Hippocampo-cortical circuits for selective memory encoding,routing, and replay

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Positive feedback circuits and memory

The concept of regulatory feedback circuit refers to oriented cyclic interactions between elements of a system. There are two classes of circuits, positive and negative, whose properties are in striking contrast. Positive circuits are a prerequisite for the occurrence of multiple steady states (multistationarity), and hence, they are involved in all processes showing hysteresis or memory. Endogenous or exogenous perturbations can lead the system to exhibit or to evoke one particular stable regime. The role of positive circuits in cell differentiation and in immunology is well documented. Negative circuits are involved in homeostatic regulation, with or without oscillations. The aim of this paper is to show: a) that positive circuits account for many features of memory stricto sensu (i.e., neural memory and mnesic evocation) as well as largo sensu (e.g. differentiation or immunological memory); and b) that simple combinations of positive and negative circuits provide powerful regulatory modules, which can also be associated in batteries. These entities have vast dynamical possibilities in the field of neurobiology, as well as in the fields of differentiation and immunology. Here we consider a universal minimal regulatory module, for which we suggest to adopt the term 'logical regulon', which can be considered as an atom of Jacob's integron. It comprises a positive and a negative circuit in its interaction matrix, and we recall the main results related to the simultaneous presence of these circuits. Finally, we give three applications of this type of interaction matrix. The first two deal with the coexistence of multiple stable steady states and periodicity in differentiation and in an immunological system showing hysteretic properties. The third deals with the dual problems of synchronization and desynchronization of a neural model for hippocampus memory evocation processes.     

Learning to learn: theta oscillations predict new learning, which enhances related learning and neurogenesis

Animals in the natural world continuously encounter learning experiences of varying degrees of novelty. New neurons in the hippocampus are especially responsive to learning associations between novel events and more cells survive if a novel and challenging task is learned. One might wonder whether new neurons would be rescued from death upon each new learning experience or whether there is an internal control system that limits the number of cells that are retained as a function of learning. In this experiment, it was hypothesized that learning a task that was similar in content to one already learned previously would not increase cell survival. We further hypothesized that in situations in which the cells are rescued hippocampal theta oscillations (3-12 Hz) would be involved and perhaps necessary for increasing cell survival. Both hypotheses were disproved. Adult male Sprague-Dawley rats were trained on two similar hippocampus-dependent tasks, trace and very-long delay eyeblink conditioning, while recording hippocampal local-field potentials. Cells that were generated after training on the first task were labeled with bromodeoxyuridine and quantified after training on both tasks had ceased. Spontaneous theta activity predicted performance on the first task and the conditioned stimulus induced a theta-band response early in learning the first task. As expected, performance on the first task correlated with performance on the second task. However, theta activity did not increase during training on the second task, even though more cells were present in animals that had learned. Therefore, as long as learning occurs, relatively small changes in the environment are sufficient to increase the number of surviving neurons in the adult hippocampus and they can do so in the absence of an increase in theta activity. In conclusion, these data argue against an upper limit on the number of neurons that can be rescued from death by learning.     

Tracking a changing environment: optimal sampling, adaptive memory and overnight effects

Foraging in a variable environment presents a classic problem of decision making with incomplete information. Animals must track the changing environment, remember the best options and make choices accordingly. While several experimental studies have explored the idea that sampling behavior reflects the amount of environmental change, we take the next logical step in asking how change influences memory. We explore the hypothesis that memory length should be tied to the ecological relevance and the value of the information learned, and that environmental change is a key determinant of the value of memory. We use a dynamic programming model to confirm our predictions and then test memory length in a factorial experiment. In our experimental situation we manipulate rates of change in a simple foraging task for blue jays over a 36 h period. After jays experienced an experimentally determined change regime, we tested them at a range of retention intervals, from 1 to 72 h. Manipulated rates of change influenced learning and sampling rates: subjects sampled more and learned more quickly in the high change condition. Tests of retention revealed significant interactions between retention interval and the experienced rate of change. We observed a striking and surprising difference between the high and low change treatments at the 24 h retention interval. In agreement with earlier work we find that a circadian retention interval is special, but we find that the extent of this ‘specialness’ depends on the subject's prior experience of environmental change. Specifically, experienced rates of change seem to influence how subjects balance recent information against past experience in a way that interacts with the passage of time.     

