Inside the brain of a neuron

Impact of microchannel width on axons for brain-on-chip applications

Technologies for axon guidance for in vitro disease models and bottom up investigations are increasingly being used in neuroscience research. One of the most prevalent patterning methods is using polydimethylsiloxane (PDMS) microstructures due to compatibility with microscopy and electrophysiology which enables systematic tracking of axon development with precision and efficiency. Previous investigations of these guidance platforms have noted axons tend to follow edges and avoid sharp turns; however, the specific impact of spatial constraints remains only partially explored. We investigated the influence of microchannel width beyond a constriction point, as well as the number of available microchannels, on axon growth dynamics. Further, by manipulating the size of micron/submicron-sized PDMS tunnels we investigated the space restriction that prevents growth cone penetration showing that restrictions smaller than 350 nm were sufficient to exclude axons. This research offers insights into the interplay of spatial constraints, axon development, and neural behavior. The findings are important for designing in vitro platforms and in vivo neural interfaces for both fundamental neuroscience and translational applications in rapidly evolving neural implant technologies.

Efficient fabrication of 3D bioprinted functional sensory neurons using an inducible Neurogenin-2 human pluripotent stem cell line

Three-dimensional (3D) tissue models have gained recognition for their improved ability to mimic the native cell microenvironment compared to traditional two-dimensional models. This progress has been driven by advances in tissue-engineering technologies such as 3D bioprinting, a promising method for fabricating biomimetic living tissues. While bioprinting has succeeded in generating various tissues to date, creating neural tissue models remains challenging. In this context, we present an accelerated approach to fabricate 3D sensory neuron (SN) structures using a transgenic human pluripotent stem cell (hPSC)-line that contains an inducible Neurogenin-2 (NGN2) expression cassette. The NGN2 hPSC line was first differentiated to neural crest cell (NCC) progenitors, then incorporated into a cytocompatible gelatin methacryloyl-based bioink for 3D bioprinting. Upregulated NGN2 expression in the bioprinted NCCs resulted in induced SN (iSN) populations that exhibited specific cell markers, with 3D analysis revealing widespread neurite outgrowth through the scaffold volume. Calcium imaging demonstrated functional activity of iSNs, including membrane excitability properties and voltage-gated sodium channel (NaV) activity. This efficient approach to generate 3D bioprinted iSN structures streamlines the development of neural tissue models, useful for the study of neurodevelopment and disease states and offering translational potential.

Synapses tagged, memories kept: synaptic tagging and capture hypothesis in brain health and disease

The synaptic tagging and capture (STC) hypothesis lays the framework on the synapse-specific mechanism of protein synthesis-dependent long-term plasticity upon synaptic induction. Activated synapses will display a transient tag that will capture plasticity-related products (PRPs). These two events, tag setting and PRP synthesis, can be teased apart and have been studied extensively-from their electrophysiological and pharmacological properties to the molecular events involved. Consequently, the hypothesis also permits interactions of synaptic populations that encode different memories within the same neuronal population-hence, it gives rise to the associativity of plasticity. In this review, the recent advances and progress since the experimental debut of the STC hypothesis will be shared. This includes the role of neuromodulation in PRP synthesis and tag integrity, behavioural correlates of the hypothesis and modelling in silico. STC, as a more sensitive assay for synaptic health, can also assess neuronal aberrations. We will also expound how synaptic plasticity and associativity are altered in ageing-related decline and pathological conditions such as juvenile stress, cancer, sleep deprivation and Alzheimer's disease. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

Synthetic biological neural networks: From current implementations to future perspectives

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning.

An Insight into Cellular and Molecular Mechanisms Underlying the Pathogenesis of Neurodegeneration in Alzheimer's Disease

Alzheimer's disease (AD) is the most prominent neurodegenerative disorder in the aging population. It is characterized by cognitive decline, gradual neurodegeneration, and the development of amyloid-β (Aβ)-plaques and neurofibrillary tangles, which constitute hyperphosphorylated tau. The early stages of neurodegeneration in AD include the loss of neurons, followed by synaptic impairment. Since the discovery of AD, substantial factual research has surfaced that outlines the disease's causes, molecular mechanisms, and prospective therapeutics, but a successful cure for the disease has not yet been discovered. This may be attributed to the complicated pathogenesis of AD, the absence of a well-defined molecular mechanism, and the constrained diagnostic resources and treatment options. To address the aforementioned challenges, extensive disease modeling is essential to fully comprehend the underlying mechanisms of AD, making it easier to design and develop effective treatment strategies. Emerging evidence over the past few decades supports the critical role of Aβ and tau in AD pathogenesis and the participation of glial cells in different molecular and cellular pathways. This review extensively discusses the current understanding concerning Aβ- and tau-associated molecular mechanisms and glial dysfunction in AD. Moreover, the critical risk factors associated with AD including genetics, aging, environmental variables, lifestyle habits, medical conditions, viral/bacterial infections, and psychiatric factors have been summarized. The present study will entice researchers to more thoroughly comprehend and explore the current status of the molecular mechanism of AD, which may assist in AD drug development in the forthcoming era.

