Synaptic Efficacies Research Articles

Maintenance of synaptic plasticity by negative-feedback of synaptic protein elimination: Dynamic modeling of KIBRA-PKMζ interactions in LTP and memory.

Activity-dependent modifications of synaptic efficacies are a cellular substrate of learning and memory. Current theories propose that the long-term maintenance of synaptic efficacies and memory is accomplished via a positive-feedback loop at the level of production of a protein species or a protein state. Here we propose a qualitatively different theoretical framework based on negative-feedback at the level of protein elimination. This theory is motivated by recent experimental findings regarding the binding of PKMζ and KIBRA, two synaptic proteins involved in maintenance of memory, and on how this binding affects the proteins' degradation. We demonstrate this theoretical framework with two different models, a simple abstract model to explore generic features of such a process, and an experimentally motivated phenomenological model. The results of these models are qualitatively consistent with existing data, and generate novel predictions that could be experimentally tested to further validate or reject the negative-feedback theory.

Synaptic self-organization of spatio-temporal pattern selectivity.

Spiking model neurons can be set up to respond selectively to specific spatio-temporal spike patterns by optimization of their input weights. It is unknown, however, if existing synaptic plasticity mechanisms can achieve this temporal mode of neuronal coding and computation. Here it is shown that changes of synaptic efficacies which tend to balance excitatory and inhibitory synaptic inputs can make neurons sensitive to particular input spike patterns. Simulations demonstrate that a combination of Hebbian mechanisms, hetero-synaptic plasticity and synaptic scaling is sufficient for self-organizing sensitivity for spatio-temporal spike patterns that repeat in the input. In networks inclusion of hetero-synaptic plasticity that depends on the pre-synaptic neurons leads to specialization and faithful representation of pattern sequences by a group of target neurons. Pattern detection is robust against a range of distortions and noise. The proposed combination of Hebbian mechanisms, hetero-synaptic plasticity and synaptic scaling is found to protect the memories for specific patterns from being overwritten by ongoing learning during extended periods when the patterns are not present. This suggests a novel explanation for the long term robustness of memory traces despite ongoing activity with substantial synaptic plasticity. Taken together, our results promote the plausibility of precise temporal coding in the brain.

Maintenance of PKMζ-modulated synaptic efficacies despite protein turnover

How can synaptic efficacies be maintained despite the fast turnover of proteins at synapses? Partially, we know that the synthesis of new proteins is essential for the induction of the late, long-lasting phase of long-term potentiation (L-LTP). Recent experiments suggest that the concentration of protein kinase Mζ (PKMζ) is increased during L-LTP and that inhibiting the PKMζ activity during the maintenance phase can effectively reverse L-LTP. Experiments have also shown that phosphorylation is necessary for the activation of PKMζ. However, it is not clear what mechanism maintains the level and activity of PKMζ despite protein turnover and phosphatase activity. Using a mathematical modeling framework, I examine the hypothesis that the activity of PKMζ is sustained through a local switching mechanism. The model for the switching mechanism is motivated by several experimental observations: 1) PKMζ has two phosphorylation sites; one is mediated by another constitutively active kinase, Phosphoinositide-dependent kinase 1 PDK1 (T410) and is essential for its activity, and another is an autophosphorylation site, T560. 2) The phosphorylation of PKMζ increases its stability and the doubly phosphorylated PKMζ has a significantly longer lifetime than the unphosphorylated and singly phosphorylated states of PKMζ. 3) The doubly phosphorylated PKMζ also regulates the new synthesis of PKMζ through a translation feedback loop. The present study implemented a mass action model consistent with these observations. The results show that such a model can be bistable and that L-LTP induction produces an increase in the total amount of PKMζ at active synapses. The increase in PKMζ concentration was maintained through the regulation of new protein synthesis by PKMζ. The results also show that blocking the activity of PKMζ in a dose-dependent manner can effectively abolish the increase in the total amount of PKMζ, which is consistent with the effect that the PKMζ inhibitor zeta inhibitory peptide (ZIP) has experimentally demonstrated. The model is consistent with available experimental results regarding the phosphorylation levels of PKMζ and the temporal aspects of blocking experiments and produces a new prediction.

