Cdc42 activation is necessary for heterosynaptic cooperation and competition.

Synapses change their weights in response to neuronal activity and in turn, neuronal networks alter their response properties and ultimately allow the brain to store information as memories. As for memories, not all events are maintained over time. Maintenance of synaptic plasticity depends on the interplay between functional changes at synapses and the synthesis of plasticity-related proteins that are involved in stabilizing the initial functional changes. Different forms of synaptic plasticity coexist in time and across the neuronal dendritic area. Thus, homosynaptic plasticity refers to activity-dependent synaptic modifications that are input-specific, whereas heterosynaptic plasticity relates to changes in non-activated synapses. Heterosynaptic forms of plasticity, such as synaptic cooperation and competition allow neurons to integrate events that occur separated by relatively large time windows, up to one hour. Here, we show that activation of Cdc42, a Rho GTPase that regulates actin cytoskeleton dynamics, is necessary for the maintenance of long-term potentiation (LTP) in a time-dependent manner. Inhibiting Cdc42 activation does not alter the time-course of LTP induction and its initial expression but blocks its late maintenance. We show that Cdc42 activation is involved in the phosphorylation of cofilin, a protein involved in modulating actin filaments and that weak and strong synaptic activation leads to similar levels on cofilin phosphorylation, despite different levels of LTP expression. We show that Cdc42 activation is required for synapses to interact by cooperation or competition, supporting the hypothesis that modulation of the actin cytoskeleton provides an activity-dependent and time-restricted permissive state of synapses allowing synaptic plasticity to occur. We found that under competition, the sequence in which synapses are activated determines the degree of LTP destabilization, demonstrating that competition is an active destabilization process. Taken together, we show that modulation of actin cytoskeleton by Cdc42 activation is necessary for the expression of homosynaptic and heterosynaptic forms of plasticity. Determining the temporal and spatial rules that determine whether synapses cooperate or compete will allow us to understand how memories are associated.

Distinct contributions of ventral CA1/amygdala co-activation to the induction and maintenance of synaptic plasticity.

The amygdala is known to modulate hippocampal synaptic plasticity. One role could be an immediate effect of basolateral amygdala (BLA) in priming synaptic plasticity in the hippocampus. Another role could be through associative synaptic co-operation and competition that triggers events involved in the maintenance of synaptic potentiation. We present evidence that the timing and activity level of BLA stimulation are important factors for the induction and maintenance of long-term potentiation (LTP) in ventral hippocampal area CA1. A 100 Hz BLA co-stimulation facilitated the induction of LTP, whereas 200 Hz co-stimulation attenuated induction. A 100 Hz BLA co-stimulation also caused enhanced persistence, sufficient to prevent synaptic competition. This maintenance effect is likely through translational mechanisms, as mRNA expression of primary response genes was unaffected, whereas protein level of plasticity-related products was increased. Further understanding of the neural mechanisms of amygdala modulation on hippocampus could provide insights into the mechanisms of emotional disorders.

Behavioral Timescale Cooperativity and Competitive Synaptic Interactions Regulate the Induction of Complex Spike Burst-Dependent Long-Term Potentiation.

