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Found 20 papers, 3 papers with code
Biologically-inspired Salience Affected Artificial Neural Network (SANN)
In this paper we introduce a novel Salience Affected Artificial Neural Network (SANN) that models the way neuromodulators such as dopamine and noradrenaline affect neural dynamics in the human brain by being distributed diffusely through neocortical regions, allowing both salience signals to modulate cognition immediately, and one time learning to take place through strengthening entire patterns of activation at one go.
Event-based Feature Extraction Using Adaptive Selection Thresholds
A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations.
EMNIST: an extension of MNIST to handwritten letters
The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems.
Ranked #6 on
Image Classification
on EMNIST-Digits
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.
Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm
Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.
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.
Learning ELM network weights using linear discriminant analysis
We present an alternative to the pseudo-inverse method for determining the hidden to output weight values for Extreme Learning Machines performing classification tasks.
Explicit Computation of Input Weights in Extreme Learning Machines
In the absence of supervised training for the input weights, random linear combinations of training data samples are used to project the input data to a higher dimensional hidden layer.
ELM Solutions for Event-Based Systems
The modifications involve the re-definition of hidden layer units as synaptic kernels, in which the input delta functions are transformed into continuous-valued signals using a variety of impulse-response functions.
Online and Adaptive Pseudoinverse Solutions for ELM Weights
The ELM method has become widely used for classification and regressions problems as a result of its accuracy, simplicity and ease of use.
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.
