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\"In 1934 Wilder Penfield's vision of an establishment dedicated to the relief of sickness and pain and the study of neurology lead to the creation of the Montreal Neurological Institute. Setting the standard for neurological research and care for patients disabled by neurological illnesses, Penfield's institute became a beacon of light in a largely unexplored field of medicine. The Wounded Brain Healed describes the pioneering research that took place during the MNI's first fifty years. During the institute's golden age, Penfield and his colleagues designed the EEG test for the study of epileptic patients, discovered some of the causes of epilepsy, and developed new treatments that have since been adopted worldwide. Additionally, they delineated the sensory and motor representation in the cerebral cortex and localized the major areas of the brain related to speech. The institute also boasts the discoveries of two types of memory--one serving immediate recall, the other long term--as well as the discovery of the localization of short-term memory to the inner structures of the temporal lobe. Physicians and scientists who trained at the MNI went on to establish renowned neurology and neurosurgery departments throughout Canada, the United States, Europe, Asia, and Latin America. Recounting the story of one of Canada's greatest contributions to international medical science through archival research, personal interviews, photographs, illustrations, and paintings, The Wounded Brain Healed provides fascinating insight into the institution that had a global and lasting impact.\"-- Provided by publisher.

The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of \"kernel analysis\" that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.

The dentate gyrus (DG) has a key role in hippocampal memory formation. Intriguingly, DG lesions impair many, but not all, hippocampus-dependent mnemonic functions, indicating that the rest of the hippocampus (CA1–CA3) can operate autonomously under certain conditions. An extensive body of theoretical work has proposed how the architectural elements and various cell types of the DG may underlie its function in cognition. Recent studies recorded and manipulated the activity of different neuron types in the DG during memory tasks and have provided exciting new insights into the mechanisms of DG computational processes, particularly for the encoding, retrieval and discrimination of similar memories. Here, we review these DG-dependent mnemonic functions in light of the new findings and explore mechanistic links between the cellular and network properties of, and the computations performed by, the DG.The dentate gyrus has an important role in memory formation in the hippocampus. In this Review, Thomas Hainmueller and Marlene Bartos examine the cells and circuits of the dentate gyrus, and discuss the evidence indicating that this brain region has multiple mnemonic functions.

Key Points At many physiological and anatomical levels in the brain, the distribution of numerous parameters is strongly skewed with a heavy tail and typically follows a lognormal distribution. The power and frequency relationship of brain oscillations is typically expressed in a log scale. Network synchrony, measured as a fraction of spiking neurons in a given time window, shows lognormal distribution in all brain states. Firing rates, spike bursts and synaptic weights follow a lognormal distribution. Importantly, these parameters remain correlated across brain states, environments and situations. The log-dynamic patterns of networks may be supported by the lognormal distribution of corticocortical connections strengths and axon diameters. A preconfigured, strongly connected minority of fast-firing neurons form the backbone of brain connectivity and serve as an ever-ready, fast-acting system. However, full performance of the brain also depends on the activity of very large numbers of weakly connected and slow-firing majority of neurons. Many physiological and anatomical parameters in the brain have a skewed distribution. Buzsáki and Mizuseki propose that this reflects a fundamental aspect of brain organization — namely, a network in which a minority of neurons does most of the work all of the time. We often assume that the variables of functional and structural brain parameters — such as synaptic weights, the firing rates of individual neurons, the synchronous discharge of neural populations, the number of synaptic contacts between neurons and the size of dendritic boutons — have a bell-shaped distribution. However, at many physiological and anatomical levels in the brain, the distribution of numerous parameters is in fact strongly skewed with a heavy tail, suggesting that skewed (typically lognormal) distributions are fundamental to structural and functional brain organization. This insight not only has implications for how we should collect and analyse data, it may also help us to understand how the different levels of skewed distributions — from synapses to cognition — are related to each other.

There is growing evidence of the contribution of microtubule dynamics to dendritic spine changes, synaptic plasticity, axonal transportation, and cell polarity. Besides, one of the well-studied effects of Cannabis on human behavior is memory disability. As [DELA].sup.9-tetrahydrocannabinol ([DELA].sup.9-THC) is the most pivotal chemical of Cannabis, we investigated the effect of [DELA].sup.9-THC on microtubule dynamicity and the structural study of tubulin (microtubule monomer). Our results show that [DELA].sup.9-THC changes microtubule dynamicity compared to the control group. The turbidity assay results demonstrated that [DELA].sup.9-THC reduces microtubule polymerization in a concentration-dependent manner. Circular Dichroism spectroscopy also studied the structural changes of the purified tubulin, which revealed significant changes in the secondary structure of the tubulin. Furthermore, Silico studies predicted one binding site for [DELA].sup.9-THC on [beta]-tubulin. We concluded that [DELA].sup.9-THC could reduce the microtubule's stability, which may conversely affect brain function by microtubule dynamic changes caused by secondary structural changes of tubulin and preventing tubulin-tubulin interaction.