Working memory: linking capacity with selectivity

Working memory is one of the most intensively studied psychological processes, but little is known about what distinguishes individuals in their working memory capacity. Recent evidence from electroencephalogram recordings suggests that one crucial component of this variation is our ability to exclude irrelevant information.     

Mushroom bodies, Ca(2+) oscillations, and the memory gene amnesiac.

The memory of odors in Drosophila is mediated by mushroom body neurons. Memory is formed, in part, by a modulation of the physiology of these neurons brought about by neuropeptides that are encoded by the amnesiac gene and released from peptidergic neurons that innervate mushroom body neurons. Slow and spontaneous oscillations of calcium levels are elevated in the mushroom body neurons of amnesiac mutants and may contribute to memory consolidation.     

Hippocampal-prefrontal connectivity predicts midfrontal oscillations and long-term memory performance

The hippocampus and prefrontal cortex interact to support working memory (WM) and long-term memory [1-3]. Neurophysiologically, WM is thought to be subserved by reverberatory activity of distributed networks within the prefrontal cortex (PFC) [2, 4-8], which become synchronized with reverberatory activity in the hippocampus [1, 4]. This electrophysiological synchronization is difficult to study in humans because noninvasive electroencephalography (EEG) cannot measure hippocampus activity. Here, using a novel integration of EEG and diffusion-weighted imaging, it is shown that individuals with relatively stronger anatomical connectivity linking the hippocampus to the right ventrolateral PFC (ventral Brodmann area 46) exhibited slower frequency neuronal oscillations during a WM task. Furthermore, subjects with stronger hippocampus-PFC connectivity were better able to encode the complex pictures used in the WM task into long-term memory. These findings are consistent with models suggesting that electrophysiological oscillations provide a mechanism of long-range interactions [9] and link hippocampus-PFC structural connectivity to PFC rhythmic electrical dynamics and memory performance. More generally, these results highlight the importance of incorporating individual differences when linking structure and function to cognition.     

Short-term depression and transient memory in sensory cortex

Persistent neuronal activity is usually studied in the context of short-term memory localized in central cortical areas. Recent studies show that early sensory areas also can have persistent representations of stimuli which emerge quickly (over tens of milliseconds) and decay slowly (over seconds). Traditional positive feedback models cannot explain sensory persistence for at least two reasons: (i) They show attractor dynamics, with transient perturbations resulting in a quasi-permanent change of system state, whereas sensory systems return to the original state after a transient. (ii) As we show, those positive feedback models which decay to baseline lose their persistence when their recurrent connections are subject to short-term depression, a common property of excitatory connections in early sensory areas. Dual time constant network behavior has also been implemented by nonlinear afferents producing a large transient input followed by much smaller steady state input. We show that such networks require unphysiologically large onset transients to produce the rise and decay observed in sensory areas. Our study explores how memory and persistence can be implemented in another model class, derivative feedback networks. We show that these networks can operate with two vastly different time courses, changing their state quickly when new information is coming in but retaining it for a long time, and that these capabilities are robust to short-term depression. Specifically, derivative feedback networks with short-term depression that acts differentially on positive and negative feedback projections are capable of dynamically changing their time constant, thus allowing fast onset and slow decay of responses without requiring unrealistically large input transients.     

