Reversible plasticity of fear memory-encoding amygdala synaptic circuits even after fear memory consolidation

It is generally believed that after memory consolidation, memory-encoding synaptic circuits are persistently modified and become less plastic. This, however, may hinder the remaining capacity of information storage in a given neural circuit. Here we consider the hypothesis that memory-encoding synaptic circuits still retain reversible plasticity even after memory consolidation. To test this, we employed a protocol of auditory fear conditioning which recruited the vast majority of the thalamic input synaptic circuit to the lateral amygdala (T-LA synaptic circuit; a storage site for fear memory) with fear conditioning-induced synaptic plasticity. Subsequently the fear memory-encoding synaptic circuits were challenged with fear extinction and re-conditioning to determine whether these circuits exhibit reversible plasticity. We found that fear memory-encoding T-LA synaptic circuit exhibited dynamic efficacy changes in tight correlation with fear memory strength even after fear memory consolidation. Initial conditioning or re-conditioning brought T-LA synaptic circuit near the ceiling of their modification range (occluding LTP and enhancing depotentiation in brain slices prepared from conditioned or re-conditioned rats), while extinction reversed this change (reinstating LTP and occluding depotentiation in brain slices prepared from extinguished rats). Consistently, fear conditioning-induced synaptic potentiation at T-LA synapses was functionally reversed by extinction and reinstated by subsequent re-conditioning. These results suggest reversible plasticity of fear memory-encoding circuits even after fear memory consolidation. This reversible plasticity of memory-encoding synapses may be involved in updating the contents of original memory even after memory consolidation.     

Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks

A large body of experimental and theoretical work on neural coding suggests that the information stored in brain circuits is represented by time-varying patterns of neural activity. Reservoir computing, where the activity of a recurrently connected pool of neurons is read by one or more units that provide an output response, successfully exploits this type of neural activity. However, the question of system robustness to small structural perturbations, such as failing neurons and synapses, has been largely overlooked. This contrasts with well-studied dynamical perturbations that lead to divergent network activity in the presence of chaos, as is the case for many reservoir networks. Here, we distinguish between two types of structural network perturbations, namely local (e.g., individual synaptic or neuronal failure) and global (e.g., network-wide fluctuations). Surprisingly, we show that while global perturbations have a limited impact on the ability of reservoir models to perform various tasks, local perturbations can produce drastic effects. To address this limitation, we introduce a new architecture where the reservoir is driven by a layer of oscillators that generate stable and repeatable trajectories. This model outperforms previous implementations while being resistant to relatively large local and global perturbations. This finding has implications for the design of reservoir models that capture the capacity of brain circuits to perform cognitively and behaviorally relevant tasks while remaining robust to various forms of perturbations. Further, our work proposes a novel role for neuronal oscillations found in cortical circuits, where they may serve as a collection of inputs from which a network can robustly generate complex dynamics and implement rich computations.     

A numerical simulation of neural fields on curved geometries

Despite the highly convoluted nature of the human brain, neural field models typically treat the cortex as a planar two-dimensional sheet of ne;urons. Here, we present an approach for solving neural field equations on surfaces more akin to the cortical geometries typically obtained from neuroimaging data. Our approach involves solving the integral form of the partial integro-differential equation directly using collocation techniques alongside efficient numerical procedures for determining geodesic distances between neural units. To illustrate our methods, we study localised activity patterns in a two-dimensional neural field equation posed on a periodic square domain, the curved surface of a torus, and the cortical surface of a rat brain, the latter of which is constructed using neuroimaging data. Our results are twofold: Firstly, we find that collocation techniques are able to replicate solutions obtained using more standard Fourier based methods on a flat, periodic domain, independent of the underlying mesh. This result is particularly significant given the highly irregular nature of the type of meshes derived from modern neuroimaging data. And secondly, by deploying efficient numerical schemes to compute geodesics, our approach is not only capable of modelling macroscopic pattern formation on realistic cortical geometries, but can also be extended to include cortical architectures of more physiological relevance. Importantly, such an approach provides a means by which to investigate the influence of cortical geometry upon the nucleation and propagation of spatially localised neural activity and beyond. It thus promises to provide model-based insights into disorders like epilepsy, or spreading depression, as well as healthy cognitive processes like working memory or attention.     

