- Proceedings of the National Academy of Sciences of the United States of America
- Published about 5 years ago
A hypothesis and the experiments to test it propose that very long-term memories, such as fear conditioning, are stored as the pattern of holes in the perineuronal net (PNN), a specialized ECM that envelops mature neurons and restricts synapse formation. The 3D intertwining of PNN and synapses would be imaged by serial-section EM. Lifetimes of PNN vs. intrasynaptic components would be compared with pulse-chase (15)N labeling in mice and (14)C content in human cadaver brains. Genetically encoded indicators and antineoepitope antibodies should improve spatial and temporal resolution of the in vivo activity of proteases that locally erode PNN. Further techniques suggested include genetic KOs, better pharmacological inhibitors, and a genetically encoded snapshot reporter, which will capture the pattern of activity throughout a large ensemble of neurons at a time precisely defined by the triggering illumination, drive expression of effector genes to mark those cells, and allow selective excitation, inhibition, or ablation to test their functional importance. The snapshot reporter should enable more precise inhibition or potentiation of PNN erosion to compare with behavioral consequences. Finally, biosynthesis of PNN components and proteases would be imaged.
Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR. Other neuron classes, such as interneurons, may possess only a few independent dendritic sub-units, or only passive dendrites where input summation is purely sub-linear, and where dendritic sub-units are only saturating. To determine if such neurons can also compute linearly non-separable functions, we enumerate, for a given parameter range, the Boolean functions implementable by a binary neuron model with a linear sub-unit and either a single spiking or a saturating dendritic sub-unit. We then analytically generalize these numerical results to an arbitrary number of non-linear sub-units. First, we show that a single non-linear dendritic sub-unit, in addition to the somatic non-linearity, is sufficient to compute linearly non-separable functions. Second, we analytically prove that, with a sufficient number of saturating dendritic sub-units, a neuron can compute all functions computable with purely excitatory inputs. Third, we show that these linearly non-separable functions can be implemented with at least two strategies: one where a dendritic sub-unit is sufficient to trigger a somatic spike; another where somatic spiking requires the cooperation of multiple dendritic sub-units. We formally prove that implementing the latter architecture is possible with both types of dendritic sub-units whereas the former is only possible with spiking dendrites. Finally, we show how linearly non-separable functions can be computed by a generic two-compartment biophysical model and a realistic neuron model of the cerebellar stellate cell interneuron. Taken together our results demonstrate that passive dendrites are sufficient to enable neurons to compute linearly non-separable functions.
The E4 isoform of apolipoprotein (apoE4) is known to be a major risk factor for Alzheimer’s Disease (AD). Previous in vitro studies have shown apoE4 to have a negative effect on neuronal outgrowth when incubated with lipids. The effect of apoE4 itself on the development of neurons from the central nervous system (CNS), however, has not been well characterized. Consequently, apoE4 alone has not been pursued as a substrate for neuronal cultures. In this study, the effect of surface-bound apoE4 on developmental features of rat hippocampal neurons was examined. We show that apoE4 substrates elicit significantly enhanced values in all developmental features at day 2 of culture when compared to laminin (LN) substrates, which is the current substrate-of-choice for neuronal cultures. Interestingly, the adhesion of hippocampal neurons was found to be significantly lower on LN substrates than on glass substrates, but the axon lengths on both substrates were similar. In addition, this study demonstrates that the adhesion- and growth-enhancing effects of apoE4 substrates are not mediated by heparan sulfate proteoglycans (HSPGs), proteins that have been indicated to function as receptors or co-receptors for apoE4. In the absence of lipids, apoE4 appears to use an unknown pathway for up-regulating neuronal adhesion and neurite outgrowth. Our results indicate that apoE4 is better than LN as a substrate for primary cultures of CNS neurons and should be considered in the design of tissue engineered CNS.
Following the initial acute stage of spinal cord injury, a cascade of cellular and inflammatory responses will lead to progressive secondary damage of the nerve tissue surrounding the primary injury site. The degeneration is manifested by loss of neurons and glial cells, demyelination and cyst formation. Injury to the mammalian spinal cord results in nearly complete failure of the severed axons to regenerate. We have previously demonstrated that the antioxidants N-acetyl-cysteine (NAC) and acetyl-L-carnitine (ALC) can attenuate retrograde neuronal degeneration after peripheral nerve and ventral root injury. The present study evaluates the effects of NAC and ALC on neuronal survival, axonal sprouting and glial cell reactions after spinal cord injury in adult rats. Tibial motoneurons in the spinal cord were pre-labeled with fluorescent tracer Fast Blue one week before lumbar L5 hemisection. Continuous intrathecal infusion of NAC (2.4 mg/day) or ALC (0.9 mg/day) was initiated immediately after spinal injury using Alzet 2002 osmotic minipumps. Neuroprotective effects of treatment were assessed by counting surviving motoneurons and by using quantitative immunohistochemistry and Western blotting for neuronal and glial cell markers 4 weeks after hemisection. Spinal cord injury induced significant loss of tibial motoneurons in L4-L6 segments. Neuronal degeneration was associated with decreased immunostaining for microtubular-associated protein-2 (MAP2) in dendritic branches, synaptophysin in presynaptic boutons and neurofilaments in nerve fibers. Immunostaining for the astroglial marker GFAP and microglial marker OX42 was increased. Treatment with NAC and ALC rescued approximately half of the motoneurons destined to die. In addition, antioxidants restored MAP2 and synaptophysin immunoreactivity. However, the perineuronal synaptophysin labeling was not recovered. Although both treatments promoted axonal sprouting, there was no effect on reactive astrocytes. In contrast, the microglial reaction was significantly attenuated. The results indicate a therapeutic potential for NAC and ALC in the early treatment of traumatic spinal cord injury.
