A biologically inspired neuronal network that stores and recognizes temporal sequences of symbols is described. Each symbol is represented by excitatory input to distinct groups of neurons (symbol pools). Unambiguous storage of multiple sequences with common subsequences is ensured by partitioning each symbol pool into subpools that respond only when the current symbol has been preceded by a pa...

The development of a mathematical model of dendritic neurons was begun because of a need to combine three different kinds of knowledge into a coherent theory. First, we have the anatomical concept of the neuron, together with the anatomical fact that extensive dendritic branching is characteristic of many important neuron types. Second, we have the theoretical development of the nerve membrane ...

In this paper we present a functional model of spiking neuron intended for hardware implementation. The model allows the design of speedand/or area-optimized architectures. Some features of biological spiking neurons are abstracted, while preserving the functionality of the network, in order to define an architecture easily implementable in hardware, mainly in field programmable gate arrays (FP...

We propose a delayed random walk model of stochastic single neuron which has a delayed self{exciting feedback. Using computer simulation the model is studied to examine how the delay in the self{excitation aaects the bistable stochastic dynamics of the model neuron. It is found that progressive delay causes the width of two bimodal peaks of the stationary probability distribution to increase, a...

Modeling spike firing assumes that spiking statistics is Poisson, but real data violates this assumption. To capture non-Poissonian features, in order to fix the inevitable inherent irregularity, researchers rescale the time axis with tedious computational overhead instead of searching for another distribution! Spikes or action potentials are precisely-timed changes in the ionic transport throu...

Chaos provides many interesting properties that can be used to achieve computational tasks. Such properties are sensitivity to initial conditions, space filling, control and synchronization. Chaotic neural models have been devised to exploit such properties. In this paper, a chaotic spiking neuron model is investigated experimentally. This investigation is performed to understand the dynamic be...

A typical human brain consists of roughly 100 billion neurons, and one key aim of Biological Cybernetics is to simulate neural systems. A good model of a neuron accurately represents the behaviour of biological neurons, typically the spiking behaviour. For cybernetic systems that aim to function in real time with thousands, millions, or even billions of simulated neurons, it is also important t...

Neuronal firing and neuron-to-neuron synaptic wiring are currently widely described as orchestrated by astrocytes—elaborately ramified glial cells tiling the cortical hippocampal space into non-overlapping domains, each covering hundreds of individual dendrites thousands synapses. A key component to astrocytic signaling is dynamics cytosolic Ca 2+ which displays multiscale spatiotemporal patter...

To explore the link between bioenergetics and motor neuron degeneration, we used a computational model in which detailed morphology and ion conductance are paired with intracellular ATP production and consumption. We found that reduced ATP availability increases the metabolic cost of a single action potential and disrupts K+/Na+ homeostasis, resulting in a chronic depolarization. The magnitude ...

1. Every theory is based on some simplifications of observed phenomena. This simplified abstract phenomenon is usually called a "model". Depending on the level of simplification the model enables us to predict more or less accurately the original situation. The scientist who tries to describe a physical phenomenon in terms of mathematical equations, or, more generally, by means of some mathemat...