Towards simulations of long-term behavior of neural networks: Modeling synaptic plasticity of connections within and between human brain regions

Random fluctuations and synaptic plasticity enhance working memory activities in the neuron-astrocyte network

Random fluctuations are inescapable feature in biological systems, but appropriate intensity of randomness can effectively facilitate information transfer and memory encoding within the nervous system. In the study, a modified spiking neuron-astrocyte network model with excitatory-inhibitory balance and synaptic plasticity is established. This model considers external input noise, and allows investigating the effects of intrinsic random fluctuations on working memory tasks. It is found that the astrocyte network, acting as a low-pass filter, reduces the noise component of the total input currents and improves the recovered images. The memory performance is enhanced by selecting appropriate intensity of random fluctuations, while excessive intensity can inhibit signal transmission of network. As the intensity of random fluctuations gradually increases, there exists a maximum value of the working memory performance. The cued recall of the network markedly decreases excessive input noise relative to test images. Meanwhile, a greater contrast effect is observed as the external input noise increases. In addition, synaptic plasticity reduces the firing rates and firing peaks of neurons, thus stabilizing the working memory activity during the test. The outcomes of this study may provide some inspirations for comprehending the role of random fluctuations in working memory mechanisms and neural information processing within the cerebral cortex.

Transcranial Direct Current Stimulation Enhances Neuroplasticity and Accelerates Motor Recovery in a Stroke Mouse Model

BACKGROUND

More effective strategies are needed to promote poststroke functional recovery. Here, we evaluated the impact of bihemispheric transcranial direct current stimulation (tDCS) on forelimb motor function recovery and the underlying mechanisms in mice subjected to focal ischemia of the motor cortex.

METHODS

Photothrombotic stroke was induced in the forelimb brain motor area, and tDCS was applied once per day for 3 consecutive days, starting 72 hours after stroke. Grid-walking, single pellet reaching, and grip strength tests were conducted to assess motor function. Local field potentials were recorded to evaluate brain connectivity. Western immunoblotting, ELISA, quantitative real-time polymerase chain reaction, and Golgi-Cox staining were used to uncover tDCS-mediated stroke recovery mechanisms.

RESULTS

Among our results, tDCS increased the rate of motor recovery, anticipating it at the early subacute stage. In this window, tDCS enhanced BDNF (brain-derived neurotrophic factor) expression and dendritic spine density in the peri-infarct motor cortex, along with increasing functional connectivity between motor and somatosensory cortices. Treatment with the BDNF TrkB (tropomyosin-related tyrosine kinase B) receptor inhibitor, ANA-12, prevented tDCS effects on motor recovery and connectivity as well as the increase of spine density, pERK (phosphorylated extracellular signal-regulated kinase), pCaMKII (phosphorylated calcium/calmodulin-dependent protein kinase II), pMEF (phosphorylated myocyte-enhancer factor), and PSD (postsynaptic density)-95. The tDCS-promoted rescue was paralleled by enhanced plasma BDNF level, suggesting its potential role as circulating prognostic biomarker.

CONCLUSIONS

The rate of motor recovery is accelerated by tDCS applied in the subacute phase of stroke. Anticipation of motor recovery via vicariate pathways or neural reserve recruitment would potentially enhance the efficacy of standard treatments, such as physical therapy, which is often delayed to a later stage when plastic responses are progressively lower.

FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency

Neural modelling tools are increasingly employed to describe, explain, and predict the human brain's behavior. Among them, spiking neural networks (SNNs) make possible the simulation of neural activity at the level of single neurons, but their use is often threatened by the resources needed in terms of processing capabilities and memory. Emerging applications where a low energy burden is required (e.g. implanted neuroprostheses) motivate the exploration of new strategies able to capture the relevant principles of neuronal dynamics in reduced and efficient models. The recent Leaky Integrate-and-Fire with Latency (LIFL) spiking neuron model shows some realistic neuronal features and efficiency at the same time, a combination of characteristics that may result appealing for SNN-based brain modelling. In this paper we introduce FNS, the first LIFL-based SNN framework, which combines spiking/synaptic modelling with the event-driven approach, allowing us to define heterogeneous neuron groups and multi-scale connectivity, with delayed connections and plastic synapses. FNS allows multi-thread, precise simulations, integrating a novel parallelization strategy and a mechanism of periodic dumping. We evaluate the performance of FNS in terms of simulation time and used memory, and compare it with those obtained with neuronal models having a similar neurocomputational profile, implemented in NEST, showing that FNS performs better in both scenarios. FNS can be advantageously used to explore the interaction within and between populations of spiking neurons, even for long time-scales and with a limited hardware configuration.