Although Hebbian LTP has an important role in memory formation, the properties of Hebbian LTP cannot fully account for, and in some cases seem incompatible with, fundamental properties of associative learning. Importantly, findings from computational and neurophysiological studies suggest that burst-dependent forms of plasticity, where dendritic spikes and bursts of action potentials provide the postsynaptic depolarization needed for LTP induction, may overcome some of the limitations of conventional Hebbian LTP. Thus, I investigated how excitatory synapses onto CA1 pyramidal cells interact during the induction of complex spike (CS) burst-dependent LTP in hippocampal slices from male mice. Consistent with previous findings, theta-frequency trains of synaptic stimulation induce a Hebbian form of plasticity where postsynaptic CS bursts provide the depolarization needed for NMDAR activation and LTP induction. However, in contrast to conventional Hebbian plasticity, where cooperative LTP induction requires coactivation of synapses on a timescale of tens of milliseconds, cooperative interactions between synapses activated several seconds apart can induce CS burst-dependent LTP. A novel, retroactive form of heterosynaptic plasticity, where activation of one group of synapses triggers LTP induction at other synapses that were active seconds earlier, also contributes to cooperativity in CS burst-dependent LTP. Moreover, competitive synaptic interactions that emerge during prolonged bouts of postsynaptic CS bursting potently regulate CS burst-dependent LTP. Together, the unusual properties of synaptic cooperativity and competition in CS burst-dependent LTP enable Hebbian synapses to operate and interact on behavioral timescales.SIGNIFICANCE STATEMENT While EPSP-evoked complex spike (CS) bursting induces LTP at excitatory synapses onto hippocampal CA1 pyramidal cells, the properties of synaptic interactions during the induction of CS burst-dependent LTP have not been investigated. Here I report that interactions between independent synaptic inputs during the induction of CS burst-dependent LTP exhibit a number of novel, computationally relevant properties. Unlike conventional Hebbian LTP, the induction of CS burst-dependent LTP is regulated by proactive and retroactive cooperative interactions between synapses activated several seconds apart. Moreover, activity-dependent, competitive interactions between synapses allow strongly activated synapses to suppress LTP induction at more weakly activated synapses. Thus, CS burst-dependent LTP exhibits a number of the unique properties that overcome significant limitations of standard Hebbian plasticity rules.

Constraint-induced intervention as an emergent phenomenon from synaptic competition in biological systems.

The principle of constraint-induced therapy is widely practiced in rehabilitation. In hemiplegic cerebral palsy (CP) with impaired contralateral corticospinal projection due to unilateral injury, function improves after imposing a temporary constraint on limbs from the less affected hemisphere. This type of partially-reversible impairment in motor control by early brain injury bears a resemblance to the experience-dependent plastic acquisition and modification of neuronal response selectivity in the visual cortex. Previously, such mechanism was modeled within the framework of BCM (Bienenstock-Cooper-Munro) theory, a rate-based synaptic modification theory. Here, we demonstrate a minimally complex yet sufficient neural network model which provides a fundamental explanation for inter-hemispheric competition using a simplified spike-based model of information transmission and plasticity. We emulate the restoration of function in hemiplegic CP by simulating the competition between cells of the ipsilateral and contralateral corticospinal tracts. We use a high-speed hardware neural simulation to provide realistic numbers of spikes and realistic magnitudes of synaptic modification. We demonstrate that the phenomenon of constraint-induced partial reversal of hemiplegia can be modeled by simplified neural descending tracts with 2 layers of spiking neurons and synapses with spike-timing-dependent plasticity (STDP). We further demonstrate that persistent hemiplegia following unilateral cortical inactivation or deprivation is predicted by the STDP-based model but is inconsistent with BCM model. Although our model is a highly simplified and limited representation of the corticospinal system, it offers an explanation of how constraint as an intervention can help the system to escape from a suboptimal solution. This is a display of an emergent phenomenon from the synaptic competition.

Temporal Gating of Synaptic Competition in the Amygdala by Cannabinoid Receptor Activation.

The acquisition of fear memories involves plasticity of the thalamic and cortical pathways to the lateral amygdala (LA). In turn, the maintenance of synaptic plasticity requires the interplay between input-specific synaptic tags and the allocation of plasticity-related proteins. Based on this interplay, weakly activated synapses can express long-lasting forms of synaptic plasticity by cooperating with strongly activated synapses. Increasing the number of activated synapses can shift cooperation to competition. Synaptic cooperation and competition can determine whether two events, separated in time, are associated or whether a particular event is selected for storage. The rules that determine whether synapses cooperate or compete are unknown. We found that synaptic cooperation and competition, in the LA, are determined by the temporal sequence of cortical and thalamic stimulation and that the strength of the synaptic tag is modulated by the endocannabinoid signaling. This modulation is particularly effective in thalamic synapses, supporting a critical role of endocannabinoids in restricting thalamic plasticity. Also, we found that the availability of synaptic proteins is activity-dependent, shifting competition to cooperation. Our data present the first evidence that presynaptic modulation of synaptic activation, by the cannabinoid signaling, functions as a temporal gating mechanism limiting synaptic cooperation and competition.