Learning accurate path integration in ring attractor models of the head direction system.

Ring attractor models for angular path integration have received strong experimental support. To function as integrators, head direction circuits require precisely tuned connectivity, but it is currently unknown how such tuning could be achieved. Here, we propose a network model in which a local, biologically plausible learning rule adjusts synaptic efficacies during development, guided by supervisory allothetic cues. Applied to the Drosophila head direction system, the model learns to path-integrate accurately and develops a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading in flies, and where the network remaps to integrate with different gains in rodents. Our model predicts that path integration requires self-supervised learning during a developmental phase, and proposes a general framework to learn to path-integrate with gain-1 even in architectures that lack the physical topography of a ring.

Conditions for Synaptic Specificity during the Maintenance Phase of Synaptic Plasticity.

Activity-dependent modifications of synaptic efficacies are a cellular substrate of learning and memory. Experimental evidence shows that these modifications are synapse specific and that the long-lasting effects are associated with the sustained increase in concentration of specific proteins like PKMζ. However, such proteins are likely to diffuse away from their initial synaptic location and spread out to neighboring synapses, potentially compromising synapse specificity. In this article, we address the issue of synapse specificity during memory maintenance. Assuming that the long-term maintenance of synaptic plasticity is accomplished by a molecular switch, we carry out analytical calculations and perform simulations using the reaction-diffusion package in NEURON to determine the limits of synapse specificity during maintenance. Moreover, we explore the effects of the diffusion and degradation rates of proteins and of the geometrical characteristics of dendritic spines on synapse specificity. We conclude that the necessary conditions for synaptic specificity during maintenance require that molecular switches reside in dendritic spines. The requirement for synaptic specificity when the molecular switch resides in spines still imposes strong limits on the diffusion and turnover of rates of maintenance molecules, as well as on the morphologic properties of synaptic spines. These constraints are quite general and apply to most existing models suggested for maintenance. The parameter values can be experimentally evaluated, and if they do not fit the appropriate predicted range, the validity of this class of maintenance models would be challenged.

Behavioral Time Scale Plasticity of Place Fields: Mathematical Analysis.

Traditional synaptic plasticity experiments and models depend on tight temporal correlations between pre- and postsynaptic activity. These tight temporal correlations, on the order of tens of milliseconds, are incompatible with significantly longer behavioral time scales, and as such might not be able to account for plasticity induced by behavior. Indeed, recent findings in hippocampus suggest that rapid, bidirectional synaptic plasticity which modifies place fields in CA1 operates at behavioral time scales. These experimental results suggest that presynaptic activity generates synaptic eligibility traces both for potentiation and depression, which last on the order of seconds. These traces can be converted to changes in synaptic efficacies by the activation of an instructive signal that depends on naturally occurring or experimentally induced plateau potentials. We have developed a simple mathematical model that is consistent with these observations. This model can be fully analyzed to find the fixed points of induced place fields and how these fixed points depend on system parameters such as the size and shape of presynaptic place fields, the animal's velocity during induction, and the parameters of the plasticity rule. We also make predictions about the convergence time to these fixed points, both for induced and pre-existing place fields.

Temporally coherent perturbation of neural dynamics during retention alters human multi-item working memory.

Temporarily storing a list of items in working memory (WM), a fundamental ability in cognition, has been posited to rely on the temporal dynamics of multi-item neural representations during retention. However, the causal evidence, particularly in human subjects, is still lacking, let alone WM manipulation. Here, we develop a novel "dynamic perturbation" approach to manipulate the relative memory strength of WM items held in human brain, by presenting temporally correlated luminance sequences during retention to interfere with the multi-item neural dynamics. Six experiments on more than 150 subjects confirm the effectiveness of this WM manipulation approach. A computational model combining continuous attractor neural network (CANN) and short-term synaptic plasticity (STP) principles further reproduces all the empirical findings. The model reveals that the "dynamic perturbation" modifies the synaptic efficacies of WM items through STP principles, eventually leading to changes in their relative memory strengths. Our results support the causal role of temporal dynamics of neural network in mediating multi-item WM, and offer a promising, purely bottom-up approach to manipulate WM.

