Project description:Local or dendritic protein synthesis is required for long-term functional synaptic change, such as long-term potentiation (LTP) and long-term depression (LTD). LTP and LTD both rely on similar signal transduction cascades, which regulate translation initiation. Current research indicates that the specificity by which new proteins participate in either LTP or LTD may be determined in part by specific RNA-binding proteins as well as activity-dependent capture.
Project description:Long-term memory and synaptic plasticity require changes in gene expression and yet can occur in a synapse-specific manner. Messenger RNA localization and regulated translation at synapses are thus critical for establishing synapse specificity. Using live-cell microscopy of photoconvertible fluorescent protein translational reporters, we directly visualized local translation at synapses during long-term facilitation of Aplysia sensory-motor synapses. Translation of the reporter required multiple applications of serotonin, was spatially restricted to stimulated synapses, was transcript- and stimulus-specific, and occurred during long-term facilitation but not during long-term depression of sensory-motor synapses. Translational regulation only occurred in the presence of a chemical synapse and required calcium signaling in the postsynaptic motor neuron. Thus, highly regulated local translation occurs at synapses during long-term plasticity and requires trans-synaptic signals.
Project description:During early postnatal brain development, experience-driven delivery of AMPA receptors to synapses participates in the initial organization of cortical function. By combining virus-mediated in vivo gene delivery with in vitro whole cell recordings, we identified a subunit-specific developmental program of experience-driven AMPA receptor delivery to synapses in rat barrel cortex. We expressed green fluorescent protein (GFP)-tagged AMPA receptors (GFP-GluR1, or GFP-GluR4) into layer 2/3 pyramidal neurons at two distinct developmental periods, postnatal day (P)8-P10 and P12-P14. Two days after viral infection, acute brain slices were prepared, and synaptic transmission from layer 4 to layer 2/3 was analyzed by whole cell recordings. We found that whisker experience drives GluR4 but not GluR1 into these synapses early in postnatal development (P8-P10). However, at P12-14, GluR1 but not GluR4 is delivered into synapses by whisker experience. This precise developmental plan suggests unique plasticity properties endowed in different AMPA receptor subunits which shape the initial experience-driven organization of cortical function.
Project description:Experience-dependent plasticity shapes postnatal development of neural circuits, but the mechanisms that refine dendritic arbors, remodel spines, and impair synaptic activity are poorly understood. Mature brain-derived neurotrophic factor (BDNF) modulates neuronal morphology and synaptic plasticity, including long-term potentiation (LTP) via TrkB activation. BDNF is initially translated as proBDNF, which binds p75(NTR). In vitro, recombinant proBDNF modulates neuronal structure and alters hippocampal long-term plasticity, but the actions of endogenously expressed proBDNF are unclear. Therefore, we generated a cleavage-resistant probdnf knockin mouse. Our results demonstrate that proBDNF negatively regulates hippocampal dendritic complexity and spine density through p75(NTR). Hippocampal slices from probdnf mice exhibit depressed synaptic transmission, impaired LTP, and enhanced long-term depression (LTD) in area CA1. These results suggest that proBDNF acts in vivo as a biologically active factor that regulates hippocampal structure, synaptic transmission, and plasticity, effects that are distinct from those of mature BDNF.
Project description:Abnormally strong neural synchronization may impair brain function, as observed in several brain disorders. We computationally study how neuronal dynamics, synaptic weights, and network structure co-emerge, in particular, during (de)synchronization processes and how they are affected by external perturbation. To investigate the impact of different types of plasticity mechanisms, we combine a network of excitatory integrate-and-fire neurons with different synaptic weight and/or structural plasticity mechanisms: (i) only spike-timing-dependent plasticity (STDP), (ii) only homeostatic structural plasticity (hSP), i.e., without weight-dependent pruning and without STDP, (iii) a combination of STDP and hSP, i.e., without weight-dependent pruning, and (iv) a combination of STDP and structural plasticity (SP) that includes hSP and weight-dependent pruning. To accommodate the diverse time scales of neuronal firing, STDP, and SP, we introduce a simple stochastic SP model, enabling detailed numerical analyses. With tools from network theory, we reveal that structural reorganization may remarkably enhance the network's level of synchrony. When weaker contacts are preferentially eliminated by weight-dependent pruning, synchrony is achieved with significantly sparser connections than in randomly structured networks in the STDP-only model. In particular, the strengthening of contacts from neurons with higher natural firing rates to those with lower rates and the weakening of contacts in the opposite direction, followed by selective removal of weak contacts, allows for strong synchrony with fewer connections. This activity-led network reorganization results in the emergence of degree-frequency, degree-degree correlations, and a mixture of degree assortativity. We compare the stimulation-induced desynchronization of synchronized states in the STDP-only model (i) with the desynchronization of models (iii) and (iv). The latter require stimuli of significantly higher intensity to achieve long-term desynchronization. These findings may inform future pre-clinical and clinical studies with invasive or non-invasive stimulus modalities aiming at inducing long-lasting relief of symptoms, e.g., in Parkinson's disease.