Our sense of touch emerges from an array of mechanosensory structures residing within the fabric of our skin. These tactile end organ structures convert innocuous forces acting on the skin into electrical signals that propagate to the CNS via the axons of low-threshold mechanoreceptors (LTMRs). Our rich capacity for tactile discrimination arises from the dissimilar intrinsic properties of the LTMR subtypes that innervate different regions of the skin and the structurally distinct end organ complexes with which they associate. These end organ structures comprise a range of non-neuronal cell types, which may themselves actively contribute to the transformation of tactile forces into neural impulses within the LTMR afferents. Although the mechanism and the site of transduction across end organs remain unclear, PIEZO2 has emerged as the principal mechanosensitive channel involved in light touch of the skin. Here we review the physiological properties of LTMR subtypes and discuss how features of their cutaneous end organ complexes shape subtype-specific tuning.Mammalian skin contains an array of specialized structures that transform mechanical forces into electrical signals. Handler and Ginty provide a comprehensive overview of the features of the skin’s mechanosensory end organs and the neurons with which they associate and consider how their diverse properties contribute to the sense of touch.

The default mode network (DMN) is classically considered an ‘intrinsic’ system, specializing in internally oriented cognitive processes such as daydreaming, reminiscing and future planning. In this Perspective, we suggest that the DMN is an active and dynamic ‘sense-making’ network that integrates incoming extrinsic information with prior intrinsic information to form rich, context-dependent models of situations as they unfold over time. We review studies that relied on naturalistic stimuli, such as stories and movies, to demonstrate how an individual’s DMN neural responses are influenced both by external information accumulated as events unfold over time and by the individual’s idiosyncratic past memories and knowledge. The integration of extrinsic and intrinsic information over long timescales provides a space for negotiating a shared neural code, which is necessary for establishing shared meaning, shared communication tools, shared narratives and, above all, shared communities and social networks.The role of the default mode network (DMN) is unclear. In this Perspective, Yeshurun, Nguyen and Hasson review evidence that the DMN integrates extrinsic inputs with intrinsic information over long timescales, enabling it to represent meaning in a way that can be shared between individuals.

The author reviews network models of the brain, including models of both structural and functional connectivity. He discusses contributions of network models to cognitive neuroscience, as well as limitations and challenges associated with constructing and interpreting these models. The confluence of new approaches in recording patterns of brain connectivity and quantitative analytic tools from network science has opened new avenues toward understanding the organization and function of brain networks. Descriptive network models of brain structural and functional connectivity have made several important contributions; for example, in the mapping of putative network hubs and network communities. Building on the importance of anatomical and functional interactions, network models have provided insight into the basic structures and mechanisms that enable integrative neural processes. Network models have also been instrumental in understanding the role of structural brain networks in generating spatially and temporally organized brain activity. Despite these contributions, network models are subject to limitations in methodology and interpretation, and they face many challenges as brain connectivity data sets continue to increase in detail and complexity.

Dysregulation of the immune system is a cardinal feature of Alzheimer disease (AD), and a considerable body of evidence indicates pathological alterations in central and peripheral immune responses that change over time. Considering AD as a systemic immune process raises important questions about how communication between the peripheral and central compartments occurs and whether this crosstalk represents a therapeutic target. We established a whitepaper workgroup to delineate the current status of the field and to outline a research prospectus for advancing our understanding of peripheral–central immune crosstalk in AD. To guide the prospectus, we begin with an overview of seminal clinical observations that suggest a role for peripheral immune dysregulation and peripheral–central immune communication in AD, followed by formative animal data that provide insights into possible mechanisms for these clinical findings. We then present a roadmap that defines important next steps needed to overcome conceptual and methodological challenges, opportunities for future interdisciplinary research, and suggestions for translating promising mechanistic studies into therapeutic interventions.Evidence is accumulating that both central and peripheral immune responses are dysregulated in Alzheimer disease (AD). This roadmap reviews the current status of this research and provides a new research prospectus to advance our understanding of peripheral–central immune crosstalk in AD.

A similar neural ensemble participates in the encoding of two distinct memories, resulting in the recall of one memory increasing the likelihood of recalling the other, but only if those memories occur very closely in time—within a day rather than across a week. Linkage of separate memories across time This paper tests and provides support for the emerging hypothesis that two distinct memories formed close in time may be linked, such that recall of one triggers recall of the other. Using a range of techniques including in vivo calcium imaging with miniature head-mounted fluorescent microscopes in freely behaving mice, Alcino Silva and colleagues show that learning-dependent changes in excitability can temporally and contextually link memories formed close in time. Interestingly the overlap between memory encoding ensembles and strengthening of the second memory within short periods of time do not occur in aged animals, which do not exhibit the increased hippocampal excitability necessary for such links to occur. Recent studies suggest that a shared neural ensemble may link distinct memories encoded close in time 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 . According to the memory allocation hypothesis 1 , 2 , learning triggers a temporary increase in neuronal excitability 13 , 14 , 15 that biases the representation of a subsequent memory to the neuronal ensemble encoding the first memory, such that recall of one memory increases the likelihood of recalling the other memory. Here we show in mice that the overlap between the hippocampal CA1 ensembles activated by two distinct contexts acquired within a day is higher than when they are separated by a week. Several findings indicate that this overlap of neuronal ensembles links two contextual memories. First, fear paired with one context is transferred to a neutral context when the two contexts are acquired within a day but not across a week. Second, the first memory strengthens the second memory within a day but not across a week. Older mice, known to have lower CA1 excitability 15 , 16 , do not show the overlap between ensembles, the transfer of fear between contexts, or the strengthening of the second memory. Finally, in aged mice, increasing cellular excitability and activating a common ensemble of CA1 neurons during two distinct context exposures rescued the deficit in linking memories. Taken together, these findings demonstrate that contextual memories encoded close in time are linked by directing storage into overlapping ensembles. Alteration of these processes by ageing could affect the temporal structure of memories, thus impairing efficient recall of related information.