Oscillatory models of the hippocampus: A study of spatio-temporal patterns of neural activity

Spatial patterns of theta-rhythm activity in oscillatory models of the hippocampus are studied here using canonical models for both Hodgkin's class-1 and class-2 excitable neuronal systems. Dynamics of these models are studied in both the frequency domain, to determine phase-locking patterns, and in the time domain, to determine the amplitude responses resulting from phase-locking patterns. Computer simulations presented here demonstrate that phase deviations (timings) between inputs from the medial septum and the entorhinal cortex can create spatial patterns of theta-rhythm phase-locking. In this way, we show that the timing of inputs (not only their frequencies alone) can encode specific patterns of theta-rhythm activity. This study suggests new experiments to determine temporal and spatial synchronization. Received: 31 July 1998 /Accepted in revised form: 20 April 1999     

A New Framework for Cortico-Striatal Plasticity: Behavioural Theory Meets In Vitro Data at the Reinforcement-Action Interface

Operant learning requires that reinforcement signals interact with action representations at a suitable neural interface. Much evidence suggests that this occurs when phasic dopamine, acting as a reinforcement prediction error, gates plasticity at cortico-striatal synapses, and thereby changes the future likelihood of selecting the action(s) coded by striatal neurons. But this hypothesis faces serious challenges. First, cortico-striatal plasticity is inexplicably complex, depending on spike timing, dopamine level, and dopamine receptor type. Second, there is a credit assignment problem—action selection signals occur long before the consequent dopamine reinforcement signal. Third, the two types of striatal output neuron have apparently opposite effects on action selection. Whether these factors rule out the interface hypothesis and how they interact to produce reinforcement learning is unknown. We present a computational framework that addresses these challenges. We first predict the expected activity changes over an operant task for both types of action-coding striatal neuron, and show they co-operate to promote action selection in learning and compete to promote action suppression in extinction. Separately, we derive a complete model of dopamine and spike-timing dependent cortico-striatal plasticity from in vitro data. We then show this model produces the predicted activity changes necessary for learning and extinction in an operant task, a remarkable convergence of a bottom-up data-driven plasticity model with the top-down behavioural requirements of learning theory. Moreover, we show the complex dependencies of cortico-striatal plasticity are not only sufficient but necessary for learning and extinction. Validating the model, we show it can account for behavioural data describing extinction, renewal, and reacquisition, and replicate in vitro experimental data on cortico-striatal plasticity. By bridging the levels between the single synapse and behaviour, our model shows how striatum acts as the action-reinforcement interface.     

Minutes,days and years: molecular interactions among different scales of biological timing

Biological clocks are genetically encoded oscillators that allow organisms to keep track of their environment. Among them, the circadian system is a highly conserved timing structure that regulates several physiological, metabolic and behavioural functions with periods close to 24 h. Time is also crucial for everyday activities that involve conscious time estimation. Timing behaviour in the second-to-minutes range, known as interval timing, involves the interaction of cortico-striatal circuits. In this review, we summarize current findings on the neurobiological basis of the circadian system, both at the genetic and behavioural level, and also focus on its interactions with interval timing and seasonal rhythms, in order to construct a multi-level biological clock.     

Dissociable reward and timing signals in human midbrain and ventral striatum

Reward prediction error (RPE) signals are central to current models of reward-learning. Temporal difference (TD) learning models posit that these signals should be modulated by predictions, not only of magnitude but also timing of reward. Here we show that BOLD activity in the VTA conforms to such TD predictions: responses to unexpected rewards are modulated by a temporal hazard function and activity between a predictive stimulus and reward is depressed in proportion to predicted reward. By contrast, BOLD activity in ventral striatum (VS) does not reflect a TD RPE, but instead encodes a signal on the variable relevant for behavior, here timing but not magnitude of reward. The results have important implications for dopaminergic models of cortico-striatal learning and suggest a modification of the conventional view that VS BOLD necessarily reflects inputs from dopaminergic VTA neurons signaling an RPE.     

