum of timescales for evaluative computations can help achieve efficient learning and adaptive motivation for a broad range of behaviors.

Model selection for dynamic interfacial tension of dead crude oil/brine to estimate pressure and temperature effects on the equilibrium tension

Interfacial tension (IFT) measurements using pendant drop techniques are typically time-dependent and embed the underlying physics that can be exploited. A physics-based modeling approach of the time-varying IFT is chosen to estimate the equilibrium IFT and to identify which phenomena are leading the dynamic. This approach is both consistent and reproducible, hence advantageous for routine research and industrial applications because typical approaches are especially challenging when the techniques rely on long-time measurements or detailed characterization of phase composition and transport properties. The approach is applied to the Brazilian pre-salt dead crude oil/brine system when the equilibrium between the phases and between bulk and interface concentrations are not assured initially. Selected models based on a different physical phenomenon, mainly focused on diffusion and sorption, and with a particular time scale behavior are tested on measured data. The model which better fits the data in its full-time range should point out which phenomenon is dominant. The results indicate that the model with t time scale seems preferable for this oil/brine system, which is associated with a diffusion-controlled process in multiple regimes, for instance, bulk-to-interface diffusion of naturally present interface chemicals and their rearrangement at the interface. The effect of pressure (14.7 psi–4000 psi) and temperature (22 °C–60 °C) variations on the equilibrium IFT of the Brazilian pre-salt reservoir dead crude oil/brine at various conditions is characterized and discussed using the consistent physics-based estimate for the equilibrium IFT. The results show that the equilibrium IFT does not change significantly with pressure and increases with temperature, further supporting the identified presence of naturally present interface chemicals in the transient IFT measurements and the brine ionic valence influence. Furthermore, the wettability of carbonate rock samples with different mineralogical compositions to the dead crude oil/formation water is assessed using adhesion tension which embeds both IFT and contact angle contributions. Pressure changes do not affect wettability considerably, while the effects of temperature increase show that every sample tended to be wetter to its respective fluid affinity, following the IFT temperature behavior.

Photoinduced modification of optical properties of ferroelectric PZT thin films

This study investigates ultrafast photoinduced changes in optical properties of ferroelectrics (PZT) on femtosecond to nanosecond timescales, using broadband transient reflectivity studies. Surprisingly, spectral features were observed below the bandgap, which could not be attributed to ground state bleaching, excited state absorption, and/or stimulated emission. A model based on probe energy independent changes in refractive index and extinction coefficient showed good agreement with experimental results. Three relaxation processes were phenomenologically considered for the temporal evolution. Laser-induced heating was ruled out as the cause of short timescale behavior and photorefractive effect was suggested as a potential mechanism for changes in the optical properties.Graphical abstract

A critical role for CaMKII in behavioral timescale synaptic plasticity in hippocampal CA1 pyramidal neurons.

Behavioral timescale synaptic plasticity (BTSP) is a type of non-Hebbian synaptic plasticity reported to underlie place field formation. Despite this important function, the molecular mechanisms underlying BTSP are poorly understood. The α-calcium-calmodulin-dependent protein kinase II (αCaMKII) is activated by synaptic transmission-mediated calcium influx, and its subsequent phosphorylation is central to synaptic plasticity. Because the activity of αCaMKII is known to outlast the event triggering phosphorylation, we hypothesized that it could mediate the extended timescale of BTSP. To examine the role of αCaMKII in BTSP, we performed whole-cell in vivo and in vitro recordings in CA1 pyramidal neurons from mice engineered with a point mutation at the autophosphorylation site (T286A) causing accelerated signaling kinetics. Here, we demonstrate a profound deficit in synaptic plasticity, strongly suggesting that αCaMKII signaling is required for BTSP. This study elucidates part of the molecular mechanism of BTSP and provides insight into the function of αCaMKII in place cell formation and ultimately learning and memory.

Cerebellar contributions across behavioural timescales: a review from the perspective of cerebro-cerebellar interactions.

Performing successful adaptive behaviour relies on our ability to process a wide range of temporal intervals with certain precision. Studies on the role of the cerebellum in temporal information processing have adopted the dogma that the cerebellum is involved in sub-second processing. However, emerging evidence shows that the cerebellum might be involved in suprasecond temporal processing as well. Here we review the reciprocal loops between cerebellum and cerebral cortex and provide a theoretical account of cerebro-cerebellar interactions with a focus on how cerebellar output can modulate cerebral processing during learning of complex sequences. Finally, we propose that while the ability of the cerebellum to support millisecond timescales might be intrinsic to cerebellar circuitry, the ability to support supra-second timescales might result from cerebellar interactions with other brain regions, such as the prefrontal cortex.