Spatiotemporal Imaging of Glutamate-Induced Biophotonic Activities and Transmission in Neural Circuits

The processing of neural information in neural circuits plays key roles in neural functions. Biophotons, also called ultra-weak photon emissions (UPE), may play potential roles in neural signal transmission, contributing to the understanding of the high functions of nervous system such as vision, learning and memory, cognition and consciousness. However, the experimental analysis of biophotonic activities (emissions) in neural circuits has been hampered due to technical limitations. Here by developing and optimizing an in vitro biophoton imaging method, we characterize the spatiotemporal biophotonic activities and transmission in mouse brain slices. We show that the long-lasting application of glutamate to coronal brain slices produces a gradual and significant increase of biophotonic activities and achieves the maximal effect within approximately 90 min, which then lasts for a relatively long time (>200 min). The initiation and/or maintenance of biophotonic activities by glutamate can be significantly blocked by oxygen and glucose deprivation, together with the application of a cytochrome c oxidase inhibitor (sodium azide), but only partly by an action potential inhibitor (TTX), an anesthetic (procaine), or the removal of intracellular and extracellular Ca²⁺. We also show that the detected biophotonic activities in the corpus callosum and thalamus in sagittal brain slices mostly originate from axons or axonal terminals of cortical projection neurons, and that the hyperphosphorylation of microtubule-associated protein tau leads to a significant decrease of biophotonic activities in these two areas. Furthermore, the application of glutamate in the hippocampal dentate gyrus results in increased biophotonic activities in its intrahippocampal projection areas. These results suggest that the glutamate-induced biophotonic activities reflect biophotonic transmission along the axons and in neural circuits, which may be a new mechanism for the processing of neural information.     

Septal networks: relevance to theta rhythm, epilepsy and Alzheimer's disease

Information processing and storing by brain networks requires a highly coordinated operation of multiple neuronal groups. The function of septal neurons is to modulate the activity of archicortical (e.g. hippocampal) and neocortical circuits. This modulation is necessary for the development and normal occurrence of rhythmical cortical activities that control the processing of sensory information and memory functions. Damage or degeneration of septal neurons results in abnormal information processing in cortical circuits and consequent brain dysfunction. Septal neurons not only provide the optimal levels of excitatory background to cortical structures, but they may also inhibit the occurrence of abnormal excitability states.     

Synaptic Scaling Enables Dynamically Distinct Short- and Long-Term Memory Formation

Memory storage in the brain relies on mechanisms acting on time scales from minutes, for long-term synaptic potentiation, to days, for memory consolidation. During such processes, neural circuits distinguish synapses relevant for forming a long-term storage, which are consolidated, from synapses of short-term storage, which fade. How time scale integration and synaptic differentiation is simultaneously achieved remains unclear. Here we show that synaptic scaling – a slow process usually associated with the maintenance of activity homeostasis – combined with synaptic plasticity may simultaneously achieve both, thereby providing a natural separation of short- from long-term storage. The interaction between plasticity and scaling provides also an explanation for an established paradox where memory consolidation critically depends on the exact order of learning and recall. These results indicate that scaling may be fundamental for stabilizing memories, providing a dynamic link between early and late memory formation processes.     

A zonal model of cortical functions

A model of cortical functions is developed with the object of simulating the observed behavior of individual neurons organized in unit circuits and functional systems of the cerebellum, the cerebrum and the hippocampal formation. The neuronal model is capable of representing refractory and potentiated states, as well as the firing and lowest resting states. The unit circuits of each system consist of all common types of cells with known synaptic connections. In the cerebral system these unit circuits are interconnected to form columns as well as zones. A new discrete neural network equation, which takes account of interactions with the extracellular field, is proposed to simulate electrical activity in these circuits. A coherent theory of cortical activity and functions is derived that accounts for many of the observed phenomena, including those associated with the development of long-term potentiation and sequential memory. Three appendices are devoted to the theory of extracellular interactions, the derivation of non-linear network equations, and a computer program to simulate learning in the cortex.     