Actin remodeling, the synaptic tag and the maintenance of synaptic plasticity.

Activity-dependent plasticity of synaptic connections is a hallmark of the mammalian brain and represents a key mechanism for rewiring neural circuits during development, experience-dependent plasticity, and brain disorders. Cellular models of memory, such as long-term potentiation and long-term depression, share common principles to memory consolidation. As for memory, the maintenance of synaptic plasticity is dependent on the synthesis of de novo protein synthesis. The synaptic-tagging and capture hypothesis states that the maintenance of synaptic plasticity is dependent on the interplay between input-specific synaptic tags and the allocation or capture of plasticity-related proteins (PRPs) at activated synapses. The setting of the synaptic tag and the capture of PRPs are independent processes that can occur separated in time and different groups of activated synapses. How are these two processes orchestrated in time and space? Here, we discuss the synaptic-tagging and capture hypothesis in the light of neuronal compartmentalization models and address the role of actin as a putative synaptic tag. If different groups of synapses interact by synaptic-tagging and capture mechanisms, understanding the spatial rules of such interaction is key to define the relevant neuronal compartment. We also discuss how actin modulation can allow an input-specific capture of PRPs and try to conciliate the temporal dynamics of synaptic actin with the maintenance of plasticity. Understanding how multiple synapses interact in time and space is fundamental to predict how neurons integrate information and ultimately how memory is acquired.

Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier.

One of the modern trends in the design of human-machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular, we have shown that sensory neurons in the input layer of SNNs can simultaneously encode the input signal based both on the spiking frequency rate and on varying the latency in generating spikes. In the case of such mixed temporal-rate coding, the SNN should implement learning working properly for both types of coding. Based on this, we investigate how a single neuron can be trained with pure rate and temporal patterns, and then build a universal SNN that is trained using mixed coding. In particular, we study Hebbian and competitive learning in SNN in the context of temporal and rate coding problems. We show that the use of Hebbian learning through pair-based and triplet-based spike timing-dependent plasticity (STDP) rule is accomplishable for temporal coding, but not for rate coding. Synaptic competition inducing depression of poorly used synapses is required to ensure a neural selectivity in the rate coding. This kind of competition can be implemented by the so-called forgetting function that is dependent on neuron activity. We show that coherent use of the triplet-based STDP and synaptic competition with the forgetting function is sufficient for the rate coding. Next, we propose a SNN capable of classifying electromyographical (EMG) patterns using an unsupervised learning procedure. The neuron competition achieved via lateral inhibition ensures the "winner takes all" principle among classifier neurons. The SNN also provides gradual output response dependent on muscular contraction strength. Furthermore, we modify the SNN to implement a supervised learning method based on stimulation of the target classifier neuron synchronously with the network input. In a problem of discrimination of three EMG patterns, the SNN with supervised learning shows median accuracy 99.5% that is close to the result demonstrated by multi-layer perceptron learned by back propagation of an error algorithm.

Neuregulin1 displayed on motor axons regulates terminal Schwann cell-mediated synapse elimination at developing neuromuscular junctions.

Synaptic connections in the nervous system are rearranged during development and in adulthood as a feature of growth, plasticity, aging, and disease. Glia are implicated as active participants in these changes. Here we investigated a signal that controls the participation of peripheral glia, the terminal Schwann cells (SCs), at the neuromuscular junction (NMJ) in mice. Transgenic manipulation of the levels of membrane-tethered neuregulin1 (NRG1-III), a potent activator of SCs normally presented on motor axons, alters the rate of loss of motor inputs at NMJs during developmental synapse elimination. In addition, NMJs of adult transgenic mice that expressed excess axonal NRG1-III exhibited continued remodeling, in contrast to the more stable morphologies of controls. In fact, synaptic SCs of these adult mice with NRG1-III overexpression exhibited behaviors evident in wild type neonates during synapse elimination, including an affinity for the postsynaptic myofiber surface and phagocytosis of nerve terminals. Given that levels of NRG1-III expression normally peak during the period of synapse elimination, our findings identify axon-tethered NRG1 as a molecular determinant for SC-driven neuromuscular synaptic plasticity.