Systems-level dissection of the cell-cycle oscillator: bypassing positive feedback produces damped oscillations

The cell-cycle oscillator includes an essential negative-feedback loop: Cdc2 activates the anaphase-promoting complex (APC), which leads to cyclin destruction and Cdc2 inactivation. Under some circumstances, a negative-feedback loop is sufficient to generate sustained oscillations. However, the Cdc2/APC system also includes positive-feedback loops, whose functional importance we now assess. We show that short-circuiting positive feedback makes the oscillations in Cdc2 activity faster, less temporally abrupt, and damped. This compromises the activation of cyclin destruction and interferes with mitotic exit and DNA replication. This work demonstrates a systems-level role for positive-feedback loops in the embryonic cell cycle and provides an example of how oscillations can emerge out of combinations of subcircuits whose individual behaviors are not oscillatory. This work also underscores the fundamental similarity of cell-cycle oscillations in embryos to repetitive action potentials in pacemaker neurons, with both systems relying on a combination of negative and positive-feedback loops.     

Visual working memory capacity and proactive interference

Background

Visual working memory capacity is extremely limited and appears to be relatively immune to practice effects or the use of explicit strategies. The recent discovery that visual working memory tasks, like verbal working memory tasks, are subject to proactive interference, coupled with the fact that typical visual working memory tasks are particularly conducive to proactive interference, suggests that visual working memory capacity may be systematically under-estimated.

Methodology/Principal Findings

Working memory capacity was probed behaviorally in adult humans both in laboratory settings and via the Internet. Several experiments show that although the effect of proactive interference on visual working memory is significant and can last over several trials, it only changes the capacity estimate by about 15%.

Conclusions/Significance

This study further confirms the sharp limitations on visual working memory capacity, both in absolute terms and relative to verbal working memory. It is suggested that future research take these limitations into account in understanding differences across a variety of tasks between human adults, prelinguistic infants and nonlinguistic animals.     

The shaping of intrinsic membrane potential oscillations: positive/negative feedback,ionic resonance/amplification,nonlinearities and time scales

The generation of intrinsic subthreshold (membrane potential) oscillations (STOs) in neuronal models requires the interaction between two processes: a relatively fast positive feedback that favors changes in voltage and a slower negative feedback that opposes these changes. These are provided by the so-called resonant and amplifying gating variables associated to the participating ionic currents. We investigate both the biophysical and dynamic mechanisms of generation of STOs and how their attributes (frequency and amplitude) depend on the model parameters for biophysical (conductance-based) models having qualitatively different types of resonant currents (activating and inactivating) and an amplifying current. Combinations of the same types of ionic currents (same models) in different parameter regimes give rise to different types of nonlinearities in the voltage equation: quasi-linear, parabolic-like and cubic-like. On the other hand, combinations of different types of ionic currents (different models) may give rise to the same type of nonlinearities. We examine how the attributes of the resulting STOs depend on the combined effect of these resonant and amplifying ionic processes, operating at different effective time scales, and the various types of nonlinearities. We find that, while some STO properties and attribute dependencies on the model parameters are determined by the specific combinations of ionic currents (biophysical properties), and are different for models with different such combinations, others are determined by the type of nonlinearities and are common for models with different types of ionic currents. Our results highlight the richness of STO behavior in single cells as the result of the various ways in which resonant and amplifying currents interact and affect the generation and termination of STOs as control parameters change. We make predictions that can be tested experimentally and are expected to contribute to the understanding of how rhythmic activity in neuronal networks emerge from the interplay of the intrinsic properties of the participating neurons and the network connectivity.     

Coordination of leaf N,anatomy, photosynthetic capacity,and hydraulics enhances evergreen expansive potential

Key message

Reduced leaf longevity, N-fixation, and enhanced hydraulic capacity combined support greater shifts in seasonal photosynthetic capacity of an expansive understory evergreen woody species relative to co-occurring less expansive evergreen species.