Linking temporal coordination of hippocampal activity to memory function.

Oscillations in neural activity are widespread throughout the brain and can be observed at the population level through the local field potential. These rhythmic patterns are associated with cycles of excitability and are thought to coordinate networks of neurons, in turn facilitating effective communication both within local circuits and across brain regions. In the hippocampus, theta rhythms (4-12 Hz) could contribute to several key physiological mechanisms including long-range synchrony, plasticity, and at the behavioral scale, support memory encoding and retrieval. While neurons in the hippocampus appear to be temporally coordinated by theta oscillations, they also tend to fire in sequences that are developmentally preconfigured. Although loss of theta rhythmicity impairs memory, these sequences of spatiotemporal representations persist in conditions of altered hippocampal oscillations. The focus of this review is to disentangle the relative contribution of hippocampal oscillations from single-neuron activity in learning and memory. We first review cellular, anatomical, and physiological mechanisms underlying the generation and maintenance of hippocampal rhythms and how they contribute to memory function. We propose candidate hypotheses for how septohippocampal oscillations could support memory function while not contributing directly to hippocampal sequences. In particular, we explore how theta rhythms could coordinate the integration of upstream signals in the hippocampus to form future decisions, the relevance of such integration to downstream regions, as well as setting the stage for behavioral timescale synaptic plasticity. Finally, we leverage stimulation-based treatment in Alzheimer's disease conditions as an opportunity to assess the sufficiency of hippocampal oscillations for memory function.

Learning predictive cognitive maps with spiking neurons during behavior and replays.

The hippocampus has been proposed to encode environments using a representation that contains predictive information about likely future states, called the successor representation. However, it is not clear how such a representation could be learned in the hippocampal circuit. Here, we propose a plasticity rule that can learn this predictive map of the environment using a spiking neural network. We connect this biologically plausible plasticity rule to reinforcement learning, mathematically and numerically showing that it implements the TD-lambda algorithm. By spanning these different levels, we show how our framework naturally encompasses behavioral activity and replays, smoothly moving from rate to temporal coding, and allows learning over behavioral timescales with a plasticity rule acting on a timescale of milliseconds. We discuss how biological parameters such as dwelling times at states, neuronal firing rates and neuromodulation relate to the delay discounting parameter of the TD algorithm, and how they influence the learned representation. We also find that, in agreement with psychological studies and contrary to reinforcement learning theory, the discount factor decreases hyperbolically with time. Finally, our framework suggests a role for replays, in both aiding learning in novel environments and finding shortcut trajectories that were not experienced during behavior, in agreement with experimental data.

Dendritic plateau potentials can process spike sequences across multiple time-scales

The brain constantly processes information encoded in temporal sequences of spiking activity. This sequential activity emerges from sensory inputs as well as from the brain's own recurrent connectivity and spans multiple dynamically changing timescales. Decoding the temporal order of spiking activity across these varying timescales is a critical function of the brain, but we do not yet understand its neural implementation. The problem is, that the passive dynamics of neural membrane potentials occur on a short millisecond timescale, whereas many cognitive tasks require the integration of information across much slower behavioral timescales. However, actively generated dendritic plateau potentials do occur on such longer timescales, and their essential role for many aspects of cognition has been firmly established by recent experiments. Here, we build on these discoveries and propose a new model of neural computation that emerges from the interaction of localized plateau potentials across a functionally compartmentalized dendritic tree. We show how this interaction offers a robust solution to the timing invariant detection and processing of sequential spike patterns in single neurons. Stochastic synaptic transmission complements the deterministic all-or-none plateau process and improves information transmission by allowing ensembles of neurons to produce graded responses to continuous combinations of features. We found that networks of such neurons can solve highly complex sequence detection tasks by breaking down long inputs into sequences of shorter, random features that can be classified reliably. These results suggest that active dendritic processes are fundamental to neural computation.