Neuronal networks and memory: role of the hippocampus

Learning and memory are related both to cognitive processes and to neurobiological mechanisms. The human pathology focused on the role of the hippocampus and animal experiments have analyzed its implications. The most usually admitted hypothesis is that memories are underlied by distributed specific neural networks defined through the strengthening of certain synapses, under the action of the flow of information during learning. The best candidate for this strengthening of the synapses is a change in synaptic plasticity similar to the artificial phenomenon of long-term potentiation. During memory processes, the hippocampus would play a particular role in information processing (analyzing novelty and significance of the information) and would allow the specification of the neural network, mainly in the cortical territories. We report data in olfactory learning in rats comforting these hypotheses. Considering neurochemistry of memory processes, specific synaptic changes and neuromodulatory processes must be distinguished. We report data about vasopressin illustrating both kinds of mechanisms in the hippocampus.     

Identification of a Functional Connectome for Long-Term Fear Memory in Mice

Long-term memories are thought to depend upon the coordinated activation of a broad network of cortical and subcortical brain regions. However, the distributed nature of this representation has made it challenging to define the neural elements of the memory trace, and lesion and electrophysiological approaches provide only a narrow window into what is appreciated a much more global network. Here we used a global mapping approach to identify networks of brain regions activated following recall of long-term fear memories in mice. Analysis of Fos expression across 84 brain regions allowed us to identify regions that were co-active following memory recall. These analyses revealed that the functional organization of long-term fear memories depends on memory age and is altered in mutant mice that exhibit premature forgetting. Most importantly, these analyses indicate that long-term memory recall engages a network that has a distinct thalamic-hippocampal-cortical signature. This network is concurrently integrated and segregated and therefore has small-world properties, and contains hub-like regions in the prefrontal cortex and thalamus that may play privileged roles in memory expression.     

Deciphering the molecular basis of memory failure in Alzheimer's disease

Acutely developing lesions of the brain have been highly instructive in elucidating the neural systems underlying memory in humans and animal models. Much less has been learned from chronic neurodegenerative disorders that insidiously impair memory. But the advent of a detailed molecular hypothesis for the development of Alzheimer's disease and the creation of compelling mouse models thereof have begun to change this situation. Experiments in rodents suggest that soluble oligomers of the amyloid beta protein (Abeta) may discretely interfere with synaptic mechanisms mediating aspects of learning and memory, including long-term potentiation. In humans, memory impairment correlates strongly with cortical levels of soluble Abeta species, which include oligomers. Local inflammatory changes, neurofibrillary degeneration, and neurotransmitter deficits all contribute to memory impairment, but available evidence suggests that these develop as a consequence of early Abeta accumulation. Accordingly, attempts to slow memory and cognitive loss by decreasing cerebral Abeta levels have entered human trials.     

记忆水平依赖的海马-前额叶神经节律交互

海马(HPC)和前额叶皮层(PFC)的协同作用是记忆加工过程的关键,其相互作用对学习和记忆功能至关重要.大量证据表明,情景记忆的形成、巩固与检索依赖于特征神经节律在PFC和HPC脑区间的同步作用,这些节律包括theta节律、gamma节律和sharp wave ripples (SWRs)节律等.在精神类疾病中患者往往伴随出现学习记忆功能障碍,基于人类和动物的脑电研究均发现以上3种神经节律在HPC和PFC之间的同步性下降,可能作为反映精神病理下认知功能障碍的重要指标.本文从HPC-PFC网络中的神经节律研究出发,总结了theta节律、gamma节律和SWRs节律在两脑区间的协调交互模式在情景记忆中的作用,以及精神分裂症和抑郁症状态下HPC-PFC通路上神经节律的异常表现及其潜在损伤机制,为今后精神疾病的快速诊断提供客观依据.     