Designing of gradient scaffolds and their applications in tissue regeneration

Gradient scaffolds are isotropic/anisotropic three-dimensional structures with gradual transitions in geometry, density, porosity, stiffness, etc., that mimic the biological extracellular matrix. The gradient structures in biological tissues play a major role in various functional and metabolic activities in the body. The designing of gradients in the scaffold can overcome the current challenges in the clinic compared to conventional scaffolds by exhibiting excellent penetration capacity for nutrients & cells, increased cellular adhesion, cell viability & differentiation, improved mechanical stability, and biocompatibility. In this review, the recent advancements in designing gradient scaffolds with desired biomimetic properties, and their implication in tissue regeneration applications have been briefly explained. Furthermore, the gradients in native tissues such as bone, cartilage, neuron, cardiovascular, skin and their specific utility in tissue regeneration have been discussed in detail. The insights from such advances using gradient-based scaffolds can widen the horizon for using gradient biomaterials in tissue regeneration applications.

Microenvironment and the progress of immunotherapy in clinical practice of NSCLC brain metastasis

One of the most frequent distant metastases of lung cancer occurs in the brain. The average natural survival duration for patients with lung cancer who have brain metastases is about 1 to 2 months. Knowledge about brain metastases is currently restricted since they are more difficult to acquire than other metastases. This review begins with an analysis of the immune microenvironment of brain metastases; focuses primarily on the functions of microglia, astrocytes, neurons, and tumor-infiltrating lymphocytes in the microenvironment of brain metastases; and offers an atlas of the immune microenvironment of brain metastases involving significant cells. In an effort to give researchers new research ideas, the study also briefly covers how immunotherapy for non-small cell lung cancer with brain metastases is currently faring.

Improving a cortical pyramidal neuron model's classification performance on a real-world ecg dataset by extending inputs

Pyramidal neurons display a variety of active conductivities and complex morphologies that support nonlinear dendritic computation. Given growing interest in understanding the ability of pyramidal neurons to classify real-world data, in our study we applied both a detailed pyramidal neuron model and the perceptron learning algorithm to classify real-world ECG data. We used Gray coding to generate spike patterns from ECG signals as well as investigated the classification performance of the pyramidal neuron's subcellular regions. Compared with the equivalent single-layer perceptron, the pyramidal neuron performed poorly due to a weight constraint. A proposed mirroring approach for inputs, however, significantly boosted the classification performance of the neuron. We thus conclude that pyramidal neurons can classify real-world data and that the mirroring approach affects performance in a way similar to non-constrained learning.

In Times of Adversity: A Neuroscience Perspective on Stress, Health, and Implications for Society Post-pandemic

The relationship between chronic stress and chronic disease (including mental illness) is well established: HPA-axis hyperactivity leads to hormonal dysregulation of primary mediators (eg, glucocorticoids, cytokines, etc.), allostatic overload, and neurological degradation, followed by clinical manifestations of disease. Amid the largest public health crisis of the century lay a myriad of challenges pushing people beyond their limit. From experiencing loss of connection or dealing with loss of life to financial shocks of COVID-19 lockdowns or infection by the SARS-CoV-2 virus, stress is at an all-time high, threatening both brain and mental health at scale. Fortunately, there is a way forward: the neuroscience of resilience teaches us that it is possible to resist, recover, and redirect the brain from trauma to re-establish balance in the body and improve well-being. At the same time, health follows a social gradient: adverse and protective psychosocial factors are shaped by wider social and economic determinants of health. This paper argues the neurobiology of stress is not separate from health disparities linked to adverse factors (ie, stress) created by complex social and economic contexts. Therefore, the field of neuroscience is challenged to inform multi-context and multi-level approaches and engage with decision-makers to enact policies and interventions aimed at promoting the resilient element in a wider population health context. Undoubtedly, achieving such a goal for current and future generations to benefit and lead healthier lives requires a heroic effort from all key stakeholders. The cost of willful neglect to resolve these issues is too expensive.

Exploring polymeric biomaterials in developing neural prostheses

Neuroprosthetics, with a range of applications such as cognitive, auditory, pain relief, recording, motor, and visual prosthetics have emerged as a promising field in recent years. However, poor electrical conductivity, a high disparity between tissue and interfaces and the onset of reactive gliosis post-implantation remains major challenges in the development of neuroprostheses. The choice of biomaterials in designing the neural interfaces’ in neuroprosthetic applications is of high importance, as the overall sustained performance of neuroprosthetic devices is based on the features of materials used for the neural interfaces. Numerous biomaterials, such as metals and carbon-based materials, have been used in neuroprosthetics thus far. Nonetheless, neuroprosthetics made from polymeric biomaterials are in high demand due to their high biocompatibility, conductivity, and biostability. Furthermore, polymeric biomaterials can be used as a hybrid design to overcome the limitations of other co-biomaterials. This article makes an attempt to review the polymeric biomaterials involved in this cutting-edge technology utilized for different purposes such as substrates, coatings, and miniaturization of electrodes, that might help in enriching our understanding on neuroprosthetics.