Modulation of human time processing by subthalamic deep brain stimulation

Timing in the range of seconds referred to as interval timing is crucial for cognitive operations and conscious time processing. According to recent models of interval timing basal ganglia (BG) oscillatory loops are involved in time interval recognition. Parkinsońs disease (PD) is a typical disease of the basal ganglia that shows distortions in interval timing. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a powerful treatment of PD which modulates motor and cognitive functions depending on stimulation frequency by affecting subcortical-cortical oscillatory loops. Thus, for the understanding of BG-involvement in interval timing it is of interest whether STN-DBS can modulate timing in a frequency dependent manner by interference with oscillatory time recognition processes. We examined production and reproduction of 5 and 15 second intervals and millisecond timing in a double blind, randomised, within-subject repeated-measures design of 12 PD-patients applying no, 10-Hz- and ≥ 130-Hz-STN-DBS compared to healthy controls. We found under(re-)production of the 15-second interval and a significant enhancement of this under(re-)production by 10-Hz-stimulation compared to no stimulation, ≥ 130-Hz-STN-DBS and controls. Milliseconds timing was not affected. We provide first evidence for a frequency-specific modulatory effect of STN-DBS on interval timing. Our results corroborate the involvement of BG in general and of the STN in particular in the cognitive representation of time intervals in the range of multiple seconds.     

Interactions between light, mealtime and calorie restriction to control daily timing in mammals

Daily variations in behaviour and physiology are controlled by a circadian timing system consisting of a network of oscillatory structures. In mammals, a master clock, located in the suprachiasmatic nuclei (SCN) of the hypothalamus, adjusts timing of other self-sustained oscillators in the brain and peripheral organs. Synchronisation to external cues is mainly achieved by ambient light, which resets the SCN clock. Other environmental factors, in particular food availability and time of feeding, also influence internal timing. Timed feeding can reset the phase of the peripheral oscillators whilst having almost no effect in shifting the phase of the SCN clockwork when animals are exposed (synchronised) to a light–dark cycle. Food deprivation and calorie restriction lead not only to loss of body mass (>15%) and increased motor activity, but also affect the timing of daily activity, nocturnal animals becoming partially diurnal (i.e. they are active during their usual sleep period). This change in behavioural timing is due in part to the fact that metabolic cues associated with calorie restriction affect the SCN clock and its synchronisation to light.     

Daily activity timing in the Anthropocene

Animals are facing novel ‘timescapes’ in which the stimuli entraining their daily activity patterns no longer match historical conditions due to anthropogenic disturbance. However, the ecological effects (e.g., altered physiology, species interactions) of novel activity timing are virtually unknown. We reviewed 1328 studies and found relatively few focusing on anthropogenic effects on activity timing. We suggest three hypotheses to stimulate future research: (i) activity-timing mismatches determine ecological effects, (ii) duration and timing of timescape modification influence effects, and (iii) consequences of altered activity timing vary biogeographically due to broad-scale variation in factors compressing timescapes. The continued growth of sampling technologies promises to facilitate the study of the consequences of altered activity timing, with emerging applications for biodiversity conservation.     

Competition in the temporal domain among neural activities phase-locked to subthreshold oscillations

 In the presence of a subthreshold membrane oscillation, analog information may be encoded in the timing of spike generation phase-locked to the oscillation. With this spike timing neural code, a competitive network of inhibitory spiking neurons was shown to achieve a novel timing mechanism of neural activity selection: the neurons had higher probabilities of becoming winners if they were stimulated earlier in each oscillatory cycle. Here the timing mechanism and its robustness are studied both numerically and analytically, and the conditions to yield a given number of winners (the inhibitory neurons that remain active after the competition) are investigated. The analysis revealed that activity selection with a small number of winners is ensured for broad ranges of values of the parameters such as the strength and time constant of inhibition. In particular, the number of winners is almost unchanged for various timing differences between stimuli to different neurons. This implies that the timing mechanism is useful for such biological information processing as requires perception of a relatively small number of significant stimulus components. Received: 24 January 1996 / Accepted in revised form: 24 July 1996     

The luteinizing hormone surge regulates circadian clock gene expression in the chicken ovary

The molecular circadian clock mechanism is highly conserved between mammalian and avian species. Avian circadian timing is regulated at multiple oscillatory sites, including the retina, pineal, and hypothalamic suprachiasmatic nucleus (SCN). Based on the authors' previous studies on the rat ovary, it was hypothesized that ovarian clock timing is regulated by the luteinizing hormone (LH) surge. The authors used the chicken as a model to test this hypothesis, because the timing of the endogenous LH surge is accurately predicted from the time of oviposition. Therefore, tissues can be removed before and after the LH surge, allowing one to determine the effect of LH on specific clock genes. The authors first examined the 24-h expression patterns of the avian circadian clock genes of Bmal1, Cry1, and Per2 in primary oscillatory tissues (hypothalamus and pineal) as well as peripheral tissues (liver and ovary). Second, the authors determined changes in clock gene expression after the endogenous LH surge. Clock genes were rhythmically expressed in each tissue, but LH influenced expression of these clock genes only in the ovary. The data suggest that expression of ovarian circadian clock genes may be influenced by the LH surge in vivo and directly by LH in cultured granulosa cells. LH induced rhythmic expression of Per1 and Bmal1 in arrhythmic, cultured granulosa cells. Furthermore, LH altered the phase and amplitude of clock gene rhythms in serum-shocked granulosa cells. Thus, the LH surge may be a mechanistic link for communicating circadian timing information from the central pacemaker to the ovary.     