The PACSIN (protein kinase C and casein kinase 2 substrate in neurons) adapter proteins couple components of the clathrin-mediated endocytosis machinery with regulators of actin polymerization and thereby regulate the surface expression of specific receptors. The brain-specific PACSIN 1 is enriched at synapses and has been proposed to affect neuromorphogenesis and the formation and maturation of dendritic spines. In studies of how phosphorylation of PACSIN 1 contributes to neuronal function, we identified serine 358 as a specific site used by casein kinase 2 (CK2) in vitro and in vivo. Phosphorylated PACSIN 1 was found in neuronal cytosol and membrane fractions. This localization could be modulated by trophic factors such as BDNF. We further show that expression of a phospho-negative PACSIN 1 mutant, S358A, or inhibition of CK2 drastically reduces spine formation in neurons. We identified a novel protein complex containing the spine regulator Rac1, its GAP neuron-associated developmentally-regulated protein (NADRIN) and PACSIN 1. CK2 phosphorylation of PACSIN 1 leads to a dissociation of the complex upon BDNF-treatment and induces Rac1-dependent spine formation in dendrites of hippocampal neurons. These findings suggest that upon BDNF signaling PACSIN 1 is phosphorylated by CK2 which is essential for spine formation.
Information in a computer is quantified by the number of bits that can be stored and recovered. An important question about the brain is how much information can be stored at a synapse through synaptic plasticity, which depends on the history of probabilistic synaptic activity. The strong correlation between size and efficacy of a synapse allowed us to estimate the variability of synaptic plasticity. In an EM reconstruction of hippocampal neuropil we found single axons making two or more synaptic contacts onto the same dendrites, having shared histories of presynaptic and postsynaptic activity. The spine heads and neck diameters, but not neck lengths, of these pairs were nearly identical in size. We found that there is a minimum of 26 distinguishable synaptic strengths, corresponding to storing 4.7 bits of information at each synapse. Because of stochastic variability of synaptic activation the observed precision requires averaging activity over several minutes.
Neurons are the computational elements that compose the brain and their fundamental principles of activity are known for decades. According to the long-lasting computational scheme, each neuron sums the incoming electrical signals via its dendrites and when the membrane potential reaches a certain threshold the neuron typically generates a spike to its axon. Here we present three types of experiments, using neuronal cultures, indicating that each neuron functions as a collection of independent threshold units. The neuron is anisotropically activated following the origin of the arriving signals to the membrane, via its dendritic trees. The first type of experiments demonstrates that a single neuron’s spike waveform typically varies as a function of the stimulation location. The second type reveals that spatial summation is absent for extracellular stimulations from different directions. The third type indicates that spatial summation and subtraction are not achieved when combining intra- and extra- cellular stimulations, as well as for nonlocal time interference, where the precise timings of the stimulations are irrelevant. Results call to re-examine neuronal functionalities beyond the traditional framework, and the advanced computational capabilities and dynamical properties of such complex systems.
Schizophrenia is a heritable brain illness with unknown pathogenic mechanisms. Schizophrenia’s strongest genetic association at a population level involves variation in the major histocompatibility complex (MHC) locus, but the genes and molecular mechanisms accounting for this have been challenging to identify. Here we show that this association arises in part from many structurally diverse alleles of the complement component 4 (C4) genes. We found that these alleles generated widely varying levels of C4A and C4B expression in the brain, with each common C4 allele associating with schizophrenia in proportion to its tendency to generate greater expression of C4A. Human C4 protein localized to neuronal synapses, dendrites, axons, and cell bodies. In mice, C4 mediated synapse elimination during postnatal development. These results implicate excessive complement activity in the development of schizophrenia and may help explain the reduced numbers of synapses in the brains of individuals with schizophrenia.
Pyramidal neurons represent the majority of excitatory neurons in the neocortex. Each pyramidal neuron receives input from thousands of excitatory synapses that are segregated onto dendritic branches. The dendrites themselves are segregated into apical, basal, and proximal integration zones, which have different properties. It is a mystery how pyramidal neurons integrate the input from thousands of synapses, what role the different dendrites play in this integration, and what kind of network behavior this enables in cortical tissue. It has been previously proposed that non-linear properties of dendrites enable cortical neurons to recognize multiple independent patterns. In this paper we extend this idea in multiple ways. First we show that a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. By this mechanism, a neuron can predict its activation in hundreds of independent contexts. We then present a network model based on neurons with these properties that learns time-based sequences. The network relies on fast local inhibition to preferentially activate neurons that are slightly depolarized. Through simulation we show that the network scales well and operates robustly over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. We contrast the properties of the new network model with several other neural network models to illustrate the relative capabilities of each. We conclude that pyramidal neurons with thousands of synapses, active dendrites, and multiple integration zones create a robust and powerful sequence memory. Given the prevalence and similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory may be a universal property of neocortical tissue.
Inspired by the brain’s structure, we have developed an efficient, scalable, and flexible non-von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts.