… as neural codes for associated memory items. Here we study the emergence of neural codes
for associated memory items in … model of recurrent networks of spiking neurons with a data-…

… memory in the form of a spiking neural network (SNN), an application of the associative
memory, … The model is created in a neural network simulator and run on a low SWaP CPU and In…

… the MIZH spiking neuron model exhibited the typical biological neuron functions and remarkably
… the IZH spiking neuron model. The 23 spiking patterns of cerebral cortical neurons were …

… The novelty of our model is that we associate memory with item-specific patterns of
astrocyte-induced enhancement of excitatory synaptic transmission. We present a new case of how …

… spiking bidirectional associative memory (BAM) method is presented to establish a cognitive
environment. The spiking BAM network … is obtained by a spiking neural network (SNN) with …

… Modeling the neuronal processes underlying short-term working memory remains the focus
… In this paper, we propose a mathematical model of a spiking neural network (SNN) which …

… First, we pre-train the spiking neuronal network consisting of pyramidal neurons and … During
pre-training, each P pattern is presented to the neuronal network 10 times in random order (…

… short-term memory implemented by a two-net spiking neural-astrocytic network. Our results
… of neuronal activity provides an enhancement of retrieval quality in comparison to standard …

… to simulate synapses in artificial neural networks. In this paper, based on the proposed
associative memory rules, we design a memristor neural network with plasticity synapses, which …

… for forming dense associative memory between various inputs. Designed with gated-RRAM
devices as synapses and CMOS-based spiking neurons, the simulated network was able to …