Paradoxical signaling regulates structural plasticity in dendritic spines

Transient spine enlargement (3-5 min timescale) is an important event associated with the structural plasticity of dendritic spines. Many of the molecular mechanisms associated with transient spine enlargement have been identified experimentally. Here, we use a systems biology approach to construct a mathematical model of biochemical signaling and actin-mediated transient spine expansion in response to calcium-influx due to NMDA receptor activation. We have identified that a key feature of this signaling network is the paradoxical signaling loop. Paradoxical components act bifunctionally in signaling networks and their role is to control both the activation and inhibition of a desired response function (protein activity or spine volume). Using ordinary differential equation (ODE)-based modeling, we show that the dynamics of different regulators of transient spine expansion including CaMKII, RhoA, and Cdc42 and the spine volume can be described using paradoxical signaling loops. Our model is able to capture the experimentally observed dynamics of transient spine volume. Furthermore, we show that actin remodeling events provide a robustness to spine volume dynamics. We also generate experimentally testable predictions about the role of different components and parameters of the network on spine dynamics

Efficient spiking neural network model of pattern motion selectivity in visual cortex

Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction- selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation outperforms a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40∈×∈40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available. © 2014 Springer Science+Business Media New York

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.Comment: Added measurements with noise in NEST simulation, add notice about journal publication. Frontiers in Neuromorphic Engineering (2019

How feedback inhibition shapes spike-timing-dependent plasticity and its implications for recent Schizophrenia models

It has been shown that plasticity is not a fixed property but, in fact, changes depending on the location of the synapse on the neuron and/or changes of biophysical parameters. Here we investigate how plasticity is shaped by feedback inhibition in a cortical microcircuit. We use a differential Hebbian learning rule to model spike-timing dependent plasticity and show analytically that the feedback inhibition shortens the time window for LTD during spike-timing dependent plasticity but not for LTP. We then use a realistic GENESIS model to test two hypothesis about interneuron hypofunction and conclude that a reduction in GAD67 is the most likely candidate as the cause for hypofrontality as observed in Schizophrenia

A Multi-Component Model of the Developing Retinocollicular Pathway Incorporating Axonal and Synaptic Growth

During development, neurons extend axons to different brain areas and produce stereotypical patterns of connections. The mechanisms underlying this process have been intensively studied in the visual system, where retinal neurons form retinotopic maps in the thalamus and superior colliculus. The mechanisms active in map formation include molecular guidance cues, trophic factor release, spontaneous neural activity, spike-timing dependent plasticity (STDP), synapse creation and retraction, and axon growth, branching and retraction. To investigate how these mechanisms interact, a multi-component model of the developing retinocollicular pathway was produced based on phenomenological approximations of each of these mechanisms. Core assumptions of the model were that the probabilities of axonal branching and synaptic growth are highest where the combined influences of chemoaffinity and trophic factor cues are highest, and that activity-dependent release of trophic factors acts to stabilize synapses. Based on these behaviors, model axons produced morphologically realistic growth patterns and projected to retinotopically correct locations in the colliculus. Findings of the model include that STDP, gradient detection by axonal growth cones and lateral connectivity among collicular neurons were not necessary for refinement, and that the instructive cues for axonal growth appear to be mediated first by molecular guidance and then by neural activity. Although complex, the model appears to be insensitive to variations in how the component developmental mechanisms are implemented. Activity, molecular guidance and the growth and retraction of axons and synapses are common features of neural development, and the findings of this study may have relevance beyond organization in the retinocollicular pathway

The Effects of Neurosteroids, such as Pregnenolone Sulfate and its receptor, TrpM3 in the Retina.

Pregnenolone sulfate (PregS) is the precursor to all steroid hormones and is produced in neurons in an activity dependent manner. Studies have shown that PregS production is upregulated during certain critical periods of development, such as in the first year of life in humans, during adolescence, and during pregnancy. Conversely, PregS is decreased during aging, as well as in several neurodevelopmental and neurodegenerative conditions. There are several known targets of PregS, such as a positive allosteric modulator NMDA receptors, sigma1 receptor, and as a negative allosteric modulator of GABA-A receptors. Recently a transient receptor potential channel, TrpM3 has been shown to be activated by PregS. TrpM3 is a heat sensitive (between 33-40oC), non-selective cation channel that is outwardly rectifying. PregS has been shown to increase the frequency of post-synaptic currents in the hippocampus and developing cerebellum, induce calcium transients in a subset of retinal ganglion cells, and enhance memory formation in rodents. Furthermore, PregS mediated TrpM3 activation induces calcium dependent transcription of early immediate genes, suggesting that activation of this channel may produce lasting effects on cells and systems in which it is activated. Because PregS is abundant during critical periods of development, we hypothesized that it may play a significant role during development. Furthermore, the role of PregS or its receptor TrpM3, has not previously been well characterized in the retina. To address this question, in this dissertation, we examine the role of the neurosteroid PregS and its receptor, TrpM3, on retinal waves, which are characteristic of specific stages of synaptic development and connectivity. Briefly, we show that PregS induces a TrpM3 dependent prolonged calcium transient, which is absent in the TrpM3-/- animals and increases the correlation of cell participation in waves. We also show that TrpM3 increases the frequency of post-synaptic currents, indicating a mechanism of action presynaptic to retinal ganglion cells, but that TrpM3 is expressed primarily in RGCs and Müller glia. Taken together, our results indicate that both PregS and TrpM3 are important in modulating spontaneous synaptic activity during development

