Two-trace model for spike-timing-dependent synaptic plasticity

Ketone Body Rescued Seizure Behavior of LRP1 Deficiency in Drosophila by Modulating Glutamate Transport

LRP1, the low-density lipoprotein receptor 1, would be a novel candidate gene of epilepsy according to our bioinformatic results and the animal study. In this study, we explored the role of LRP1 in epilepsy and whether beta-hydroxybutyrate, the principal ketone body of the ketogenic diet, can treat epilepsy caused by LRP1 deficiency in drosophila. UAS/GAL4 system was used to establish different genotype models. Flies were given standard, high-sucrose, and ketone body food randomly. The bang-sensitive test was performed on flies and seizure-like behavior was assessed. In morphologic experiments, we found that LRP1 deficiency caused partial loss of the ellipsoidal body and partial destruction of the fan-shaped body. Whole-body and glia LRP1 defect flies had a higher seizure rate compared to the control group. Ketone body decreased the seizure rate in behavior test in all LRP1 defect flies, compared to standard and high sucrose diet. Overexpression of glutamate transporter gene Eaat1 could mimic the ketone body effect on LRP1 deficiency flies. This study demonstrated that LRP1 defect globally or in glial cells or neurons could induce epilepsy in drosophila. The ketone body efficaciously rescued epilepsy caused by LRP1 knockdown. The results support screening for LRP1 mutations as discriminating conduct for individuals who require clinical attention and further clarify the mechanism of the ketogenic diet in epilepsy, which could help epilepsy patients make a precise treatment case by case.

Supervised Learning in SNN via Reward-Modulated Spike-Timing-Dependent Plasticity for a Target Reaching Vehicle

Spiking neural networks (SNNs) offer many advantages over traditional artificial neural networks (ANNs) such as biological plausibility, fast information processing, and energy efficiency. Although SNNs have been used to solve a variety of control tasks using the Spike-Timing-Dependent Plasticity (STDP) learning rule, existing solutions usually involve hard-coded network architectures solving specific tasks rather than solving different kinds of tasks generally. This results in neglecting one of the biggest advantages of ANNs, i.e., being general-purpose and easy-to-use due to their simple network architecture, which usually consists of an input layer, one or multiple hidden layers and an output layer. This paper addresses the problem by introducing an end-to-end learning approach of spiking neural networks constructed with one hidden layer and reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) synapses in an all-to-all fashion. We use the supervised reward-modulated Spike-Timing-Dependent-Plasticity learning rule to train two different SNN-based sub-controllers to replicate a desired obstacle avoiding and goal approaching behavior, provided by pre-generated datasets. Together they make up a target-reaching controller, which is used to control a simulated mobile robot to reach a target area while avoiding obstacles in its path. We demonstrate the performance and effectiveness of our trained SNNs to achieve target reaching tasks in different unknown scenarios.

Drifting States and Synchronization Induced Chaos in Autonomous Networks of Excitable Neurons

The study of balanced networks of excitatory and inhibitory neurons has led to several open questions. On the one hand it is yet unclear whether the asynchronous state observed in the brain is autonomously generated, or if it results from the interplay between external drivings and internal dynamics. It is also not known, which kind of network variabilities will lead to irregular spiking and which to synchronous firing states. Here we show how isolated networks of purely excitatory neurons generically show asynchronous firing whenever a minimal level of structural variability is present together with a refractory period. Our autonomous networks are composed of excitable units, in the form of leaky integrators spiking only in response to driving currents, remaining otherwise quiet. For a non-uniform network, composed exclusively of excitatory neurons, we find a rich repertoire of self-induced dynamical states. We show in particular that asynchronous drifting states may be stabilized in purely excitatory networks whenever a refractory period is present. Other states found are either fully synchronized or mixed, containing both drifting and synchronized components. The individual neurons considered are excitable and hence do not dispose of intrinsic natural firing frequencies. An effective network-wide distribution of natural frequencies is however generated autonomously through self-consistent feedback loops. The asynchronous drifting state is, additionally, amenable to an analytic solution. We find two types of asynchronous activity, with the individual neurons spiking regularly in the pure drifting state, albeit with a continuous distribution of firing frequencies. The activity of the drifting component, however, becomes irregular in the mixed state, due to the periodic driving of the synchronized component. We propose a new tool for the study of chaos in spiking neural networks, which consists of an analysis of the time series of pairs of consecutive interspike intervals. In this space, we show that a strange attractor with a fractal dimension of about 1.8 is formed in the mentioned mixed state.