Drosophila Dendritic Arborisation Neurons: Fantastic Actin Dynamics and Where to Find Them

Neuronal dendrites receive, integrate, and process numerous inputs and therefore serve as the neuron's "antennae". Dendrites display extreme morphological diversity across different neuronal classes to match the neuron's specific functional requirements. Understanding how this structural diversity is specified is therefore important for shedding light on information processing in the healthy and diseased nervous system. Popular models for in vivo studies of dendrite differentiation are the four classes of dendritic arborization (c1da-c4da) neurons of Drosophila larvae with their class-specific dendritic morphologies. Using da neurons, a combination of live-cell imaging and computational approaches have delivered information on the distinct phases and the time course of dendrite development from embryonic stages to the fully developed dendritic tree. With these data, we can start approaching the basic logic behind differential dendrite development. A major role in the definition of neuron-type specific morphologies is played by dynamic actin-rich processes and the regulation of their properties. This review presents the differences in the growth programs leading to morphologically different dendritic trees, with a focus on the key role of actin modulatory proteins. In addition, we summarize requirements and technological progress towards the visualization and manipulation of such actin regulators in vivo.

Optically superior fluorescent probes for selective imaging of cells, tumors, and reactive chemical species

Fluorescent chemical probes have become powerful tools to study biological events in living cells. They provide a great opportunity to quantitatively and qualitatively analyze the physiological and biochemical properties of living cells in real time. The ability of researchers to manipulate these probes for a desired specific purpose has turned many heads in the scientific community. Despite a slow start, fluorescent probe research has seen exponential growth over the last decade in the world. This change required some adventurous and creative scientists from different fields-like biology, medicine, and chemistry-to come together to facilitate the constant expansion of this field. This review article introduces some fundamental concepts related to fluorescent probe designing and development. It also summarizes various fluorescent probes with superior optical properties used in fields like cell biology, cellular imaging, medical research, and cancer diagnosis. It is hoped that this article will encourage more young and creative scientists to contribute their talents to this field.

Dynamic input-dependent encoding of individual basal ganglia neurons

Computational models are crucial to studying the encoding of individual neurons. Static models are composed of a fixed set of parameters, thus resulting in static encoding properties that do not change under different inputs. Here, we challenge this basic concept which underlies these models. Using generalized linear models, we quantify the encoding and information processing properties of basal ganglia neurons recorded in-vitro. These properties are highly sensitive to the internal state of the neuron due to factors such as dependency on the baseline firing rate. Verification of these experimental results with simulations provides insights into the mechanisms underlying this input-dependent encoding. Thus, static models, which are not context dependent, represent only part of the neuronal encoding capabilities, and are not sufficient to represent the dynamics of a neuron over varying inputs. Input-dependent encoding is crucial for expanding our understanding of neuronal behavior in health and disease and underscores the need for a new generation of dynamic neuronal models.

Millisecond two-photon optical ribbon imaging for small-animal functional connectome study

We developed a high-speed two-photon optical ribbon imaging system, which combines galvo-mirrors for an arbitrary curve scan on a lateral plane and a tunable acoustic gradient-index lens for a 100 kHz-1 MHz axial scan. The system provides micrometer/millisecond spatiotemporal resolutions, which enable continuous readout of functional dynamics from small and densely packed neurons in a living adult Drosophila brain. Compared to sparse sampling techniques, the ribbon imaging modality avoids motion artifacts. Combined with a Drosophila anatomical connectome database, which is the most complete among all model animals, this technique paves the way toward establishing whole-brain functional connectome.

Achieving Neuroplasticity in Artificial Neural Networks through Smart Cities

Through the Internet of things (IoT), as promoted by smart cities, there is an emergence of big data accentuating the use of artificial intelligence through various components of urban planning, management, and design. One such system is that of artificial neural networks (ANNs), a component of machine learning that boasts similitude with brain neurological networks and its functioning. However, the development of ANN was done in singular fashion, whereby processes are rendered in sequence in a unidimensional perspective, contrasting with the functions of the brain to which ANN boasts similitude, and in particular to the concept of neuroplasticity which encourages unique complex interactions in self-learning fashion, thereby encouraging more inclusive urban processes and render urban coherence. This paper takes inspiration from Christopher Alexander’s Nature of Order and dwells in the concept of complexity theory; it also proposes a theoretical model of how ANN can be rendered with the same plastic properties as brain neurological networks with multidimensional interactivity in the context of smart cities through the use of big data and its emerging complex networks. By doing so, this model caters to the creation of stronger, richer, and more complex patterns that support Alexander’s concept of “wholeness” through the connection of overlapping networks. This paper is aimed toward engineers with interdisciplinary interest looking at creating more complex and intricate ANN models, and toward urban planners and urban theorists working on the emerging contemporary concept of smart cities.