From Topological Analyses to Functional Modeling: The Case of Hippocampus.

Topological data analyses are widely used for describing and conceptualizing large volumes of neurobiological data, e.g., for quantifying spiking outputs of large neuronal ensembles and thus understanding the functions of the corresponding networks. Below we discuss an approach in which convergent topological analyses produce insights into how information may be processed in mammalian hippocampus—a brain part that plays a key role in learning and memory. The resulting functional model provides a unifying framework for integrating spiking data at different timescales and following the course of spatial learning at different levels of spatiotemporal granularity. This approach allows accounting for contributions from various physiological phenomena into spatial cognition—the neuronal spiking statistics, the effects of spiking synchronization by different brain waves, the roles played by synaptic efficacies and so forth. In particular, it is possible to demonstrate that networks with plastic and transient synaptic architectures can encode stable cognitive maps, revealing the characteristic timescales of memory processing.

Structural Correlates of CA2 and CA3 Pyramidal Cell Activity in Freely-Moving Mice.

Plasticity within hippocampal circuits is essential for memory functions. The hippocampal CA2/CA3 region is thought to be able to rapidly store incoming information by plastic modifications of synaptic weights within its recurrent network. High-frequency spike-bursts are believed to be essential for this process, by serving as triggers for synaptic plasticity. Given the diversity of CA2/CA3 pyramidal neurons, it is currently unknown whether and how burst activity, assessed in vivo during natural behavior, relates to principal cell heterogeneity. To explore this issue, we juxtacellularly recorded the activity of single CA2/CA3 neurons from freely-moving male mice, exploring a familiar environment. In line with previous work, we found that spatial and temporal activity patterns of pyramidal neurons correlated with their topographical position. Morphometric analysis revealed that neurons with a higher proportion of distal dendritic length displayed a higher tendency to fire spike-bursts. We propose that the dendritic architecture of pyramidal neurons might determine burst-firing by setting the relative amount of distal excitatory inputs from the entorhinal cortex.SIGNIFICANCE STATEMENT High-frequency spike-bursts are thought to serve fundamental computational roles within neural circuits. Within hippocampal circuits, spike-bursts are believed to serve as potent instructive signals, which increase the efficiency of information transfer and induce rapid modifications of synaptic efficacies. In the present study, by juxtacellularly recording and labeling single CA2/CA3 neurons in freely-moving mice, we explored whether and how burst propensity relates to pyramidal cell heterogeneity. We provide evidence that, within the CA2/CA3 region, neurons with higher proportion of distal dendritic length display a higher tendency to fire spike-bursts. Thus, the relative amount of entorhinal inputs, arriving onto the distal dendrites, might determine the burst propensity of individual CA2/CA3 neurons in vivo during natural behavior.

Circuit mechanisms for the maintenance and manipulation of information in working memory.

Recently it has been proposed that information in working memory (WM) may not always be stored in persistent neuronal activity but can be maintained in 'activity-silent' hidden states, such as synaptic efficacies endowed with short-term synaptic plasticity. To test this idea computationally, we investigated recurrent neural network models trained to perform several WM-dependent tasks, in which WM representation emerges from learning and is not a priori assumed to depend on self-sustained persistent activity. We found that short-term synaptic plasticity can support the short-term maintenance of information, provided that the memory delay period is sufficiently short. However, in tasks that require actively manipulating information, persistent activity naturally emerges from learning, and the amount of persistent activity scales with the degree of manipulation required. These results shed insight into the current debate on WM encoding and suggest that persistent activity can vary markedly between short-term memory tasks with different cognitive demands.

Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits

Spiking neural networks, the most realistic artificial representation of biological nervous systems, are promising due to their inherent local training rules that enable low-overhead online learning, and energy-efficient information encoding. Their downside is more demanding functionality of the artificial synapses, notably including spike-timing-dependent plasticity, which makes their compact efficient hardware implementation challenging with conventional device technologies. Recent work showed that memristors are excellent candidates for artificial synapses, although reports of even simple neuromorphic systems are still very rare. In this study, we experimentally demonstrate coincidence detection using a spiking neural network, implemented with passively integrated metal-oxide memristive synapses connected to an analogue leaky-integrate-and-fire silicon neuron. By employing spike-timing-dependent plasticity learning, the network is able to robustly detect the coincidence by selectively increasing the synaptic efficacies corresponding to the synchronized inputs. Not surprisingly, our results indicate that device-to-device variation is the main challenge towards realization of more complex spiking networks.

Competition for synaptic building blocks shapes synaptic plasticity.

Changes in the efficacies of synapses are thought to be the neurobiological basis of learning and memory. The efficacy of a synapse depends on its current number of neurotransmitter receptors. Recent experiments have shown that these receptors are highly dynamic, moving back and forth between synapses on time scales of seconds and minutes. This suggests spontaneous fluctuations in synaptic efficacies and a competition of nearby synapses for available receptors. Here we propose a mathematical model of this competition of synapses for neurotransmitter receptors from a local dendritic pool. Using minimal assumptions, the model produces a fast multiplicative scaling behavior of synapses. Furthermore, the model explains a transient form of heterosynaptic plasticity and predicts that its amount is inversely related to the size of the local receptor pool. Overall, our model reveals logistical tradeoffs during the induction of synaptic plasticity due to the rapid exchange of neurotransmitter receptors between synapses.

Probing synaptic connectivity and function using high-density microelectrode arrays and whole-cell patch-clamp recordings