Microglia shape presynaptic properties at developing glutamatergic synapses

Deficient neuron-microglia signaling during brain development is associated with abnormal synaptic maturation. However, the precise impact of deficient microglia function on synaptic maturation and the mechanisms involved remain poorly defined. Here we report that mice defective in neuron-to-microglia signaling via the fractalkine receptor (Cx3cr1 KO) show reduced microglial branching and altered motility and develop widespread deficits in glutamatergic neurotransmission. We characterized the functional properties of CA3-CA1 synapses in hippocampal slices from these mice and found that they display altered glutamatergic release probability, maintaining immature properties also at late developmental stages. In particular, CA1 synapses of Cx3cr1 KO show (i) immature AMPA/NMDA ratio across developmental time, displaying a normal NMDA component and a defective AMPA component of EPSC; (ii) defective functional connectivity, as demonstrated by reduced current amplitudes in the input/output curve; and (iii) greater facilitation in the paired pulse ratio (PPR), suggesting decreased release probability. In addition, minimal stimulation experiments revealed that excitatory synapses have normal potency, but an increased number of failures, confirming a deficit in presynaptic release. Consistently, KO mice were characterized by higher number of silent synapses in comparison to WT. The presynaptic deficits were corrected by performing experiments in conditions of high release probability (Ca2+ /Mg2+ ratio 8), where excitatory synapses showed normal synaptic multiplicity, AMPA/NMDA ratio, and proportion of silent synapses. These results establish that neuron-microglia interactions profoundly influence the functional maturation of excitatory presynaptic function

Dysregulation of microtubule stability impairs morphofunctional connectivity in primary neuronal networks

Functionally related neurons assemble into connected networks that process and transmit electrochemical information. To do this in a coordinated manner, the number and strength of synaptic connections is tightly regulated. Synapse function relies on the microtubule (MT) cytoskeleton, the dynamics of which are in turn controlled by a plethora of MT-associated proteins, including the MT-stabilizing protein Tau. Although mutations in the Tau-encodingMAPT gene underlie a set of neurodegenerative disorders, termed tauopathies, the exact contribution of MT dynamics and the perturbation thereof to neuronal network connectivity has not yet been scrutinized. Therefore, we investigated the impact of targeted perturbations of MT stability on morphological (e.g., neurite- and synapse density) and functional (e.g., synchronous calcium bursting) correlates of connectivity in networks of primary hippocampal neurons. We found that treatment with MT-stabilizing or -destabilizing compounds impaired morphofunctional connectivity in a reversible manner. We also discovered that overexpression of MAPT induced significant connectivity defects, which were accompanied by alterations in MT dynamics and increased resistance to pharmacological MT depolymerization. Overexpression of a MAPT variant harboring the P301L point mutation in the MT-binding domain did far less, directly linking neuronal connectivity with Tau's MT binding affinity. Our results show that MT stability is a vulnerable node in tauopathies and that its precise pharmacological tuning may positively affect neuronal network connectivity. However, a critical balance in MT turnover causes it to be a difficult therapeutic target with a narrow operating window

Dendritic Synapse Location and Neocortical Spike-Timing-Dependent Plasticity

While it has been appreciated for decades that synapse location in the dendritic tree has a powerful influence on signal processing in neurons, the role of dendritic synapse location on the induction of long-term synaptic plasticity has only recently been explored. Here, we review recent work revealing how learning rules for spike-timing-dependent plasticity (STDP) in cortical neurons vary with the spatial location of synaptic input. A common principle appears to be that proximal synapses show conventional STDP, whereas distal inputs undergo plasticity according to novel learning rules. One crucial factor determining location-dependent STDP is the backpropagating action potential, which tends to decrease in amplitude and increase in width as it propagates into the dendritic tree of cortical neurons. We discuss additional location-dependent mechanisms as well as the functional implications of heterogeneous learning rules at different dendritic locations for the organization of synaptic inputs