Ionic Diffusive Nanomemristors with Dendritic Competition and Cooperation Functions for Ultralow Voltage Neuromorphic Computing.

Realization of dendric signal processing in the human brain is of great significance for spatiotemporal neuromorphic engineering. Here, we proposed an ionic dendrite device with multichannel communication, which could realize synaptic behaviors even under an ultralow action potential of 80 mV. The device not only could simulate one-to-one information transfer of axons but also achieve a many-to-one modulation mode of dendrites. By the adjustment of two presynapses, Pavlov's dog conditioning experiment was learned successfully. Furthermore, the device also could emulate the biological synaptic competition and synaptic cooperation phenomenon through the comodulation of three presynapses, which are crucial for artificial neural network (ANN) implementation. Finally, an ANN was further constructed to realize highly efficient and anti-interference recognition of fashion patterns. By introducing the cooperative device, synaptic weight updates could be improved for higher linearity and larger dynamic regulation range in neuromorphic computing, resulting in higher recognition accuracy and efficiency. Such an artificial dendric device has great application prospects in the processing of more complex information and the construction of an ANN system with more functions.

Evidence that Alzheimer's Disease Is a Disease of Competitive Synaptic Plasticity Gone Awry.

 Mounting evidence indicates that a physiological function of amyloid-ß (Aß) is to mediate neural activity-dependent homeostatic and competitive synaptic plasticity in the brain. I have previously summarized the lines of evidence supporting this hypothesis and highlighted the similarities between Aß and anti-microbial peptides in mediating cell/synapse competition. In cell competition, anti-microbial peptides deploy a multitude of mechanisms to ensure both self-protection and competitor elimination. Here I review recent studies showing that similar mechanisms are at play in Aß-mediated synapse competition and perturbations in these mechanisms underpin Alzheimer's disease (AD). Specifically, I discuss evidence that Aß and ApoE, two crucial players in AD, co-operate in the regulation of synapse competition. Glial ApoE promotes self-protection by increasing the production of trophic monomeric Aß and inhibiting its assembly into toxic oligomers. Conversely, Aß oligomers, once assembled, promote the elimination of competitor synapses via direct toxic activity and amplification of "eat-me" signals promoting the elimination of weak synapses. I further summarize evidence that neuronal ApoE may be part of a gene regulatory network that normally promotes competitive plasticity, explaining the selective vulnerability of ApoE expressing neurons in AD brains. Lastly, I discuss evidence that sleep may be key to Aß-orchestrated plasticity, in which sleep is not only induced by Aß but is also required for Aß-mediated plasticity, underlining the link between sleep and AD. Together, these results strongly argue that AD is a disease of competitive synaptic plasticity gone awry, a novel perspective that may promote AD research.

The molecular signals that regulate activity-dependent synapse refinement in the brain.

The formation of appropriate synaptic connections is critical for the proper functioning of the brain. Early in development, neurons form a surplus of immature synapses. To establish efficient, functional neural networks, neurons selectively stabilize active synapses and eliminate less active ones. This process is known as activity-dependent synapse refinement. Defects in this process have been implicated in neuropsychiatric disorders such as schizophrenia and autism. Here we review the manner and mechanisms by which synapse elimination is regulated through activity-dependent competition. We propose a theoretical framework for the molecular mechanisms of synapse refinement, in which three types of signals regulate the refinement. We then describe the identity of these signals and discuss how multiple molecular signals interact to achieve appropriate synapse refinement in the brain.

Competition on presynaptic resources enhances the discrimination of interfering memories.