Biological timing and the clock metaphor: oscillatory and hourglass mechanisms

Living organisms have developed a multitude of timing mechanisms--"biological clocks." Their mechanisms are based on either oscillations (oscillatory clocks) or unidirectional processes (hourglass clocks). Oscillatory clocks comprise circatidal, circalunidian, circadian, circalunar, and circannual oscillations--which keep time with environmental periodicities--as well as ultradian oscillations, ovarian cycles, and oscillations in development and in the brain, which keep time with biological timescales. These clocks mainly determine time points at specific phases of their oscillations. Hourglass clocks are predominantly found in development and aging and also in the brain. They determine time intervals (duration). More complex timing systems combine oscillatory and hourglass mechanisms, such as the case for cell cycle, sleep initiation, or brain clocks, whereas others combine external and internal periodicities (photoperiodism, seasonal reproduction). A definition of a biological clock may be derived from its control of functions external to its own processes and its use in determining temporal order (sequences of events) or durations. Biological and chemical oscillators are characterized by positive and negative feedback (or feedforward) mechanisms. During evolution, living organisms made use of the many existing oscillations for signal transmission, movement, and pump mechanisms, as well as for clocks. Some clocks, such as the circadian clock, that time with environmental periodicities are usually compensated (stabilized) against temperature, whereas other clocks, such as the cell cycle, that keep time with an organismic timescale are not compensated. This difference may be related to the predominance of negative feedback in the first class of clocks and a predominance of positive feedback (autocatalytic amplification) in the second class. The present knowledge of a compensated clock (the circadian oscillator) and an uncompensated clock (the cell cycle), as well as relevant models, are briefly re viewed. Hourglass clocks are based on linear or exponential unidirectional processes that trigger events mainly in the course of development and aging. An important hourglass mechanism within the aging process is the limitation of cell division capacity by the length of telomeres. The mechanism of this clock is briefly reviewed. In all clock mechanisms, thresholds at which "dependent variables" are triggered play an important role.     

Encoding time in neural dynamic regimes with distinct computational tradeoffs

Converging evidence suggests the brain encodes time in dynamic patterns of neural activity, including neural sequences, ramping activity, and complex dynamics. Most temporal tasks, however, require more than just encoding time, and can have distinct computational requirements including the need to exhibit temporal scaling, generalize to novel contexts, or robustness to noise. It is not known how neural circuits can encode time and satisfy distinct computational requirements, nor is it known whether similar patterns of neural activity at the population level can exhibit dramatically different computational or generalization properties. To begin to answer these questions, we trained RNNs on two timing tasks based on behavioral studies. The tasks had different input structures but required producing identically timed output patterns. Using a novel framework we quantified whether RNNs encoded two intervals using either of three different timing strategies: scaling, absolute, or stimulus-specific dynamics. We found that similar neural dynamic patterns at the level of single intervals, could exhibit fundamentally different properties, including, generalization, the connectivity structure of the trained networks, and the contribution of excitatory and inhibitory neurons. Critically, depending on the task structure RNNs were better suited for generalization or robustness to noise. Further analysis revealed different connection patterns underlying the different regimes. Our results predict that apparently similar neural dynamic patterns at the population level (e.g., neural sequences) can exhibit fundamentally different computational properties in regards to their ability to generalize to novel stimuli and their robustness to noise—and that these differences are associated with differences in network connectivity and distinct contributions of excitatory and inhibitory neurons. We also predict that the task structure used in different experimental studies accounts for some of the experimentally observed variability in how networks encode time.     

