Concept: Pyramidal cell
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.
Loss of a sensory input causes the hypersensitivity in other modalities. In addition to cross-modal plasticity, the sensory cortices without receiving inputs undergo the plastic changes. It is not clear how the different types of neurons and synapses in the sensory cortex coordinately change after input deficits in order to prevent loss of their functions and to be used for other modalities. We studied this subject in the barrel cortices from whiskers-trimmed mice vs. controls. After whisker trimming for a week, the intrinsic properties of pyramidal neurons and the transmission of excitatory synapses were upregulated in the barrel cortex, but inhibitory neurons and GABAergic synapses were downregulated. The morphological analyses indicated that the number of processes and spines in pyramidal neurons increased, whereas the processes of GABAergic neurons decreased in the barrel cortex. The upregulation of excitatory neurons and the downregulation of inhibitory neurons boost the activity of network neurons in the barrel cortex to be high levels, which prevent the loss of their functions and enhances their sensitivity to sensory inputs. These changes may prepare for attracting the innervations from sensory cortices and/or peripheral nerves for other modalities during cross-modal plasticity.
Theoretical and computational frameworks for synaptic plasticity and learning have a long and cherished history, with few parallels within the well-established literature for plasticity of voltage-gated ion channels. In this study, we derive rules for plasticity in the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, and assess the synergy between synaptic and HCN channel plasticity in establishing stability during synaptic learning. To do this, we employ a conductance-based model for the hippocampal pyramidal neuron, and incorporate synaptic plasticity through the well-established Bienenstock-Cooper-Munro (BCM)-like rule for synaptic plasticity, wherein the direction and strength of the plasticity is dependent on the concentration of calcium influx. Under this framework, we derive a rule for HCN channel plasticity to establish homeostasis in synaptically-driven firing rate, and incorporate such plasticity into our model. In demonstrating that this rule for HCN channel plasticity helps maintain firing rate homeostasis after bidirectional synaptic plasticity, we observe a linear relationship between synaptic plasticity and HCN channel plasticity for maintaining firing rate homeostasis. Motivated by this linear relationship, we derive a calcium-dependent rule for HCN-channel plasticity, and demonstrate that firing rate homeostasis is maintained in the face of synaptic plasticity when moderate and high levels of cytosolic calcium influx induced depression and potentiation of the HCN-channel conductance, respectively. Additionally, we show that such synergy between synaptic and HCN-channel plasticity enhances the stability of synaptic learning through metaplasticity in the BCM-like synaptic plasticity profile. Finally, we demonstrate that the synergistic interaction between synaptic and HCN-channel plasticity preserves robustness of information transfer across the neuron under a rate-coding schema. Our results establish specific physiological roles for experimentally observed plasticity in HCN channels accompanying synaptic plasticity in hippocampal neurons, and uncover potential links between HCN-channel plasticity and calcium influx, dynamic gain control and stable synaptic learning.
PPARγ-coactivator-1α gene transfer reduces neuronal loss and amyloid-β generation by reducing β-secretase in an Alzheimer’s disease model
- Proceedings of the National Academy of Sciences of the United States of America
- Published over 3 years ago
Current therapies for Alzheimer’s disease (AD) are symptomatic and do not target the underlying Aβ pathology and other important hallmarks including neuronal loss. PPARγ-coactivator-1α (PGC-1α) is a cofactor for transcription factors including the peroxisome proliferator-activated receptor-γ (PPARγ), and it is involved in the regulation of metabolic genes, oxidative phosphorylation, and mitochondrial biogenesis. We previously reported that PGC-1α also regulates the transcription of β-APP cleaving enzyme (BACE1), the main enzyme involved in Aβ generation, and its expression is decreased in AD patients. We aimed to explore the potential therapeutic effect of PGC-1α by generating a lentiviral vector to express human PGC-1α and target it by stereotaxic delivery to hippocampus and cortex of APP23 transgenic mice at the preclinical stage of the disease. Four months after injection, APP23 mice treated with hPGC-1α showed improved spatial and recognition memory concomitant with a significant reduction in Aβ deposition, associated with a decrease in BACE1 expression. hPGC-1α overexpression attenuated the levels of proinflammatory cytokines and microglial activation. This effect was accompanied by a marked preservation of pyramidal neurons in the CA3 area and increased expression of neurotrophic factors. The neuroprotective effects were secondary to a reduction in Aβ pathology and neuroinflammation, because wild-type mice receiving the same treatment were unaffected. These results suggest that the selective induction of PGC-1α gene in specific areas of the brain is effective in targeting AD-related neurodegeneration and holds potential as therapeutic intervention for this disease.
Cortical surface recording techniques such as EEG and ECoG are widely used for measuring brain activity. The prevailing assumption is that surface potentials primarily reflect synaptic activity, although non-synaptic events may also contribute. Here we show that dendritic calcium spikes occurring in pyramidal neurons (that we showed previously are cognitively relevant) are clearly detectable in cortical surface potentials. To show this we developed an optogenetic, non-synaptic approach to evoke dendritic calcium spikes in vivo. We found that optogenetically evoked calcium spikes were easily detectable and had an unexpected waveform near the cortical surface. Sensory-evoked dendritic calcium spikes were also clearly detectable with amplitudes that matched the contribution of synaptic input. These results reveal how dendritic calcium spikes appear at the cortical surface and their significant impact on surface potentials, suggesting that long-standing surface recording data may contain information about dendritic activity that is relevant to behavior and cognitive function.Surface EEG recordings are thought to primarily detect synaptic activity. Here the authors devise an optogenetic method to evoke dendritic calcium spikes in layer 5 pyramidal cells of the rat somatosensory cortex, and report that optogenetically evoked, as well as sensory-evoked dendritic calcium spikes make a significant contribution to surface EEG recordings.