Controlling Complexity of Cerebral Cortex Simulations-I: CxSystem, a Flexible Cortical Simulation Framework

Simulation of the cerebral cortex requires a combination of extensive domain-specific knowledge and efficient software. However, when the complexity of the biological system is combined with that of the software, the likelihood of coding errors increases, which slows model adjustments. Moreover, few life scientists are familiar with software engineering and would benefit from simplicity in form of a high-level abstraction of the biological model. Our primary aim was to build a scalable cortical simulation framework for personal computers. We isolated an adjustable part of the domain-specific knowledge from the software. Next, we designed a framework that reads the model parameters from comma-separated value files and creates the necessary code for Brian2 model simulation. This separation allows rapid exploration of complex cortical circuits while decreasing the likelihood of coding errors and automatically using efficient hardware devices. Next, we tested the system on a simplified version of the neocortical microcircuit proposed by Markram and colleagues ( 2015 ). Our results indicate that the framework can efficiently perform simulations using Python, C <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>+</mml:mo><mml:mo>+</mml:mo></mml:mrow></mml:math> , and GPU devices. The most efficient device varied with computer hardware and the duration and scale of the simulated system. The speed of Brian2 was retained despite an overlying layer of software. However, the Python and C <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>+</mml:mo><mml:mo>+</mml:mo></mml:mrow></mml:math> devices inherited the single core limitation of Brian2. The CxSystem framework supports exploration of complex models on personal computers and thus has the potential to facilitate research on cortical networks and systems.

Development of Microplatforms to Mimic the In Vivo Architecture of CNS and PNS Physiology and Their Diseases

Understanding the mechanisms that govern nervous tissues function remains a challenge. In vitro two-dimensional (2D) cell culture systems provide a simplistic platform to evaluate systematic investigations but often result in unreliable responses that cannot be translated to pathophysiological settings. Recently, microplatforms have emerged to provide a better approximation of the in vivo scenario with better control over the microenvironment, stimuli and structure. Advances in biomaterials enable the construction of three-dimensional (3D) scaffolds, which combined with microfabrication, allow enhanced biomimicry through precise control of the architecture, cell positioning, fluid flows and electrochemical stimuli. This manuscript reviews, compares and contrasts advances in nervous tissues-on-a-chip models and their applications in neural physiology and disease. Microplatforms used for neuro-glia interactions, neuromuscular junctions (NMJs), blood-brain barrier (BBB) and studies on brain cancer, metastasis and neurodegenerative diseases are addressed. Finally, we highlight challenges that can be addressed with interdisciplinary efforts to achieve a higher degree of biomimicry. Nervous tissue microplatforms provide a powerful tool that is destined to provide a better understanding of neural health and disease.

Immunolocalization of muscarinic M1 receptor in the rat medial prefrontal cortex

The medial prefrontal cortex (mPFC) has been considered to participate in many higher cognitive functions, such as memory formation and spatial navigation. These cognitive functions are modulated by cholinergic afferents via muscarinic acetylcholine receptors. Previous pharmacological studies have strongly suggested that the M1 receptor (M1R) is the most important subtype among muscarinic receptors to perform these cognitive functions. Actually, M1R is abundant in mPFC. However, the proportion of somata containing M1R among cortical cellular types, and the precise intracellular localization of M1R remain unclear. In this study, to clarify the precise immunolocalization of M1R in rat mPFC, we examined three major cellular types, pyramidal neurons, inhibitory neurons, and astrocytes. M1R immunopositivity signals were found in the majority of the somata of both pyramidal neurons and inhibitory neurons. In pyramidal neurons, strong M1R immunopositivity signals were usually found throughout their somata and dendrites including spines. On the other hand, the signal strength of M1R immunopositivity in the somata of inhibitory neurons significantly varied. Some neurons showed strong signals. Whereas about 40% of GAD67‐immunopositive neurons and 30% of parvalbumin‐immunopositive neurons (PV neurons) showed only weak signals. In PV neurons, M1R immunopositivity signals were preferentially distributed in somata. Furthermore, we found that many astrocytes showed substantial M1R immunopositivity signals. These signals were also mainly distributed in their somata. Thus, the distribution pattern of M1R markedly differs between cellular types. This difference might underlie the cholinergic modulation of higher cognitive functions subserved by mPFC.

Pattern separation in the hippocampus through the eyes of computational modeling

Pattern separation is a mnemonic process that has been extensively studied over the years. It entails the ability -of primarily hippocampal circuits- to distinguish between highly similar inputs, via generating different neuronal activity (output) patterns. The dentate gyrus (DG) in particular has long been hypothesized to implement pattern separation by detecting and storing similar inputs as distinct representations. The ways in which these distinct representations can be generated have been explored in a number of theoretical and computational modeling studies. Here, we review two categories of pattern separation models: those that address the phenomenon in an abstract mathematical fashion and those that delve into the underlying biological mechanisms by taking into account the anatomy and/or physiology of hippocampal circuits. We summarize the strategies, findings and limitations of these modeling approaches in the light of new experimental findings and propose a unifying framework whereby different network, cellular and sub-cellular mechanisms converge to a common goal: controlling sparsity, the key determinant of pattern separation in the DG.