Event Abstract Back to Event Probing synaptic connectivity and function using high-density microelectrode arrays and whole-cell patch-clamp recordings Julian Bartram1*, Manuel Schröter1, Silvia Ronchi1, Vishalini Emmenegger1, Jan Müller1 and Andreas R. Hierlemann1 1 ETH Zürich, Department of Biosystems Science and Engineering, Switzerland Motivation Synaptic efficacy and synapse number of monosynaptic connections between neurons are often regulated by the spiking activity of the respective pre- and postsynaptic cell. Progress towards a better understanding of the rules and mechanisms that underlie such modifications has been limited due to the difficulties associated with simultaneously studying plasticity at multiple synaptic inputs. Here, we provide a solution to this problem by combining cutting-edge high-density microelectrode array (HD-MEA) technology with the patch-clamp technique. While the latter allows for accurate measurement of postsynaptic currents or potentials, evoked by individual synaptic activation, the HD-MEA technology provides large-scale information about unit activity and allows for selective stimulation of neurons, including multiple presynaptic cells. The proposed approach has been applied to comprehensively examine forms of homeostatic plasticity – a collection of crucial processes acting at different temporal scales in order to stabilize neuronal firing rates. We report on a characterization of classic synaptic scaling operating in mature cortical networks and propose a novel model for the study of homeostatic plasticity during natural network states. Material and Methods In this study, we investigated homeostatic plasticity in cultures of primary cortical neurons. Many variants of this form of plasticity were originally identified in reduced preparations, but have since been confirmed in vivo [1,2], which supports the choice of such a cell culture model system. Isolation, plating on MEA chips, and treatment of primary cortical neurons were previously described [3]. A complementary-metal-oxide-semiconductor- (CMOS) based HD-MEA system with 26’400 microelectrodes, an electrode pitch of 17.5 μm and 1024 reconfigurable readout channels [4] was employed for all extracellular recording and stimulation experiments. Patch-clamp recordings in current-clamp or voltage-clamp mode were conducted in conjunction with HD-MEA stimulation and recordings or, in some cases, independently. Homeostatic plasticity was initially induced by pharmacological alteration of global network spiking starting at 14 - 21 days in vitro. Effects on network spiking, induced in this way, were investigated by performing array-wide activity scans. Some of these results were further complemented with miniature excitatory postsynaptic current (mEPSC) recordings, which provide more direct information about synaptic efficacies. Selective stimulation of cells with the HD-MEA during whole-cell patch-clamp recordings enabled the assessment of synaptic strength at multiple inputs. Finally, recording of the spiking activity of all identified presynaptic cells and the postsynaptic neuron during the induction of homeostatic plasticity will permit the examination of correlations between spike patterns and changes in synaptic strength. Results Array-wide network activity was assessed before, during and after the induction of homeostatic plasticity with 12 – 48 h of tetrodotoxin (TTX; 1 μM) treatment. The relatively limited cell migration in mature cultures facilitated the tracking of units over the multiday recording period. Clear increases in network activity could be observed following the washing out of TTX relative to controls. mEPSC recordings from before and after TTX treatment suggest that this increase was due to homeostatic adjustments of excitatory synaptic efficacy. Interestingly, while the network remained in an elevated activity state for several hours following TTX withdrawal, spiking subsequently decreased within a 24 h period. This likely reflected a homeostatic down-regulation of synaptic strength due to the high network spiking activity. Finally, using the electrical stimulation capabilities of the HD-MEA system, we have successfully measured the strength of multiple synaptic inputs. Discussion We demonstrated in this study how the combination of HD-MEA technology and the patch-clamp technique provides unique abilities to study synaptic homeostasis, and synaptic plasticity in general, by providing crucial information about spontaneous global network spiking, exquisite control over spiking of individual cells, and the ability to record subthreshold postsynaptic responses. A homeostatic down-regulation of network activity and (excitatory) synaptic efficacy, following the elevation of network spiking activity by TTX treatment, is a very attractive form of homeostatic plasticity, as no artificial stimulus or agent (e. g. TTX) is present during the induction phase. Moreover, with global spiking activity being consistent with synaptic efficacies, the network is in a more natural state. We are now attempting to link the observed changes in synaptic efficacy with the respective pre- and postsynaptic spike patterns, recorded during the induction phase, promising new insights into the rules and mechanisms underlying homeostatic plasticity. Acknowledgements This work was supported by the ERC Advanced Grant 694829 ‘neuroXscales’.

Modulating STDP Balance Impacts the Dendritic Mosaic.

The ability for cortical neurons to adapt their input/output characteristics and information processing capabilities ultimately relies on the interplay between synaptic plasticity, synapse location, and the nonlinear properties of the dendrite. Collectively, they shape both the strengths and spatial arrangements of convergent afferent inputs to neuronal dendrites. Recent experimental and theoretical studies support a clustered plasticity model, a view that synaptic plasticity promotes the formation of clusters or hotspots of synapses sharing similar properties. We have previously shown that spike timing-dependent plasticity (STDP) can lead to synaptic efficacies being arranged into spatially segregated clusters. This effectively partitions the dendritic tree into a tessellated imprint which we have called a dendritic mosaic. Here, using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and STDP learning, we investigated the impact of altered STDP balance on forming such a spatial organization. We show that cluster formation and extend depend on several factors, including the balance between potentiation and depression, the afferents' mean firing rate and crucially on the dendritic morphology. We find that STDP balance has an important role to play for this emergent mode of spatial organization since any imbalances lead to severe degradation- and in some case even destruction- of the mosaic. Our model suggests that, over a broad range of of STDP parameters, synaptic plasticity shapes the spatial arrangement of synapses, favoring the formation of clustered efficacy engrams.

Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network.