Project description:Neuronal avalanches measured in vitro and in vivo in different cortical networks consistently exhibit power law behaviour for the size and duration distributions with exponents typical for a mean field self-organized branching process. These exponents are also recovered in neuronal network simulations implementing various neuronal dynamics on different network topologies. They can therefore be considered a very robust feature of spontaneous neuronal activity. Interestingly, this scaling behaviour is also observed on regular lattices in finite dimensions, which raises the question about the origin of the mean field behavior observed experimentally. In this study we provide an answer to this open question by investigating the effect of activity dependent plasticity in combination with the neuronal refractory time in a neuronal network. Results show that the refractory time hinders backward avalanches forcing a directed propagation. Hebbian plastic adaptation plays the role of sculpting these directed avalanche patterns into the topology of the network slowly changing it into a branched structure where loops are marginal.
Project description:Amyloid precursor protein (APP) is associated with both familial and sporadic forms of Alzheimer's disease. Despite its importance, the role of APP family in neuronal function and survival remains unclear because of perinatal lethality exhibited by knockout mice lacking all three APP family members. Here we report that selective inactivation of APP family members in excitatory neurons of the postnatal forebrain results in neither cortical neurodegeneration nor increases in apoptosis and gliosis up to ∼2 years of age. However, hippocampal synaptic plasticity, learning, and memory are impaired in these mutant mice. Furthermore, hippocampal neurons lacking APP family exhibit hyperexcitability, as evidenced by increased neuronal spiking in response to depolarizing current injections, whereas blockade of Kv7 channels mimics and largely occludes the effects of APP family inactivation. These findings demonstrate that APP family is not required for neuronal survival and suggest that APP family may regulate neuronal excitability through Kv7 channels.
Project description:Neurons are highly compartmentalized cells with tightly controlled subcellular protein organization. While brain transcriptome, connectome and global proteome maps are being generated, system-wide analysis of temporal protein dynamics at the subcellular level are currently lacking. Here, we perform a temporally-resolved surfaceome analysis of primary neuron cultures and reveal dynamic surface protein clusters that reflect the functional requirements during distinct stages of neuronal development. Direct comparison of surface and total protein pools during development and homeostatic synaptic scaling demonstrates system-wide proteostasis-independent remodeling of the neuronal surface, illustrating widespread regulation on the level of surface trafficking. Finally, quantitative analysis of the neuronal surface during chemical long-term potentiation (cLTP) reveals fast externalization of diverse classes of surface proteins beyond the AMPA receptor, providing avenues to investigate the requirement of exocytosis for LTP. Our resource (neurosurfaceome.ethz.ch) highlights the importance of subcellular resolution for systems-level understanding of cellular processes.
Project description:A growing body of work underlines striking similarities between biological neural networks and recurrent, binary neural networks. A relatively smaller body of work, however, addresses the similarities between learning dynamics employed in deep artificial neural networks and synaptic plasticity in spiking neural networks. The challenge preventing this is largely caused by the discrepancy between the dynamical properties of synaptic plasticity and the requirements for gradient backpropagation. Learning algorithms that approximate gradient backpropagation using local error functions can overcome this challenge. Here, we introduce Deep Continuous Local Learning (DECOLLE), a spiking neural network equipped with local error functions for online learning with no memory overhead for computing gradients. DECOLLE is capable of learning deep spatio temporal representations from spikes relying solely on local information, making it compatible with neurobiology and neuromorphic hardware. Synaptic plasticity rules are derived systematically from user-defined cost functions and neural dynamics by leveraging existing autodifferentiation methods of machine learning frameworks. We benchmark our approach on the event-based neuromorphic dataset N-MNIST and DvsGesture, on which DECOLLE performs comparably to the state-of-the-art. DECOLLE networks provide continuously learning machines that are relevant to biology and supportive of event-based, low-power computer vision architectures matching the accuracies of conventional computers on tasks where temporal precision and speed are essential.
Project description:Surface proteins are of fundamental importance for formation of synaptic connections and activity-dependent plasticity. Here, we used a spatiotemporally resolved cell-surface proteotype analysis to characterize the neuronal surface-exposed proteome, or surfaceome, during neuronal development and synapse formation in primary neuronal cultures. We established a map of the neuronal surfaceome, which includes about 1,000 surface proteins, and analyzed the dynamic remodeling of the quantitative surfaceome during development. We identified time-resolved surface-abundance profile clusters that correspond to distinct stages of neuronal development. We discovered that surface abundance changes can correlate with or be uncoupled from the total cellular abundance. Finally, we observed system-wide surfaceome modulation in response to homeostatic synaptic scaling and exocytosis of diverse cargo during long-term potentiation.