Evaluating geophysical logs as proxies for cyclostratigraphy in lacustrine deposits using power ratio accumulation

Using a single insensitive proxy in cyclostratigraphy may lead to misleading conclusions regarding timescale, paleoenvironment, paleoclimate and orbital behavior. How to obtain the most reliable one from multiple solutions of geophysical log proxies without time constraints is still an outstanding problem. Here, we develop a new method (power ratio accumulation, or PRA) combining power decomposition and null hypothesis of no astronomical forcing with the Monte Carlo simulation. This method obtains both optimal sedimentation rate and optimal proxy from multiple untuned proxy datasets. The sensitivity of multiple geophysical log proxies in lacustrine deposits is first revealed from member 1 of Nenjiang Fm of Cretaceous Continental Scientific Drilling in the Songliao Basin using this method. The results show that the optimal proxy is thorium content, followed by potassium content, uranium content and natural gamma-ray intensity, while total organic carbon content, resistivity, interval transit time and density are the worst. Evidence of phase reversal of proxies caused by the joint influence of astronomically forced weathering, water level and burial of biogenic materials is found. This supports a new mechanism that explains the sensitivity of geophysical log proxies from the perspective of phase stability. Our research not only provides a powerful tool (PRA method) for astrochronologic testing and sensitivity evaluation of multiple untuned proxy datasets, but also builds a bridge (new mechanism) between geophysical parameters and astronomically forced cycles. This will promote the application of geophysical data in the field of cyclostratigraphy.

All-optical physiology resolves a synaptic basis for behavioral timescale plasticity

Learning has been associated with modifications of synaptic and circuit properties, but the precise changes storing information in mammals have remained largely unclear. We combined genetically targeted voltage imaging with targeted optogenetic activation and silencing of pre- and post-synaptic neurons to study the mechanisms underlying hippocampal behavioral timescale plasticity. In mice navigating a virtual-reality environment, targeted optogenetic activation of individual CA1 cells at specific places induced stable representations of these places in the targeted cells. Optical elicitation, recording, and modulation of synaptic transmission in behaving mice revealed that activity in presynaptic CA2/3 cells was required for the induction of plasticity in CA1 and, furthermore, that during induction of these place fields in single CA1 cells, synaptic input from CA2/3 onto these same cells was potentiated. These results reveal synaptic implementation of hippocampal behavioral timescale plasticity and define a methodology to resolve synaptic plasticity during learning and memory in behaving mammals.

Dynamic Community Detection Decouples Multiple Time Scale Behavior of Complex Chemical Systems.

Although community or cluster identification is becoming a standard tool within the simulation community, traditional algorithms are challenging to adapt to time-dependent data. Here, we introduce temporal community identification using the Δ-screening algorithm, which has the flexibility to account for varying community compositions, merging and splitting behaviors within dynamically evolving chemical networks. When applied to a complex chemical system whose varying chemical environments cause multiple time scale behavior, Δ-screening is able to resolve the multiple time scales of temporal communities. This computationally efficient algorithm is easily adapted to a wide range of dynamic chemical systems; flexibility in implementation allows the user to increase or decrease the resolution of temporal features by controlling parameters associated with community composition and fluctuations therein.

Entorhinal cortex directs learning-related changes in CA1 representations

Learning-related changes in brain activity are thought to underlie adaptive behaviours1,2. For instance, the learning of a reward site by rodents requires the development of an over-representation of that location in the hippocampus3–6. How this learning-related change occurs remains unknown. Here we recorded hippocampal CA1 population activity as mice learned a reward location on a linear treadmill. Physiological and pharmacological evidence suggests that the adaptive over-representation required behavioural timescale synaptic plasticity (BTSP)7. BTSP is known to be driven by dendritic voltage signals that we proposed were initiated by input from entorhinal cortex layer 3 (EC3). Accordingly, the CA1 over-representation was largely removed by optogenetic inhibition of EC3 activity. Recordings from EC3 neurons revealed an activity pattern that could provide an instructive signal directing BTSP to generate the over-representation. Consistent with this function, our observations show that exposure to a second environment possessing a prominent reward-predictive cue resulted in both EC3 activity and CA1 place field density that were more elevated at the cue than at the reward. These data indicate that learning-related changes in the hippocampus are produced by synaptic plasticity directed by an instructive signal from the EC3 that seems to be specifically adapted to the behaviourally relevant features of the environment.

Long timescale behavior of large self-modulation of electromagnetic wave caused by vacuum nonlinearity

Long timescale behavior of large self-modulation of electromagnetic wave caused by vacuum nonlinearity