Many experiments have suggested that the brain operates close to a critical state, based on signatures of criticality such as power-law distributed neuronal avalanches. In neural network models, criticality is a dynamical state that maximizes information processing capacities, e.g. sensitivity to input, dynamical range and storage capacity, which makes it a favorable candidate state for brain function. Although models that self-organize towards a critical state have been proposed, the relation between criticality signatures and learning is still unclear. Here, we investigate signatures of criticality in a self-organizing recurrent neural network (SORN). Investigating criticality in the SORN is of particular interest because it has not been developed to show criticality. Instead, the SORN has been shown to exhibit spatio-temporal pattern learning through a combination of neural plasticity mechanisms and it reproduces a number of biological findings on neural variability and the statistics and fluctuations of synaptic efficacies. We show that, after a transient, the SORN spontaneously self-organizes into a dynamical state that shows criticality signatures comparable to those found in experiments. The plasticity mechanisms are necessary to attain that dynamical state, but not to maintain it. Furthermore, onset of external input transiently changes the slope of the avalanche distributions – matching recent experimental findings. Interestingly, the membrane noise level necessary for the occurrence of the criticality signatures reduces the model’s performance in simple learning tasks. Overall, our work shows that the biologically inspired plasticity and homeostasis mechanisms responsible for the SORN’s spatio-temporal learning abilities can give rise to criticality signatures in its activity when driven by random input, but these break down under the structured input of short repeating sequences.

Semantic integration by pattern priming: experiment and cortical network model.

Neural network models describe semantic priming effects by way of mechanisms of activation of neurons coding for words that rely strongly on synaptic efficacies between pairs of neurons. Biologically inspired Hebbian learning defines efficacy values as a function of the activity of pre- and post-synaptic neurons only. It generates only pair associations between words in the semantic network. However, the statistical analysis of large text databases points to the frequent occurrence not only of pairs of words (e.g., "the way") but also of patterns of more than two words (e.g., "by the way"). The learning of these frequent patterns of words is not reducible to associations between pairs of words but must take into account the higher level of coding of three-word patterns. The processing and learning of pattern of words challenges classical Hebbian learning algorithms used in biologically inspired models of priming. The aim of the present study was to test the effects of patterns on the semantic processing of words and to investigate how an inter-synaptic learning algorithm succeeds at reproducing the experimental data. The experiment manipulates the frequency of occurrence of patterns of three words in a multiple-paradigm protocol. Results show for the first time that target words benefit more priming when embedded in a pattern with the two primes than when only associated with each prime in pairs. A biologically inspired inter-synaptic learning algorithm is tested that potentiates synapses as a function of the activation of more than two pre- and post-synaptic neurons. Simulations show that the network can learn patterns of three words to reproduce the experimental results.

Modulation of Functional Connectivity in Auditory-Motor Networks in Musicians Compared with Nonmusicians.

Correlation of spontaneous fluctuations at rest between anatomically distinct brain areas are proposed to reflect the profile of individual a priori cognitive biases, coded as synaptic efficacies in cortical networks. Here, we investigate functional connectivity at rest (rs-FC) in musicians and nonmusicians to test for differences in auditory, motor, and audiomotor connectivity. As expected, musicians had stronger rs-FC between the right auditory cortex (AC) and the right ventral premotor cortex than nonmusicians, and this stronger rs-FC was greater in musicians with more years of practice. We also found reduced rs-FC between the motor areas that control both hands in musicians compared with nonmusicians, which was more evident in the musicians whose instrument required bimanual coordination and as a function of hours of practice. Finally, we replicated previous morphometric data to show an increased volume in the right AC in musicians, which was greater in those with earlier musical training, and that this anatomic feature was in turn related to greater rs-FC between auditory and motor systems. These results show that functional coupling within the motor system and between motor and auditory areas is modulated as a function of musical training, suggesting a link between anatomic and functional brain features.

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

Plasticity-Driven Self-Organization under Topological Constraints Accounts for Non-random Features of Cortical Synaptic Wiring.