Evidence suggests that hippocampal adult neurogenesis is critical for discriminating considerably interfering memories. During adult neurogenesis, synaptic competition modifies the weights of synaptic connections nonlocally across neurons, thus providing a different form of unsupervised learning from Hebb's local plasticity rule. However, how synaptic competition achieves separating similar memories largely remains unknown. Here, we aim to link synaptic competition with such pattern separation. In synaptic competition, adult-born neurons are integrated into the existing neuronal pool by competing with mature neurons for synaptic connections from the entorhinal cortex. We show that synaptic competition and neuronal maturation play distinct roles in separating interfering memory patterns. Furthermore, we demonstrate that a feedforward neural network trained by a competition-based learning rule can outperform a multilayer perceptron trained by the backpropagation algorithm when only a small number of samples are available. Our results unveil the functional implications and potential applications of synaptic competition in neural computation.

Activity-dependent local protection and lateral inhibition control synaptic competition in developing mitral cells in mice.

In developing brains, activity-dependent remodeling facilitates the formation of precise neuronal connectivity. Synaptic competition is known to facilitate synapse elimination; however, it has remained unknown how different synapses compete with one another within a post-synaptic cell. Here, we investigate how a mitral cell in the mouse olfactory bulb prunes all but one primary dendrite during the developmental remodeling process. We find that spontaneous activity generated within the olfactory bulb is essential. We show that strong glutamatergic inputs to one dendrite trigger branch-specific changes in RhoA activity to facilitate the pruning of the remaining dendrites: NMDAR-dependent local signals suppress RhoA to protect it from pruning; however, the subsequent neuronal depolarization induces neuron-wide activation of RhoA to prune non-protected dendrites. NMDAR-RhoA signals are also essential for the synaptic competition in the mouse barrel cortex. Our results demonstrate a general principle whereby activity-dependent lateral inhibition across synapses establishes a discrete receptive field of a neuron.

The synaptic basis of activity-dependent eye-specific competition.

Binocular vision requires proper developmental wiring of eye-specific inputs to the brain. In the thalamus, axons from the two eyes initially overlap in the dorsal lateral geniculate nucleus and undergo activity-dependent competition to segregate into target domains. Here, we combine eye-specific tract tracing with volumetric super-resolution imaging to measure the nanoscale molecular reorganization of developing retinogeniculate eye-specific synapses in the mouse brain. We show there are eye-specific differences in presynaptic vesicle pool size and vesicle association with the active zone at the earliest stages of retinogeniculate refinement but find no evidence of eye-specific differences in subsynaptic domain number, size, or transsynaptic alignment across development. Genetic disruption of spontaneous retinal activity decreases retinogeniculate synapse density, delays the emergence eye-specific differences in vesicle organization, and disrupts subsynaptic domain maturation. These results suggest that activity-dependent eye-specific presynaptic maturation underlies synaptic competition in the mammalian visual system.

Simplified calcium signaling cascade for synaptic plasticity.

We propose a model for synaptic plasticity based on a calcium signaling cascade. The model simplifies the full signaling pathways from a calcium influx to the phosphorylation (potentiation) and dephosphorylation (depression) of glutamate receptors that are gated by fictive C1 and C2 catalysts, respectively. This model is based on tangible chemical reactions, including fictive catalysts, for long-term plasticity rather than the conceptual theories commonplace in various models, such as preset thresholds of calcium concentration. Our simplified model successfully reproduced the experimental synaptic plasticity induced by different protocols such as (i) a synchronous pairing protocol and (ii) correlated presynaptic and postsynaptic action potentials (APs). Further, the ocular dominance plasticity (or the experimental verification of the celebrated Bienenstock-Cooper-Munro theory) was reproduced by two model synapses that compete by means of back-propagating APs (bAPs). The key to this competition is synapse-specific bAPs with reference to bAP-boosting on the physiological grounds.

Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot.

