Static Synapses Research Articles

What makes memories last—dynamic ensembles or static synapses?

What makes memories last—dynamic ensembles or static synapses?

Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices

Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses but also how long it takes to instantiate the network model in computer memory. On the hardware side, acceleration via highly parallel GPUs is being increasingly utilized. On the software side, code generation approaches ensure highly optimized code at the expense of repeated code regeneration and recompilation after modifications to the network model. Aiming for a greater flexibility with respect to iterative model changes, here we propose a new method for creating network connections interactively, dynamically, and directly in GPU memory through a set of commonly used high-level connection rules. We validate the simulation performance with both consumer and data center GPUs on two neuroscientifically relevant models: a cortical microcircuit of about 77,000 leaky-integrate-and-fire neuron models and 300 million static synapses, and a two-population network recurrently connected using a variety of connection rules. With our proposed ad hoc network instantiation, both network construction and simulation times are comparable or shorter than those obtained with other state-of-the-art simulation technologies while still meeting the flexibility demands of explorative network modeling.

A scalable implementation of the recursive least-squares algorithm for training spiking neural networks.

Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a popular way to study computations performed by the nervous system. As the size and complexity of neural recordings increase, there is a need for efficient algorithms that can train models in a short period of time using minimal resources. We present optimized CPU and GPU implementations of the recursive least-squares algorithm in spiking neural networks. The GPU implementation can train networks of one million neurons, with 100 million plastic synapses and a billion static synapses, about 1,000 times faster than an unoptimized reference CPU implementation. We demonstrate the code's utility by training a network, in less than an hour, to reproduce the activity of > 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables a more interactive in-silico study of the dynamics and connectivity underlying multi-area computations. It also admits the possibility to train models as in-vivo experiments are being conducted, thus closing the loop between modeling and experiments.

Double inverse stochastic resonance with dynamic synapses.

We investigate the behavior of a model neuron that receives a biophysically realistic noisy postsynaptic current based on uncorrelated spiking activity from a large number of afferents. We show that, with static synapses, such noise can give rise to inverse stochastic resonance (ISR) as a function of the presynaptic firing rate. We compare this to the case with dynamic synapses that feature short-term synaptic plasticity and show that the interval of presynaptic firing rate over which ISR exists can be extended or diminished. We consider both short-term depression and facilitation. Interestingly, we find that a double inverse stochastic resonance (DISR), with two distinct wells centered at different presynaptic firing rates, can appear.

In Vivo Killing Capacity of Cytotoxic T Cells Is Limited and Involves Dynamic Interactions and T Cell Cooperativity

SummaryAccording to in vitro assays, T cells are thought to kill rapidly and efficiently, but the efficacy and dynamics of cytotoxic T lymphocyte (CTL)-mediated killing of virus-infected cells in vivo remains elusive. We used two-photon microscopy to quantify CTL-mediated killing in mice infected with herpesviruses or poxviruses. On average, one CTL killed 2–16 virus-infected cells per day as determined by real-time imaging and by mathematical modeling. In contrast, upon virus-induced MHC class I downmodulation, CTLs failed to destroy their targets. During killing, CTLs remained migratory and formed motile kinapses rather than static synapses with targets. Viruses encoding the calcium sensor GCaMP6s revealed strong heterogeneity in individual CTL functional capacity. Furthermore, the probability of death of infected cells increased for those contacted by more than two CTLs, indicative of CTL cooperation. Thus, direct visualization of CTLs during killing of virus-infected cells reveals crucial parameters of CD8+ T cell immunity.

Исследование спонтанной активности в модели нейрональной культуры с долговременной пластичностью

Existence of spontaneous population bursts is a widely studied phenomenon observed in neuronal cultures in vitro. Recent models of neuronal cultures network activity consist of a number of burst generating mechanisms such as synaptic noise and presence of pacemaker neurons in the network. In the previous simulations of bursting in neuronal cultures synaptic weights change in accordance with the rule of short-term plasticity whereas the long-term values of them, and hence the network structure, remain unchanged. In this paper we reproduce neuronal network models with static synapses, and then investigate spontaneous activity changes in neuronal networks with long-term plasticity defined by STDP rule. Our results demonstrate that introduction of long-term plasticity in the model leads to discrepancy with the experimental data.

Irregular Dynamics in Up and Down Cortical States

Complex coherent dynamics is present in a wide variety of neural systems. A typical example is the voltage transitions between up and down states observed in cortical areas in the brain. In this work, we study this phenomenon via a biologically motivated stochastic model of up and down transitions. The model is constituted by a simple bistable rate dynamics, where the synaptic current is modulated by short-term synaptic processes which introduce stochasticity and temporal correlations. A complete analysis of our model, both with mean-field approaches and numerical simulations, shows the appearance of complex transitions between high (up) and low (down) neural activity states, driven by the synaptic noise, with permanence times in the up state distributed according to a power-law. We show that the experimentally observed large fluctuation in up and down permanence times can be explained as the result of sufficiently noisy dynamical synapses with sufficiently large recovery times. Static synapses cannot account for this behavior, nor can dynamical synapses in the absence of noise.

Maximum Memory Capacity on Neural Networks with Short-Term Synaptic Depression and Facilitation

In this work, we study, analytically and employing Monte Carlo simulations, the influence of the competition between several activity-dependent synaptic processes, such as short-term synaptic facilitation and depression, on the maximum memory storage capacity in a neural network. In contrast to the case of synaptic depression, which drastically reduces the capacity of the network to store and retrieve "static" activity patterns, synaptic facilitation enhances the storage capacity in different contexts. In particular, we found optimal values of the relevant synaptic parameters (such as the neurotransmitter release probability or the characteristic facilitation time constant) for which the storage capacity can be maximal and similar to the one obtained with static synapses, that is, without activity-dependent processes. We conclude that depressing synapses with a certain level of facilitation allow recovering the good retrieval properties of networks with static synapses while maintaining the nonlinear characteristics of dynamic synapses, convenient for information processing and coding.

Subthreshold cross-correlations between cortical neurons: A reference model with static synapses

The structure of cross-correlations between subthreshold potentials of neocortical neurons was recently examined. Characteristic features included broad widths and significant peak advances. It was suggested that dynamic synapses shape these cross-correlations. Here a reference model is developed comprising leaky integrators with static synapses. The forms of the subthreshold correlations are derived analytically for two different forms of synaptic input: steady drive and populations bursts. For the latter case the model captures the widths seen in experiment. However, the model could not account for the peak advance. It is concluded that models with static synapses lack the necessary biological details for describing cortical dynamics.

Coincidence detection with dynamic synapses.

Recent experimental findings show that the efficacy of transmission in cortical synapses depends on presynaptic activity. In most neural models, however, the synapses are regarded as static entities where this dependence is not included. We study the role of activity-dependent (dynamic) synapses in neuronal responses to temporal patterns of afferent activity. Our results demonstrate that, for suitably chosen threshold values, dynamic synapses are capable of coincidence detection (CD) over a much larger range of frequencies than static synapses. The phenomenon appears to be valid for an integrate-and-fire as well as a Hodgkin-Huxley neuron and various types of CD tasks.