Understanding the structure and dynamics of cortical connectivity is vital to understanding cortical function. Experimental data strongly suggest that local recurrent connectivity in the cortex is significantly non-random, exhibiting, for example, above-chance bidirectionality and an overrepresentation of certain triangular motifs. Additional evidence suggests a significant distance dependency to connectivity over a local scale of a few hundred microns, and particular patterns of synaptic turnover dynamics, including a heavy-tailed distribution of synaptic efficacies, a power law distribution of synaptic lifetimes, and a tendency for stronger synapses to be more stable over time. Understanding how many of these non-random features simultaneously arise would provide valuable insights into the development and function of the cortex. While previous work has modeled some of the individual features of local cortical wiring, there is no model that begins to comprehensively account for all of them. We present a spiking network model of a rodent Layer 5 cortical slice which, via the interactions of a few simple biologically motivated intrinsic, synaptic, and structural plasticity mechanisms, qualitatively reproduces these non-random effects when combined with simple topological constraints. Our model suggests that mechanisms of self-organization arising from a small number of plasticity rules provide a parsimonious explanation for numerous experimentally observed non-random features of recurrent cortical wiring. Interestingly, similar mechanisms have been shown to endow recurrent networks with powerful learning abilities, suggesting that these mechanism are central to understanding both structure and function of cortical synaptic wiring.

Spatiotemporal dynamics in spiking simulations of superior colliculus fit via MCMC suggest disinhibition responsible for superlinear summation

The superior colliculus (SC) is a midbrain region with visually responsive neurons in the superficial layers and eye movement controlling neurons in the deeper layers. Recently, [1] performed in vitro experiments to elucidate lateral interactions within horizontal slices of the SC (Figure ​(Figure1A).1A). The experiments indicate that the superficial (visual) layers implement surround inhibition, and furthermore that strong stimulation at two adjacent locations (separation ~150 μm) produces an unexpected super-linear summation that is not seen in the deeper layers (Figure ​(Figure1B,1B, a+b). We used differential evolution Markov-chain Monte Carlo (MCMC) to estimate the parameters of a large-scale spiking neural circuit simulation to fit the slice data [2]. The model contains populations of inhibitory and excitatory neurons as well as input axons from retina/cortex. We included free anatomical (dendrite/axon spread) and dynamic (short-term synaptic plasticity) hyper-parameters in the model, and used MCMC estimate the posterior distribution of parameters that is most likely given the slice data. The resulting marginal distributions show promising agreement with verifiable anatomical parameters such as the lateral spread of dendrites and axons of the inhibitory and excitatory neuron populations in the superficial colliculus, even though no such constraints were coded into the model [2]. However, the posterior distributions for non-intuitive parameters (such as synaptic efficacies and facilitation/depression time constants) cannot be verified directly with existing data. Furthermore, it is not clear what the role of the dynamical parameters is in producing the behavior of the best-fit models, for example the local superlinearity described above. In this work, we take the additional step of analyzing the spatio-temporal dynamics of one of the best-fit regions of parameter space found via MCMC. The purpose is to provide a mechanistic explanation for the super-linear summation observed during two-point stimulation. Figure ​Figure1D1D shows the difference in spatio-temporal dynamics between actual two-point stimulation versus the linear sum of two independent stimulations. Thus, positive values indicate an increase in conductance sent to neurons at the horizontal axis position, arriving from neurons at the position indicated by the vertical axis (as explained in 1C). 16 ms from stimulation onset, there is a large increase in flow of inhibitory input from near the center of the circuit to inhibitory neurons all around the circuit, thus disinhibiting the circuit. 5 ms later, the excitatory neurons near the center receive input from excitatory neurons near the middle (i.e. recurrent activity), suggesting that cause for the super-linearity. Figure 1 (A) Experimental setup of [1], showing slice containing excitatory (red) and inhibitory (gray) neurons. Stimulation applied to electrodes at lateral distances while recording from an excitatory neuron. (B) Empirical results (dotted) and model fits. Purple: ...