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Found 13 papers, 1 papers with code
Investigation of event-based memory surfaces for high-speed tracking, unsupervised feature extraction and object recognition
This variance motivated the investigation of event-based decaying memory surfaces in comparison to time-based decaying memory surfaces to capture the temporal aspect of the event-based data.
A Stochastic Approach to STDP
The decay generator will then generate an exponential decay, which will be used by the STDP adaptor to perform the weight adaption.
A Reconfigurable Mixed-signal Implementation of a Neuromorphic ADC
The architecture consists of an analogue chip and a control module.
A compact aVLSI conductance-based silicon neuron
We present an analogue Very Large Scale Integration (aVLSI) implementation that uses first-order lowpass filters to implement a conductance-based silicon neuron for high-speed neuromorphic systems.
A neuromorphic hardware architecture using the Neural Engineering Framework for pattern recognition
The architecture is not limited to handwriting recognition, but is generally applicable as an extremely fast pattern recognition processor for various kinds of patterns such as speech and images.
A Trainable Neuromorphic Integrated Circuit that Exploits Device Mismatch
Additionally, we characterise each neuron and discuss the statistical variability of its tuning curve that arises due to random device mismatch, a desirable property for the learning capability of the TAB.
An Online Learning Algorithm for Neuromorphic Hardware Implementation
We propose a sign-based online learning (SOL) algorithm for a neuromorphic hardware framework called Trainable Analogue Block (TAB).
A neuromorphic hardware framework based on population coding
These neuronal populations are characterised by a diverse distribution of tuning curves, ensuring that the entire range of input stimuli is encoded.
FPGA Implementation of the CAR Model of the Cochlea
Here, we implement the Cascade of Asymmetric Resonators (CAR) model of the cochlea on an FPGA.
Turn Down that Noise: Synaptic Encoding of Afferent SNR in a Single Spiking Neuron
We have added a simplified neuromorphic model of Spike Time Dependent Plasticity (STDP) to the Synapto-dendritic Kernel Adapting Neuron (SKAN).
Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron with Adaptive Kernels
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns.
Delay Learning Architectures for Memory and Classification
We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns by changing the delays of every connection as opposed to modifying the weights.
The Ripple Pond: Enabling Spiking Networks to See
In this paper we present the biologically inspired Ripple Pond Network (RPN), a simply connected spiking neural network that, operating together with recently proposed PolyChronous Networks (PCN), enables rapid, unsupervised, scale and rotation invariant object recognition using efficient spatio-temporal spike coding.
