The development of direction selectivity in the visual system depends on visual experience. In the developing Xenopus retinotectal system, tectal neurons (TNs) become direction selective through spike timing-dependent plasticity (STDP) after repetitive retinal exposure to a moving bar in a specific direction. We investigated the mechanism responsible for the development of direction selectivity...

We show that the local spike timing-dependent plasticity (STDP) rule has the effect of regulating the trans-synaptic weights of loops of any length within a simulated network of neurons. We show that depending on STDP's polarity, functional loops are formed or eliminated in networks driven to normal spiking conditions by random, partially correlated inputs, where functional loops comprise synap...

We present analog neuromorphic circuits for implementing bistable synapses with spike-timing-dependent plasticity (STDP) properties. In these types of synapses, the short-term dynamics of the synaptic efficacies are governed by the relative timing of the preand post-synaptic spikes, while on long time scales the efficacies tend asymptotically to either a potentiated state or to a depressed one....

We present test results from spike-timing correlation learning experiments carried out with silicon neurons with STDP (Spike Timing Dependent Plasticity) synapses. The weight change scheme of the STDP synapses can be set to either weight-independent or weight-dependent mode. We present results that characterise the learning window implemented for both modes of operation. When presented with spi...

Implementation of a correlation-based learning rule, SpikeTiming-Dependent-Plasticity (STDP), for asynchronous neuromorphic networks is demonstrated using ‘memristive’ nanodevice. STDP is performed using locally available information at the specific moment of time, for which mapping to crossbar-based CMOS-Nano architectures, such as CMOS-MOLecular (CMOL), is done rather easily. The learning met...

A neuromorphic chip with an array of neurons has connections (“synapses”) which implement a biological learning mechanism known as spike-timing-dependent plasticity (“STDP”). STDP is a homeostatic mechanism which regulates the firing rate of neurons. This mechanism is shown here to reduce variation in performance between neurons, due to both mismatch in fabrication and inhomogeneities in the el...

We develop the major steps taken to map a realistic spike timing-dependent plasticity (STDP) model into digital hardware architecture. Several types of mappings are implemented and tested on FPGA device. We compare their applicability to a real-time spiking neural network (SNN) simulator running in biological time-scale. Keywords—Spiking neural network, STDP, realistic models, real-time process...

In this paper, adaptive stochastic resonance in time-delayed Newman–Watts small-world neuronal networks is studied, where the strength of synaptic connections between neurons is adaptively modulated by spike-timing-dependent plasticity (STDP). Numerical results show that, in the absence of information transmission delay, the phenomenon of stochastic resonance occurs and the efficiency of networ...

A computationally rich algorithm of synaptic plasticity has been proposed based on the experimental observation that the sign and amplitude of the change in synaptic weight is dictated by the temporal order and temporal contiguity between pre- and postsynaptic activities. For more than a decade, this spike-timing-dependent plasticity (STDP) has been studied mainly in brain slices of different b...

Spike timing-dependent plasticity (STDP) has been proposed as one of the key mechanisms underlying learning and memory. Repetitive median nerve stimulation, followed by transcranial magnetic stimulation (TMS) of the contralateral primary motor cortex (M1), defined as paired-associative stimulation (PAS), has been used as an in vivo model of STDP in humans. PAS-induced excitability changes in M1...