The Luteinizing Hormone Surge Regulates Circadian Clock Gene Expression in the Chicken Ovary

The molecular circadian clock mechanism is highly conserved between mammalian and avian species. Avian circadian timing is regulated at multiple oscillatory sites, including the retina, pineal, and hypothalamic suprachiasmatic nucleus (SCN). Based on the authors’ previous studies on the rat ovary, it was hypothesized that ovarian clock timing is regulated by the luteinizing hormone (LH) surge. The authors used the chicken as a model to test this hypothesis, because the timing of the endogenous LH surge is accurately predicted from the time of oviposition. Therefore, tissues can be removed before and after the LH surge, allowing one to determine the effect of LH on specific clock genes. The authors first examined the 24-h expression patterns of the avian circadian clock genes of Bmal1, Cry1, and Per2 in primary oscillatory tissues (hypothalamus and pineal) as well as peripheral tissues (liver and ovary). Second, the authors determined changes in clock gene expression after the endogenous LH surge. Clock genes were rhythmically expressed in each tissue, but LH influenced expression of these clock genes only in the ovary. The data suggest that expression of ovarian circadian clock genes may be influenced by the LH surge in vivo and directly by LH in cultured granulosa cells. LH induced rhythmic expression of Per1 and Bmal1 in arrhythmic, cultured granulosa cells. Furthermore, LH altered the phase and amplitude of clock gene rhythms in serum-shocked granulosa cells. Thus, the LH surge may be a mechanistic link for communicating circadian timing information from the central pacemaker to the ovary. (Author correspondence: stischkau@siumed.edu)     

Habitat‐specific capture timing of deer mice (Peromyscus maniculatus) suggests that predators structure temporal activity of prey

Timing is an essential component of the choices that animals make: The likelihood of successful resource capture (and predator avoidance) depends not just on what an animal chooses to do, but when it chooses to do it. Despite the importance of activity timing, our ability to understand the forces that constrain activity timing has been limited because this aspect of animal behavior is shaped by several factors (e.g., interspecific competitors, predators, physical conditions), and it is difficult to examine activity timing in a setting where only a single factor is operating. Using an island system that makes it possible to focus on the effect of predation risk in the absence of interspecific competition, we examine how the onset of activity of the deer mouse (Peromyscus maniculatus) varies between habitats with unique predation risks (i.e., minimal‐shrub cover versus abundant‐shrub cover sites). Using capture time to assess the timing of mouse activity, we found that mice in habitats with minimal shrub cover were captured 1.7 hr earlier than mice in habitats with abundant shrub cover. This difference in timing between habitats was likely a direct response to differences in predation risk between the two habitats: There were no differences in thermal conditions between the two habitats, and the difference in activity timing disappeared during a night when overcast skies reduced island‐wide predation risk. Our results demonstrate that predation risk, independent of interspecific competition, can generate significant changes in animal activity timing. Our work suggests that habitat structure that provides safety (i.e., refuge habitats) plays a direct role in the timing of prey activity and that habitat modification that alters refuge availability (e.g., shrub dominance) may alter the timing of animal activity.     

Modeling activity patterns of wildlife using time‐series analysis

The study of wildlife activity patterns is an effective approach to understanding fundamental ecological and evolutionary processes. However, traditional statistical approaches used to conduct quantitative analysis have thus far had limited success in revealing underlying mechanisms driving activity patterns. Here, we combine wavelet analysis, a type of frequency‐based time‐series analysis, with high‐resolution activity data from accelerometers embedded in GPS collars to explore the effects of internal states (e.g., pregnancy) and external factors (e.g., seasonal dynamics of resources and weather) on activity patterns of the endangered giant panda (Ailuropoda melanoleuca). Giant pandas exhibited higher frequency cycles during the winter when resources (e.g., water and forage) were relatively poor, as well as during spring, which includes the giant panda's mating season. During the summer and autumn when resources were abundant, pandas exhibited a regular activity pattern with activity peaks every 24 hr. A pregnant individual showed distinct differences in her activity pattern from other giant pandas for several months following parturition. These results indicate that animals adjust activity cycles to adapt to seasonal variation of the resources and unique physiological periods. Wavelet coherency analysis also verified the synchronization of giant panda activity level with air temperature and solar radiation at the 24‐hr band. Our study also shows that wavelet analysis is an effective tool for analyzing high‐resolution activity pattern data and its relationship to internal and external states, an approach that has the potential to inform wildlife conservation and management across species.     