Glucocorticoids (GCs) are known to alter neuronal plasticity, impair learning and memory and play important roles in the generation and progression of Alzheimer’s disease. There are no effective drug options for preventing neuronal injury induced by chronic GC exposure. Ginsenoside Rg1 (Rg1) is a steroidal saponin found in ginseng. The present study investigated the neuroprotective effect of Rg1 on neuroinflammation damage induced by chronic dexamethasone (5 mg/kg for 28 days) exposure in male mice. Our results showed that Rg1 (2 and 4 mg/kg) treatment increased spontaneous motor activity and exploratory behavior in an open field test, and increased the number of entries into the new object zone in a novel object recognition test. Moreover, Rg1 (2 and 4 mg/kg) treatment significantly alleviated neuronal degeneration and increased MAP2 expression in the frontal cortex and hippocampus. Additionally, inhibition of NLRP‑1 inflammasomes was also involved in the mechanisms underlying the effect of Rg1 on GC‑induced neuronal injury. We found that Rg1 (2 and 4 mg/kg) treatment increased the expression of glucocorticosteroid receptor and decreased the expression of NLRP‑1, ASC, caspase‑1, caspase‑5, IL‑1β and IL‑18 in the hippocampus in male mice. The present study indicates that Rg1 may have protective effects on neuroinflammation and neuronal injury induced by chronic GC exposure.
The mechanisms that contribute to selective vulnerability of the magnocellular basal forebrain cholinergic neurons in neurodegenerative diseases, such as Alzheimer’s disease, are not fully understood. Because age is the primary risk factor for Alzheimer’s disease, mechanisms of interest must include age-related alterations in protein expression, cell type-specific markers and pathology. The present study explored the extent and characteristics of intraneuronal amyloid-β accumulation, particularly of the fibrillogenic 42-amino acid isoform, within basal forebrain cholinergic neurons in normal young, normal aged and Alzheimer’s disease brains as a potential contributor to the selective vulnerability of these neurons using immunohistochemistry and western blot analysis. Amyloid-β1-42 immunoreactivity was observed in the entire cholinergic neuronal population regardless of age or Alzheimer’s disease diagnosis. The magnitude of this accumulation as revealed by optical density measures was significantly greater than that in cortical pyramidal neurons, and magnocellular neurons in the globus pallidus did not demonstrate a similar extent of amyloid immunoreactivity. Immunoblot analysis with a panel of amyloid-β antibodies confirmed accumulation of high concentration of amyloid-β in basal forebrain early in adult life. There was no age- or Alzheimer-related alteration in total amyloid-β content within this region. In contrast, an increase in the large molecular weight soluble oligomer species was observed with a highly oligomer-specific antibody in aged and Alzheimer brains when compared with the young. Similarly, intermediate molecular weight oligomeric species displayed an increase in aged and Alzheimer brains when compared with the young using two amyloid-β42 antibodies. Compared to cortical homogenates, small molecular weight oligomeric species were lower and intermediate species were enriched in basal forebrain in ageing and Alzheimer’s disease. Regional and age-related differences in accumulation were not the result of alterations in expression of the amyloid precursor protein, as confirmed by both immunostaining and western blot. Our results demonstrate that intraneuronal amyloid-β accumulation is a relatively selective trait of basal forebrain cholinergic neurons early in adult life, and increases in the prevalence of intermediate and large oligomeric assembly states are associated with both ageing and Alzheimer’s disease. Selective intraneuronal amyloid-β accumulation in adult life and oligomerization during the ageing process are potential contributors to the degeneration of basal forebrain cholinergic neurons in Alzheimer’s disease.
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.
How sleep helps learning and memory remains unknown. We report in mouse motor cortex that sleep after motor learning promotes the formation of postsynaptic dendritic spines on a subset of branches of individual layer V pyramidal neurons. New spines are formed on different sets of dendritic branches in response to different learning tasks and are protected from being eliminated when multiple tasks are learned. Neurons activated during learning of a motor task are reactivated during subsequent non-rapid eye movement sleep, and disrupting this neuronal reactivation prevents branch-specific spine formation. These findings indicate that sleep has a key role in promoting learning-dependent synapse formation and maintenance on selected dendritic branches, which contribute to memory storage.
Developmental alterations of excitatory synapses are implicated in autism spectrum disorders (ASDs). Here, we report increased dendritic spine density with reduced developmental spine pruning in layer V pyramidal neurons in postmortem ASD temporal lobe. These spine deficits correlate with hyperactivated mTOR and impaired autophagy. In Tsc2+/- ASD mice where mTOR is constitutively overactive, we observed postnatal spine pruning defects, blockade of autophagy, and ASD-like social behaviors. The mTOR inhibitor rapamycin corrected ASD-like behaviors and spine pruning defects in Tsc2+/ mice, but not in Atg7(CKO) neuronal autophagy-deficient mice or Tsc2+/-:Atg7(CKO) double mutants. Neuronal autophagy furthermore enabled spine elimination with no effects on spine formation. Our findings suggest that mTOR-regulated autophagy is required for developmental spine pruning, and activation of neuronal autophagy corrects synaptic pathology and social behavior deficits in ASD models with hyperactivated mTOR.