A resource of Cre driver lines for genetic targeting of GABAergic neurons in cerebral cortex

A key obstacle to understanding neural circuits in the?cerebral cortex is that of unraveling the diversity of GABAergic interneurons. This diversity poses general questions for neural circuit analysis: how are these interneuron cell types generated and assembled into stereotyped local circuits and how do they differentially contribute to circuit operations that underlie cortical functions ranging from perception to cognition? Using genetic engineering in mice, we have generated and characterized approximately 20 Cre and inducible CreER knockin driver lines that reliably target major classes and lineages of GABAergic neurons. More select populations are captured by intersection of Cre and Flp drivers. Genetic targeting allows reliable identification, monitoring, and manipulation of cortical GABAergic neurons, thereby enabling a systematic and comprehensive analysis from cell fate specification, migration, and connectivity, to their functions in network dynamics and behavior. As such, this approach will accelerate the study of GABAergic circuits throughout the mammalian brain.     

非侵入性脑刺激应用于创伤后应激障碍和物质滥用障碍的记忆干预

记忆是进行思维、想象等高级心理活动的基础，是累积经验、促进个体生存的重要功能。然而，创伤后应激障碍和物质滥用障碍具有某种非适应性记忆过强的特征，不利于个体生存。因此，以病理性改变的记忆为靶点，通过削弱或更新非适应性记忆，可以达到缓解症状甚至治愈的目的。记忆并非是对经验的刻板记录，而是对经验不断更新整合的过程，因此记忆有被干预的可能。记忆的再次激活可能会诱发记忆消退和再巩固，这为记忆相关精神疾病的干预提供了思路和启发。非侵入性脑刺激（noninvasive brain stimulation,NIBS）技术作为一种时间、空间分辨率较高的无创神经调控技术，近年来开始被结合运用到记忆干预研究中。不同刺激参数的NIBS （如频率、极性，以及受刺激区域的初始神经激活状态）应用于特定大脑皮质区域，可以调节神经可塑性，增强或降低靶点脑区的兴奋性，从而削弱或增强行为表现，实现记忆消退增强或在再巩固时间窗内干预记忆。本文首先介绍了记忆相关的脑功能基础研究与局部脑区干预方案的理论联系，继而回顾了近年来NIBS与记忆干预相结合应用于创伤或物质滥用相关障碍的临床干预研究，为精神疾病临床诊疗提供理论依据和启发。     

Neurogenesis in mouse models of Alzheimer's disease

The brains of the adult mouse and human possess neural stem cells (NSCs) that retain the capacity to generate new neurons through the process of neurogenesis. They share the same anatomical locations of stem cell niches in the brain, as well as the prominent feature of rostral migratory stream formed by neuroblasts migrating from the lateral ventricles towards the olfactory bulb. Therefore the mouse possesses some fundamental features that may qualify it as a relevant model for adult human neurogenesis. Adult born young hippocampal neurons in the mouse display the unique property of enhanced plasticity, and can integrate physically and functionally into existing neural circuits in the brain. Such crucial properties of neurogenesis may at least partially underlie the improved learning and memory functions observed in the mouse when hippocampal neurogenesis is augmented, leading to the suggestion that neurogenesis induction may be a novel therapeutic approach for diseases with cognitive impairments such as Alzheimer's disease (AD). Research towards this goal has benefited significantly from the use of AD mouse models to facilitate the understanding in the impact of AD pathology on neurogenesis. The present article reviews the growing body of controversial data on altered neurogenesis in mouse models of AD and attempts to assess their relative relevance to humans.     

Towards understanding of the cortical network underlying associative memory

Declarative knowledge and experiences are represented in the association cortex and are recalled by reactivation of the neural representation. Electrophysiological experiments have revealed that associations between semantically linked visual objects are formed in neural representations in the temporal and limbic cortices. Memory traces are created by the reorganization of neural circuits. These regions are reactivated during retrieval and contribute to the contents of a memory. Two different types of retrieval signals are suggested as follows: automatic and active. One flows backward from the medial temporal lobe during the automatic retrieval process, whereas the other is conveyed as a top-down signal from the prefrontal cortex to the temporal cortex during the active retrieval process. By sending the top-down signal, the prefrontal cortex manipulates and organizes to-be-remembered information, devises strategies for retrieval and monitors the outcome. To further understand the neural mechanism of memory, the following two complementary views are needed: how the multiple cortical areas in the brain-wide network interact to orchestrate cognitive functions and how the properties of single neurons and their synaptic connections with neighbouring neurons combine to form local circuits and to exhibit the function of each cortical area. We will discuss some new methodological innovations that tackle these challenges.     