Temporal uncertainty in reading the neural code (proportional noise)

It is widely accepted that neural information is encoded in the timing of action potentials. However, such information can only be transmitted if the receiver (soma, muscle fibre) can measure time precisely. We argue that such timing is inevitably imprecise due to biological fluctuations in receiver function. We show that this leads to a receiver output, where the standard deviation of receiver output noise is proportional to the mean level of the receiver output. Proportional noise is fundamentally distinct from the common assumption of square-root relationship seen in renewal processes (e.g. Poisson noise), which is usually assumed in neural firing patterns. We show that proportional noise may be the limiting constraint in receiver function, and that it could account for such diverse perceptual and motor behaviours encompassed by Weber's and Fitt's laws, as well as the dynamic trajectories of rapid feed-forward motor behaviours such as arm reaching and saccades. We conclude that temporal uncertainty should be potentially a fundamental stochastic constraint on network function and behaviour.     

Promoters Architecture-Based Mechanism for Noise-Induced Oscillations in a Single-Gene Circuit

It is well known that single-gene circuits with negative feedback loop can lead to oscillatory gene expression when they operate with time delay. In order to generate these oscillations many processes can contribute to properly timing such delay. Here we show that the time delay coming from the transitions between internal states of the cis-regulatory system (CRS) can drive sustained oscillations in an auto-repressive single-gene circuit operating in a small volume like a cell. We found that the cooperative binding of repressor molecules is not mandatory for a oscillatory behavior if there are enough binding sites in the CRS. These oscillations depend on an adequate balance between the CRS kinetic, and the synthesis/degradation rates of repressor molecules. This finding suggest that the multi-site CRS architecture can play a key role for oscillatory behavior of gene expression. Finally, our results can also help to synthetic biologists on the design of the promoters architecture for new genetic oscillatory circuits.     

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.     

Asymmetrical Effect of Levodopa on the Neural Activity of Motor Regions in PD

Parkinson''s disease (PD) is a neurodegenerative illness often characterized by asymmetrical symptoms. However, the reason for this asymmetry and the cerebral correlates underlying symptom asymmetry are still not well understood. Furthermore, the effects of levodopa on the cerebral correlates of disease asymmetry have not been investigated. In this study, right-handed PD patients performed self-initiated, externally triggered and repetitive control finger movements with both their right and left hands during functional magnetic resonance imaging (fMRI) to investigate asymmetrical effects of levodopa on the hemodynamic correlates of finger movements. Patients completed two experimental sessions OFF and ON medication after a minimum of 12 hours medication withdrawal. We compared the effect of levodopa on the neural activation patterns underlying the execution of both the more affected and less affected hand for self-initiated and externally triggered movements. Our results show that levodopa led to larger differences in cerebral activity for movements of the more affected, left side: there were significant differences in activity after levodopa administration in regions of the motor cortico-striatal network when patients performed self-initiated and externally triggered movements with their left hand. By contrast, when patients used their right hand, levodopa led to differences in cerebellar activity only. As our patients were affected more severely on their left side, we propose that levodopa may help provide additional dopaminergic input, improving movements for the more severely affected side. These results suggest that the impact of reduced dopamine in the cortico-striatal system and the action of levodopa is not symmetrical.     

Saccadic Reaction Times to Audiovisual Stimuli Show Effects of Oscillatory Phase Reset

Initiating an eye movement towards a suddenly appearing visual target is faster when an accessory auditory stimulus occurs in close spatiotemporal vicinity. Such facilitation of saccadic reaction time (SRT) is well-documented, but the exact neural mechanisms underlying the crossmodal effect remain to be elucidated. From EEG/MEG studies it has been hypothesized that coupled oscillatory activity in primary sensory cortices regulates multisensory processing. Specifically, it is assumed that the phase of an ongoing neural oscillation is shifted due to the occurrence of a sensory stimulus so that, across trials, phase values become highly consistent (phase reset). If one can identify the phase an oscillation is reset to, it is possible to predict when temporal windows of high and low excitability will occur. However, in behavioral experiments the pre-stimulus phase will be different on successive repetitions of the experimental trial, and average performance over many trials will show no signs of the modulation. Here we circumvent this problem by repeatedly presenting an auditory accessory stimulus followed by a visual target stimulus with a temporal delay varied in steps of 2 ms. Performing a discrete time series analysis on SRT as a function of the delay, we provide statistical evidence for the existence of distinct peak spectral components in the power spectrum. These frequencies, although varying across participants, fall within the beta and gamma range (20 to 40 Hz) of neural oscillatory activity observed in neurophysiological studies of multisensory integration. Some evidence for high-theta/alpha activity was found as well. Our results are consistent with the phase reset hypothesis and demonstrate that it is amenable to testing by purely psychophysical methods. Thus, any theory of multisensory processes that connects specific brain states with patterns of saccadic responses should be able to account for traces of oscillatory activity in observable behavior.