Spiking Neural Networks are powerful computational modelling tools that have attracted
much interest because of the bioinspired modelling of synaptic interactions between …

Membrane computing is a discipline that aims to perform computation by mimicking nature
at the cellular level. Spiking Neural P (in short, SN P) systems are a subset of membrane …

Background Finding appropriate model parameters for multi-compartmental neuron models
can be challenging. Parameters such as the leak and axial conductance are not always …

Hierarchical Temporal Memory (HTM) is a computational model of the neocortex that is
capable of online learning to predict and detect anomalies from continuous data streams. To …

Membrane computing is a discipline that aims to perform computation by mimicking nature
at the cellular level. Spiking Neural P (in short, SN P) systems are a subset of membrane …

Membrane computing is a discipline that aims to perform computation by mimicking nature
at the cellular level. Spiking Neural P (in short, SN P) systems are a subset of membrane …

Abstract Artificial Neural Networks (ANNs) have been developed to mimic the dynamical
biological behavior of the brain. ANNs have been implemented to solve different kinds of …

Memristors are passive elements that allow us to store information using a single element
per bit. However, this is not the only utility of the memristor. Considering the physical …

dc. description. abstract The ability of a robot to adapt in-mission to achieve an assigned
goal is highly desirable. This thesis project places an emphasis on employing learning …

Spiking neural networks (SNNs) attempt to computationally model biological neurons. While
similar to artificial neural networks (ANNs), SNNs preserve the temporal and binary aspects …