Development of spiking neural networks (SNNs) controlling mobile robots is one of the modern challenges in computational neuroscience and artificial intelligence. Such networks, being replicas of biological ones, are expected to have a higher computational potential than traditional artificial neural networks (ANNs). The critical problem is in the design of robust learning algorithms aimed at building a "living computer" based on SNNs. Here, we propose a simple SNN equipped with a Hebbian rule in the form of spike-timing-dependent plasticity (STDP). The SNN implements associative learning by exploiting the spatial properties of STDP. We show that a LEGO robot controlled by the SNN can exhibit classical and operant conditioning. Competition of spike-conducting pathways in the SNN plays a fundamental role in establishing associations of neural connections. It replaces the irrelevant associations by new ones in response to a change in stimuli. Thus, the robot gets the ability to relearn when the environment changes. The proposed SNN and the stimulation protocol can be further enhanced and tested in developing neuronal cultures, and also admit the use of memristive devices for hardware implementation.

Purinergic-Dependent Glial Regulation of Synaptic Plasticity of Competing Terminals and Synapse Elimination at the Neuromuscular Junction.

The precise wiring of synaptic connections requires the elimination of supernumerary inputs competing for innervation of the same target cell. This competition is activity-dependent, strengthening some inputs whereas others are eliminated. Although glial cells are required for the elimination and clearance of terminals, their involvement in activity-dependent synaptic competition remains ill-defined. Here, we used the developing neuromuscular junctions of mice to show that perisynaptic glial cells, through 2Y1 purinergic receptors (P2Y1Rs), decode synaptic efficacy of competing terminals in a Ca2+-dependent manner. This glial activity induces long-lasting synaptic potentiation of strong but not weak terminals via presynaptic adenosine 2A receptors. Blockade of glial activity by intracellular Ca2+ chelation or blockade of P2Y1Rs prevents this plasticity. In addition, blockade of P2Y1Rs delays synapse elimination in vivo. Hence, P2Y1Rs drive glial cell regulation of strong synaptic inputs and influence synapse competition and elimination.

Similar synapse elimination motifs at successive relays in the same efferent pathway during development in mice.

In many parts of the nervous system, signals pass across multiple synaptic relays on their way to a destination, but little is known about how these relays form and the function they serve. To get some insight into this question we ask how the connectivity patterns are organized at two successive synaptic relays in a simple, cholinergic efferent pathway. We found that the organization at successive relays in the parasympathetic nervous system strongly resemble each other despite the different embryological origin and physiological properties of the pre- and postsynaptic cells. Additionally, we found a similar developmental synaptic pruning and elaboration strategy is used at both sites to generate their adult organizations. The striking parallels in adult innervation and developmental mechanisms at the relays argue that a general strategy is in operation. We discuss why from a functional standpoint this structural organization may amplify central signals while at the same time maintaining positional targeting.

Homeostatic Plasticity Achieved by Incorporation of Random Fluctuations and Soft-Bounded Hebbian Plasticity in Excitatory Synapses.

Homeostatic plasticity is considered to maintain activity in neuronal circuits within a functional range. In the absence of homeostatic plasticity neuronal activity is prone to be destabilized because Hebbian plasticity mechanisms induce positive feedback change. Several studies on homeostatic plasticity assumed the existence of a process for monitoring neuronal activity on a time scale of hours and adjusting synaptic efficacy by scaling up and down. However, the underlying mechanism still remains unclear. Excitatory synaptic efficacy is associated with the size of the dendritic spine, and dendritic spine size fluctuates even after neuronal activity is silenced. These fluctuations could be a non-Hebbian form of synaptic plasticity that serves such a homeostatic function. This study proposed and analyzed a synaptic plasticity model incorporating random fluctuations and soft-bounded Hebbian plasticity at excitatory synapses, and found that the proposed model can prevent excessive changes in neuronal activity by scaling synaptic efficacy up and down. Soft-bounded Hebbian plasticity suppresses strong synapses, thereby scaling synapses down and preventing runaway excitation. Random fluctuations diffuse synaptic efficacy, thereby scaling synapses up and preventing neurons from falling silent. The proposed model acts as a form of homeostatic plasticity, regardless of neuronal activity monitoring.