Sensory experience plays an instructive role in the development of the nervous system. Here we showed that visual experience can induce persistent modification of developing retinotectal circuits via spike timing-dependent plasticity (STDP). Pairing light stimuli with spiking of the tectal cell induced persistent enhancement or reduction of light-evoked responses, with a dependence on the relat...

Understanding learning and memory mechanisms is an important goal in neuroscience. To gain insights into the underlying cellular mechanisms for memory formation, synaptic plasticity processes are studied with various techniques in different brain regions. A valid model to scrutinize different ways to enhance or decrease synaptic transmission is recording of long-term potentiation (LTP) or long-...

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with bac...

Spike timing dependent plasticity (STDP) has been observed experimentally in vitro and is a widely studied neural algorithm for synaptic modification. While the functional role of STDP has been investigated extensively, the effect of rhythms on the precise timing of STDP has not been characterized as well. We use a simplified biophysical model of a cortical network that generates pyramidal inte...

In the locust olfactory system multiple intrinsic and circuit neuronal properties act together, conveying coincidence detection attributes to the decoders of olfactory information Kenyon cells (KCs) of the mushroom body (MB). Acting as coincidence detectors, KCs respond to selective odor specific patterns of synchronization across antennal lobe (AL) projection neurons (PNs). Using a computer mo...

SpiNNaker is a digital neuromorphic architecture, designed specifically for the low power simulation of large-scale spiking neural networks at speeds close to biological real-time. Unlike other neuromorphic systems, SpiNNaker allows users to develop their own neuron and synapse models as well as specify arbitrary connectivity. As a result SpiNNaker has proved to be a powerful tool for studying ...

Learning rules, such as spike-timing-dependent plasticity (STDP), change the structure of networks of neurons based on the firing activity. A network level understanding of these mechanisms can help infer how the brain learns patterns and processes information. Previous studies have shown that STDP selectively potentiates feed-forward connections that have specific axonal delays, and that this ...

Spike-timing-dependent plasticity (STDP) is described by long-term potentiation (LTP), when a presynaptic event precedes a postsynaptic event, and by long-term depression (LTD), when the temporal order is reversed. In this article, we present a biophysical model of STDP based on a differential Hebbian learning rule (ISO learning). This rule correlates presynaptically the NMDA channel conductanc...

Metal-oxide memristors have emerged as promising candidates for hardware implementation of artificial synapses - the key components of high-performance, analog neuromorphic networks - due to their excellent scaling prospects. Since some advanced cognitive tasks require spiking neuromorphic networks, which explicitly model individual neural pulses ("spikes") in biological neural systems, it is c...

Calcium has been proposed as a postsynaptic signal underlying synaptic spike-timing-dependent plasticity (STDP). We examine this hypothesis with computational modeling based on experimental results from hippocampal cultures, some of which are presented here, in which pairs and triplets of pre- and postsynaptic spikes induce potentiation and depression in a temporally asymmetric way. Specificall...