Adult-born dentate granule cells show a critical period of dendritic reorganization and are distinct from developmentally born cells

Adult-born dentate granule cells (abGCs) exhibit a critical developmental phase during function integration. The time window of this phase is debated and whether abGCs become indistinguishable from developmentally born mature granule cells (mGCs) is uncertain. We analyzed complete dendritic reconstructions from abGCs and mGCs using viral labeling. AbGCs from 21-77 days post intrahippocampal injection (dpi) exhibited comparable dendritic arbors, suggesting that structural maturation precedes functional integration. In contrast, significant structural differences were found compared to mGCs: AbGCs had more curved dendrites, more short terminal segments, a different branching pattern, and more proximal terminal branches. Morphological modeling attributed these differences to developmental dendritic pruning and postnatal growth of the dentate gyrus. We further correlated GC morphologies with the responsiveness to unilateral medial perforant path stimulation using the immediate-early gene Arc as a marker of synaptic activation. Only abGCs at 28 and 35 dpi but neither old abGCs nor mGCs responded to stimulation with a remodeling of their dendritic arbor. Summarized, abGCs stay distinct from mGCs and their dendritic arbor can be shaped by afferent activity during a narrow critical time window.

Semantic processing of English sentences using statistical computation based on neurophysiological models

Computer programs that can accurately interpret natural human language and carry out instructions would improve the lives of people with language processing deficits and greatly benefit society in general. von Neumann in theorized that the human brain utilizes its own unique statistical neuronal computation to decode language and that this produces specific patterns of neuronal activity. This paper extends von Neumann's theory to the processing of partial semantics of declarative sentences. I developed semantic neuronal network models that emulate key features of cortical language processing and accurately compute partial semantics of English sentences. The method of computation implements the MAYA Semantic Technique, a mathematical technique I previously developed to determine partial semantics of sentences within a natural language processing program. Here I further simplified the technique by grouping repeating patterns into fewer categories. Unlike other natural language programs, my approach computes three partial semantics. The results of this research show that the computation of partial semantics of a sentence uses both feedforward and feedback projection which suggest that the partial semantic presented in this research might be a conscious activity within the human brain.

Modeling fMRI signals can provide insights into neural processing in the cerebral cortex

Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals.

A subset of thalamocortical projections to the retrosplenial cortex possesses two vesicular glutamate transporter isoforms, VGluT1 and VGluT2, in axon terminals and somata

Vesicular glutamate transporter isoforms, VGluT1-VGluT3, accumulate glutamate into synaptic vesicles and are considered to be important molecules in glutamatergic transmission. Among them, VGluT2 mRNA is expressed predominantly throughout the dorsal thalamus, whereas VGluT1 mRNA is expressed in a few thalamic nuclei. In the thalamic nuclei that project to the retrosplenial cortex (RSC), VGluT1 mRNA is expressed strongly in the anterodorsal thalamic nucleus (AD), is expressed moderately in the anteroventral and laterodorsal thalamic nuclei, and is not expressed in the anteromedial thalamic nucleus. Thus, it has been strongly suggested that a subset of thalamocortical projections to RSC possesses both VGluT1 and VGluT2. In this study, double-labeled neuronal somata showing both VGluT1 and VGluT2 immunolabelings were found exclusively in the ventral region of AD (vAD). Many double-labeled axon terminals were also found in two major targets of vAD, the rostral part of the reticular thalamic nucleus and layers Ia and III-IV of the retrosplenial granular b cortex (RSGb). Some were also found in layer Ia of the retrosplenial granular a cortex (RSGa). These axon terminals contain significant amounts of both VGluTs. Because the subset of thalamocortical projections to RSC has a unique molecular basis in the glutamatergic transmission system, it might play an important role in the higher cognitive functions processed in the RSC. Furthermore, double-labeled axon terminals of a different type were distributed in RSGb and RSGa. Because they are small and the immunoreactivity of VGluT2 is significantly weaker than that of VGluT1, they seemed to be a subset of corticocortical terminals.

