Are there Many Philosophies or is there Just ‘Doing Philosophy’?

The term ‘philosophy’ may be used in more than one sense to refer to both the subjective human activity of ‘doing philosophy’ and its result, namely the production of systems of thought – philosophical theories – which history demonstrates as many and various. It will be argued that there is only one way of doing philosophy and that this is proceeding from the common principles of the human mind in the search for truth of what is real. The mark of true philosophy is unity. A unity of true philosophy may be sought from what seems disparate: Aquinas embodies this effort towards synthesis, convinced that reality is unified and ordered. ‘Just doing philosophy’ is what Thomism embodies

Rotationally invariant vision recognition with neuromorphic transformation and learning networks

In this paper we present a biologically inspired rotationally-invariant end-to-end recognition system demonstrated in hardware with a bitmap camera and a Field Programmable Gate Array (FPGA). The system integrates the Ripple Pond Network (RPN), a neural network that performs image transformation from two dimensions to one dimensional rotationally invariant temporal patterns (TPs), and the Synaptic Kernel Adaptation Network (SKAN), a neural network capable of unsupervised learning of a spatio-temporal pattern of input spikes. Our results demonstrate rapid learning and recognition of simple hand gestures with no prior training and minimal usage of FPGA hardware

The Synaptic Kernel Adaptation Network

In this paper we present the Synaptic Kernel Adaptation Network (SKAN) circuit, a dynamic circuit that implements Spike Timing Dependent Plasticity (STDP), not by adjusting synaptic weights but via dynamic synaptic kernels. SKAN performs unsupervised learning of the commonest spatio-temporal pattern of input spikes using simple analog or digital circuits. It features tunable robustness to temporal jitter and will unlearn a pattern that has not been present for a period of time using tunable 'forgetting' parameters. It is compact and scalable for use as a building block in a larger network to form a multilayer hierarchical unsupervised memory system which develops models based on the temporal statistics of its environment. Here we show results from simulations as well present digital and analog implementations. Our results show that the SKAN is fast, accurate and robust to noise and jitter