Abstract

Physiological functioning typically declines with increased leaf life span. While an evergreen leaf habit is generally associated with reduced leaf N, physiological capacity, and slower growth, most expansive woody species are evergreens and/or N fixers. An evergreen leaf habit enables year-round activity and less investment in carbon and nutrients, while N-fixation enhances photosynthetic capacity. Our objective was to compare anatomy and physiology of three woody evergreens Ilex opaca Aiton (Aquifoliaceae), Kalmia latifolia L. (Ericaceae), and Myrica cerifera (Myricaceae) of varying leaf longevity, N-fixation capability, and known expansive potential in a deciduous forest understory to determine if seasonal physiological performance integrated these factors. We hypothesized that I. opaca (non-expansive) and K. latifolia (moderately expansive), which have longer leaf longevities, would have reduced physiological performance compared to M. cerifera (expansive), which has shorter leaf longevity, and symbiotically fixes atmospheric N. Stomatal conductance to water vapor, photosynthetic and hydraulic capacities, specific leaf area, and leaf %N decreased with increasing leaf life span; however, trends among species were not consistent seasonally. While hydraulic capacity remained constant throughout the year, photosynthetic capacity did not. During the growing season, M. cerifera displayed photosynthetic capacity similar to deciduous species, yet, during the winter, photosynthetic capacity was similar to the slower-growing evergreens. Reduced leaf life span, enhanced hydraulic capacity, and nitrogen fixation support the seasonal shift in photosynthetic capacity observed in M. cerifera. This “hybrid” strategy enables M. cerifera to maximize productivity during months of optimal conditions, thereby promoting rapid growth and expansion in the understory.     

Dietary citrate supplementation enhances longevity,metabolic health,and memory performance through promoting ketogenesis

Citrate is an essential substrate for energy metabolism that plays critical roles in regulating cell growth and survival. However, the action of citrate in regulating metabolism, cognition, and aging at the organismal level remains poorly understood. Here, we report that dietary supplementation with citrate significantly reduces energy status and extends lifespan in Drosophila melanogaster. Our genetic studies in fruit flies implicate a molecular mechanism associated with AMP‐activated protein kinase (AMPK), target of rapamycin (TOR), and ketogenesis. Mice fed a high‐fat diet that supplemented with citrate or the ketone body β‐hydroxybutyrate (βOHB) also display improved metabolic health and memory. These results suggest that dietary citrate supplementation may prove to be a useful intervention in the future treatment of age‐related dysfunction.     

Sequence learning,prediction, and replay in networks of spiking neurons

Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns high-order sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities. The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role of sequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effect of the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay.     

Robust,tunable genetic memory from protein sequestration combined with positive feedback

Natural regulatory networks contain many interacting components that allow for fine-tuning of switching and memory properties. Building simple bistable switches, synthetic biologists have learned the design principles of complex natural regulatory networks. However, most switches constructed so far are so simple (e.g. comprising two regulators) that they are functional only within a limited parameter range. Here, we report the construction of robust, tunable bistable switches in Escherichia coli using three heterologous protein regulators (ExsADC) that are sequestered into an inactive complex through a partner swapping mechanism. On the basis of mathematical modeling, we accurately predict and experimentally verify that the hysteretic region can be fine-tuned by controlling the interactions of the ExsADC regulatory cascade using the third member ExsC as a tuning knob. Additionally, we confirm that a dual-positive feedback switch can markedly increase the hysteretic region, compared to its single-positive feedback counterpart. The dual-positive feedback switch displays bistability over a 10⁶-fold range of inducer concentrations, to our knowledge, the largest range reported so far. This work demonstrates the successful interlocking of sequestration-based ultrasensitivity and positive feedback, a design principle that can be applied to the construction of robust, tunable, and predictable genetic programs to achieve increasingly sophisticated biological behaviors.     

On being the right size: food-limited feedback on optimal body size

An insular population of white-tailed deer Odocoileus virginianus introduced in 1896 to predator-free Anticosti Island, Quebec, has caused long-term changes in the plant community. Food quality declined as did body weight. Although different parameters of reproduction changed, overall reproductive rates remained similar, thus maintaining deer density and promoting further change in habitat. These results show (i) long-term feedbacks on carrying capacity, (ii) the mechanism for reduction of body mass, and (iii) the lack of strong reduction in reproductive rates to regulate the population at high density, a feature of Eutherians. They are relevant to mechanisms determining the evolution of vertebrate body sizes.