Trigeminal intersubnuclear neurons: morphometry and input-dependent structural plasticity in adult rats

Intersubnuclear neurons in the caudal division of the spinal trigeminal nucleus that project to the principal nucleus (Pr5) play an active role in shaping the receptive fields of other neurons, at different levels in the ascending sensory system that processes information originating from the vibrissae. By using retrograde labeling and digital reconstruction, we investigated the morphometry and topology of the dendritic trees of these neurons and the changes induced by long-term experience-dependent plasticity in adult male rats. Primary afferent input was either eliminated by transection of the right infraorbital nerve (IoN), or selectively altered by repeated whisker clipping on the right side. These neurons do not display asymmetries between sides in basic metric and topologic parameters (global number of trees, nodes, spines, or dendritic ends), although neurons on the left tend to have longer terminal segments. Ipsilaterally, both deafferentation (IoN transection) and deprivation (whisker trimming) reduced the density of spines, and the former also caused a global increase in total dendritic length and a relative increase in more complex arbors. Contralaterally, deafferentation reduced more complex dendritic trees, and caused a moderate decline in dendritic length and spatial reach, and a loss of spines in number and density. Deprivation caused a similar, but more profound, effect on spines. Our findings provide original quantitative descriptions of a scarcely known cell population, and show that denervation- or deprivation-derived plasticity is expressed not only by neurons at higher levels of the sensory pathways, but also by neurons in key subcortical circuits for sensory processing.

Inter-synaptic learning of combination rules in a cortical network model

Selecting responses in working memory while processing combinations of stimuli depends strongly on their relations stored in long-term memory. However, the learning of XOR-like combinations of stimuli and responses according to complex rules raises the issue of the non-linear separability of the responses within the space of stimuli. One proposed solution is to add neurons that perform a stage of non-linear processing between the stimuli and responses, at the cost of increasing the network size. Based on the non-linear integration of synaptic inputs within dendritic compartments, we propose here an inter-synaptic (IS) learning algorithm that determines the probability of potentiating/depressing each synapse as a function of the co-activity of the other synapses within the same dendrite. The IS learning is effective with random connectivity and without either a priori wiring or additional neurons. Our results show that IS learning generates efficacy values that are sufficient for the processing of XOR-like combinations, on the basis of the sole correlational structure of the stimuli and responses. We analyze the types of dendrites involved in terms of the number of synapses from pre-synaptic neurons coding for the stimuli and responses. The synaptic efficacy values obtained show that different dendrites specialize in the detection of different combinations of stimuli. The resulting behavior of the cortical network model is analyzed as a function of inter-synaptic vs. Hebbian learning. Combinatorial priming effects show that the retrospective activity of neurons coding for the stimuli trigger XOR-like combination-selective prospective activity of neurons coding for the expected response. The synergistic effects of inter-synaptic learning and of mixed-coding neurons are simulated. The results show that, although each mechanism is sufficient by itself, their combined effects improve the performance of the network.

Coding and decoding with dendrites

Since the discovery of complex, voltage dependent mechanisms in the dendrites of multiple neuron types, great effort has been devoted in search of a direct link between dendritic properties and specific neuronal functions. Over the last few years, new experimental techniques have allowed the visualization and probing of dendritic anatomy, plasticity and integrative schemes with unprecedented detail. This vast amount of information has caused a paradigm shift in the study of memory, one of the most important pursuits in Neuroscience, and calls for the development of novel theories and models that will unify the available data according to some basic principles. Traditional models of memory considered neural cells as the fundamental processing units in the brain. Recent studies however are proposing new theories in which memory is not only formed by modifying the synaptic connections between neurons, but also by modifications of intrinsic and anatomical dendritic properties as well as fine tuning of the wiring diagram. In this review paper we present previous studies along with recent findings from our group that support a key role of dendrites in information processing, including the encoding and decoding of new memories, both at the single cell and the network level.

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, e.g. fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of RNNs may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics.

Dendritic structural plasticity

Dendrites represent the compartment of neurons primarily devoted to collecting and computating input. Far from being static structures, dendrites are highly dynamic during development and appear to be capable of plastic changes during the adult life of animals. During development, it is a combination of intrinsic programs and external signals that shapes dendrite morphology; input activity is a conserved extrinsic factor involved in this process. In adult life, dendrites respond with more modest modifications of their structure to various types of extrinsic information, including alterations of input activity. Here, the author reviews classical and recent evidence of dendrite plasticity in invertebrates and vertebrates and current progress in the understanding of the molecular mechanisms that underlie this plasticity. Importantly, some fundamental questions such as the functional role of dendrite remodeling and the causal link between structural modifications of neurons and plastic processes, including learning, are still open.

Parameter estimation of a spiking silicon neuron

Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics. Parameter estimation of a single neuron model, such that the model's output matches that of a biological neuron is an extremely important task. Hand tuning of parameters to obtain such behaviors is a difficult and time consuming process. This is further complicated when the neuron is instantiated in silicon (an attractive medium in which to implement these models) as fabrication imperfections make the task of parameter configuration more complex. In this paper we show two methods to automate the configuration of a silicon (hardware) neuron's parameters. First, we show how a Maximum Likelihood method can be applied to a leaky integrate and fire silicon neuron with spike induced currents to fit the neuron's output to desired spike times. We then show how a distance based method which approximates the negative log likelihood of the lognormal distribution can also be used to tune the neuron's parameters. We conclude that the distance based method is better suited for parameter configuration of silicon neurons due to its superior optimization speed.