Spatial organization and genetic information in brain development

In the course of brain development neurons acquire qualitative and quantitative biochemical and morphological properties which depend on the position of the cells within the nervous system. In the dimensions tangential to multilayered cell sheets mechanisms contributing to spatial order include induction by adjacent tissue as well as internal generation of morphogenetic fields (presumably by reactions involving autocatalysis and lateral inhibition). In the dimensions across the sheet cells of different types are produced in one layer and sort into another layer, guided presumably by contact mediated cell interaction. Positional and directional cues encoded in the developing brain are essentially involved in axonal guidance and the formation of neuronal connections. In mammals and man, the number of neurons and their connections in the brain is much higher than the number of genes. This is possible because there are repetitive neuronal circuits in the brain, and there is topographic order of connections between different brain areas. For instance, few quantitatively graded markers would suffice for specifying the projection of one area of the nervous system onto another, generating spatial order for a large number of fibers while requiring only a limited amount of genetic information. Higher brain functions, such as learning and memory, may logically require only a neural network consisting of repetitive subunits. On the other hand, it is an evolutionary advantage for an organism to be endowed, from the outset, with a pattern of neural connections which is subtly and quantitatively tuned for efficiency in dealing with the environment, while remaining flexible for change and adaptation in the course of learning. Estimates are given suggesting that a considerable part of the genetic information in DNA may be invested for such quantitative specification of connections within the brain.     

Vision First? The Development of Primary Visual Cortical Networks Is More Rapid Than the Development of Primary Motor Networks in Humans

The development of cortical functions and the capacity of the mature brain to learn are largely determined by the establishment and maintenance of neocortical networks. Here we address the human development of long-range connectivity in primary visual and motor cortices, using well-established behavioral measures--a Contour Integration test and a Finger-tapping task--that have been shown to be related to these specific primary areas, and the long-range neural connectivity within those. Possible confounding factors, such as different task requirements (complexity, cognitive load) are eliminated by using these tasks in a learning paradigm. We find that there is a temporal lag between the developmental timing of primary sensory vs. motor areas with an advantage of visual development; we also confirm that human development is very slow in both cases, and that there is a retained capacity for practice induced plastic changes in adults. This pattern of results seems to point to human-specific development of the "canonical circuits" of primary sensory and motor cortices, probably reflecting the ecological requirements of human life.     

Molecules to remember

"Working memory" is used for the transient storage of information in the brain. In this issue of Cell, Wang et al. (2007) now reveal how a series of molecular events involving alpha2A-adrenoceptors and a class of ion channels gated by cAMP tune the responses of neural circuits that function in working memory in mammals.     

Integrated mechanisms of anticipation and rate-of-change computations in cortical circuits

Local neocortical circuits are characterized by stereotypical physiological and structural features that subserve generic computational operations. These basic computations of the cortical microcircuit emerge through the interplay of neuronal connectivity, cellular intrinsic properties, and synaptic plasticity dynamics. How these interacting mechanisms generate specific computational operations in the cortical circuit remains largely unknown. Here, we identify the neurophysiological basis of both the rate of change and anticipation computations on synaptic inputs in a cortical circuit. Through biophysically realistic computer simulations and neuronal recordings, we show that the rate-of-change computation is operated robustly in cortical networks through the combination of two ubiquitous brain mechanisms: short-term synaptic depression and spike-frequency adaptation. We then show how this rate-of-change circuit can be embedded in a convergently connected network to anticipate temporally incoming synaptic inputs, in quantitative agreement with experimental findings on anticipatory responses to moving stimuli in the primary visual cortex. Given the robustness of the mechanism and the widespread nature of the physiological machinery involved, we suggest that rate-of-change computation and temporal anticipation are principal, hard-wired functions of neural information processing in the cortical microcircuit.