Local model for contextual modulation in the cerebral cortex

A neural response to a sensory stimulus in cerebral cortex is modulated when other stimuli are presented simultaneously. The other stimuli can modulate responses even when they do not drive the neural output alone, indicating a non-linear summation of synaptic activity. The mechanisms of the nonlinearity have remained unclear. Here, I explore a model which considers both network and intracellular processes, and which can account for various types of contextual modulation. The processes include synaptic sensitivity function, determination of inhibition strength, dendritic decay of membrane voltage, and summation of excitatory and inhibitory membrane voltages. First, the model assumes that excitatory and inhibitory units have the same input sensitivity function, which is more broadly tuned than the output tuning function. Second, a central property of the model is that inhibition is a fraction of excitation, determined by covariance between the input and the sensitivity function. With proper fraction, a model neuron sums apparently decorrelated input, regardless of correlations in the original input. Third, the model assumes that synaptic input lands anisotropically on the dendrites, which together with passive dendritic decay cause exponential decay in summation along the input space. This explains the difference between input sensitivity function and output tuning function, and thus accounts for the division between driving classical and modulating extra-classical receptive fields. The model simulations replicate single-cell area summation function, far surround facilitation, and a shift in tuning function due to contextual stimulation. The model is very general, and should be applicable to various interactions between cortical representations.

Beyond faithful conduction: short-term dynamics, neuromodulation, and long-term regulation of spike propagation in the axon

Most spiking neurons are divided into functional compartments: a dendritic input region, a soma, a site of action potential initiation, an axon trunk and its collaterals for propagation of action potentials, and distal arborizations and terminals carrying the output synapses. The axon trunk and lower order branches are probably the most neglected and are often assumed to do nothing more than faithfully conducting action potentials. Nevertheless, there are numerous reports of complex membrane properties in non-synaptic axonal regions, owing to the presence of a multitude of different ion channels. Many different types of sodium and potassium channels have been described in axons, as well as calcium transients and hyperpolarization-activated inward currents. The complex time- and voltage-dependence resulting from the properties of ion channels can lead to activity-dependent changes in spike shape and resting potential, affecting the temporal fidelity of spike conduction. Neural coding can be altered by activity-dependent changes in conduction velocity, spike failures, and ectopic spike initiation. This is true under normal physiological conditions, and relevant for a number of neuropathies that lead to abnormal excitability. In addition, a growing number of studies show that the axon trunk can express receptors to glutamate, GABA, acetylcholine or biogenic amines, changing the relative contribution of some channels to axonal excitability and therefore rendering the contribution of this compartment to neural coding conditional on the presence of neuromodulators. Long-term regulatory processes, both during development and in the context of activity-dependent plasticity may also affect axonal properties to an underappreciated extent.

Encoding of spatio-temporal input characteristics by a CA1 pyramidal neuron model

The in vivo activity of CA1 pyramidal neurons alternates between regular spiking and bursting, but how these changes affect information processing remains unclear. Using a detailed CA1 pyramidal neuron model, we investigate how timing and spatial arrangement variations in synaptic inputs to the distal and proximal dendritic layers influence the information content of model responses. We find that the temporal delay between activation of the two layers acts as a switch between excitability modes: short delays induce bursting while long delays decrease firing. For long delays, the average firing frequency of the model response discriminates spatially clustered from diffused inputs to the distal dendritic tree. For short delays, the onset latency and inter-spike-interval succession of model responses can accurately classify input signals as temporally close or distant and spatially clustered or diffused across different stimulation protocols. These findings suggest that a CA1 pyramidal neuron may be capable of encoding and transmitting presynaptic spatiotemporal information about the activity of the entorhinal cortex-hippocampal network to higher brain regions via the selective use of either a temporal or a rate code.

Pyramidal neurons in the hippocampus are crucially involved in learning and memory functions, but the ways in which they contribute to the processing of sensory inputs and their internal representation remain mostly unclear. The principal neurons of the CA1 region of the hippocampus are surrounded by at least 21 different types of interneurons. This feature, together with the fact that CA1 pyramidal dendrites associate two major glutamatergic inputs arriving from the entorhinal cortex, makes it laborious to track the ‘how’ and ‘what’ of synaptic integration. The present study tries to shed light on the ‘what’, that is, the information content of the CA1 discharge pattern. Using a detailed biophysical CA1 neuron model, we show that the output of the model neuron contains spatial and temporal features of the incoming synaptic input. This information lies in the temporal pattern of the inter-spike intervals produced during the bursting activity which is induced by the temporal coincidence of the two activated synaptic streams. Our findings suggest that CA1 pyramidal neurons may be capable of capturing features of the ongoing network activity via the use of a temporal code for information transfer.

Cell-cell channels, viruses, and evolution: via infection, parasitism, and symbiosis toward higher levels of biological complexity

Between prokaryotic cells and eukaryotic cells there is dramatic difference in complexity which represents a problem for the current version of the cell theory, as well as for the current version of evolution theory. In the past few decades, the serial endosymbiotic theory of Lynn Margulis has been confirmed. This results in a radical departure from our understanding of living systems: the eukaryotic cell represents de facto"cells-within-cell." Higher order "cells-within-cell" situations are obvious at the eukaryotic cell level in the form of secondary and tertiary endosymbiosis, or in the male and female gametophytes of higher plants. The next challenge of the current version of the cell theory is represented by the fact that the multicellular fungi and plants are, in fact, supracellular assemblies as their cells are not physically separated from each other. Moreover, there are also examples of alliances and mergings between multicellular organisms. Infection, especially the viral one, but also bacterial and fungal infections, followed by symbiosis, is proposed to act as the major force that drives the biological evolution toward higher complexity.

Molecular and cellular approaches to memory allocation in neural circuits

Although memory allocation is a subject of active research in computer science, little is known about how the brain allocates information within neural circuits. There is an extensive literature on how specific types of memory engage different parts of the brain, and how neurons in these regions process and store information. Until recently, however, the mechanisms that determine how specific cells and synapses within a neural circuit (and not their neighbors) are recruited during learning have received little attention. Recent findings suggest that memory allocation is not random, but rather specific mechanisms regulate where information is stored within a neural circuit. New methods that allow tagging, imaging, activation, and inactivation of neurons in behaving animals promise to revolutionize studies of brain circuits, including memory allocation. Results from these studies are likely to have a considerable impact on computer science, as well as on the understanding of memory and its disorders.

Complex intrinsic membrane properties and dopamine shape spiking activity in a motor axon

We studied the peripheral motor axons of the two pyloric dilator (PD) neurons of the stomatogastric ganglion in the lobster, Homarus americanus. Intracellular recordings from the motor nerve showed both fast and slow voltage- and activity-dependent dynamics. During rhythmic bursts, the PD axons displayed changes in spike amplitude and duration. Pharmacological experiments and the voltage dependence of these phenomena suggest that inactivation of sodium and A-type potassium channels are responsible. In addition, the "resting" membrane potential was dependent on ongoing spike or burst activity, with more hyperpolarized values when activity was strong. Nerve stimulations, pharmacological block and current clamp experiments suggest that this is due to a functional antagonism between a slow after-hyperpolarization (sAHP) and inward rectification through hyperpolarization-activated current (IH). Dopamine application resulted in modest depolarization and "ectopic" peripheral spike initiation in the absence of centrally generated activity. This effect was blocked by CsCl and ZD7288, consistent with a role of IH. High frequency nerve stimulation inhibited peripheral spike initiation for several seconds, presumably due to the sAHP. Both during normal bursting activity and antidromic nerve stimulation, the conduction delay over the length of the peripheral nerve changed in a complex manner. This suggests that axonal membrane dynamics can have a substantial effect on the temporal fidelity of spike patterns propagated from a spike initiation site to a synaptic target, and that neuromodulators can influence the extent to which spike patterns are modified.

A propagating ERKII switch forms zones of elevated dendritic activation correlated with plasticity

Strong inputs to neurons trigger complex biochemical events leading to synaptic plasticity. These biochemical events occur at many spatial scales, ranging from submicron dendritic spines to signals that propagate hundreds of microns from dendrites to the nucleus. ERKII is an important signaling molecule that is involved in many aspects of plasticity, including local excitability, communication with the nucleus, and control of local protein synthesis. We observed that ERKII activation spreads long distances in apical dendrites of stimulated hippocampal CA1 pyramidal neurons. We combined experiments and models to show that this >100 mum spread was too large to be explained by biochemical reaction-diffusion effects. We show that two modes of calcium entry along the dendrite contribute to the extensive activation of ERKII. We predict the occurrence of feedback between biophysical events resulting in calcium entry, and biochemical events resulting in ERKII activation. This feedback causes a switch-like propagation of ERKII activation, coupled with enhanced electrical excitability, along the apical dendrite. We propose that this propagating switch forms zones on dendrites in which plasticity is facilitated.

A propagating ERKII switch forms zones of elevated dendritic activation correlated with plasticity

Strong inputs to neurons trigger complex biochemical events leading to synaptic plasticity. These biochemical events occur at many spatial scales, ranging from submicron dendritic spines to signals that propagate hundreds of microns from dendrites to the nucleus. ERKII is an important signaling molecule that is involved in many aspects of plasticity, including local excitability, communication with the nucleus, and control of local protein synthesis. We observed that ERKII activation spreads long distances in apical dendrites of stimulated hippocampal CA1 pyramidal neurons. We combined experiments and models to show that this >100 mum spread was too large to be explained by biochemical reaction-diffusion effects. We show that two modes of calcium entry along the dendrite contribute to the extensive activation of ERKII. We predict the occurrence of feedback between biophysical events resulting in calcium entry, and biochemical events resulting in ERKII activation. This feedback causes a switch-like propagation of ERKII activation, coupled with enhanced electrical excitability, along the apical dendrite. We propose that this propagating switch forms zones on dendrites in which plasticity is facilitated.