1,304 research outputs found
Evolving Spatio-temporal Data Machines Based on the NeuCube Neuromorphic Framework: Design Methodology and Selected Applications
The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include ‘on the fly’ new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this are presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM
Deep Unsupervised Learning Using Spike-Timing-Dependent Plasticity
Spike-Timing-Dependent Plasticity (STDP) is an unsupervised learning
mechanism for Spiking Neural Networks (SNNs) that has received significant
attention from the neuromorphic hardware community. However, scaling such local
learning techniques to deeper networks and large-scale tasks has remained
elusive. In this work, we investigate a Deep-STDP framework where a
convolutional network is trained in tandem with pseudo-labels generated by the
STDP clustering process on the network outputs. We achieve higher
accuracy and faster convergence speed at iso-accuracy on a 10-class
subset of the Tiny ImageNet dataset in contrast to a -means clustering
approach
Hardware-Amenable Structural Learning for Spike-based Pattern Classification using a Simple Model of Active Dendrites
This paper presents a spike-based model which employs neurons with
functionally distinct dendritic compartments for classifying high dimensional
binary patterns. The synaptic inputs arriving on each dendritic subunit are
nonlinearly processed before being linearly integrated at the soma, giving the
neuron a capacity to perform a large number of input-output mappings. The model
utilizes sparse synaptic connectivity; where each synapse takes a binary value.
The optimal connection pattern of a neuron is learned by using a simple
hardware-friendly, margin enhancing learning algorithm inspired by the
mechanism of structural plasticity in biological neurons. The learning
algorithm groups correlated synaptic inputs on the same dendritic branch. Since
the learning results in modified connection patterns, it can be incorporated
into current event-based neuromorphic systems with little overhead. This work
also presents a branch-specific spike-based version of this structural
plasticity rule. The proposed model is evaluated on benchmark binary
classification problems and its performance is compared against that achieved
using Support Vector Machine (SVM) and Extreme Learning Machine (ELM)
techniques. Our proposed method attains comparable performance while utilizing
10 to 50% less computational resources than the other reported techniques.Comment: Accepted for publication in Neural Computatio
Signal Propagation in Feedforward Neuronal Networks with Unreliable Synapses
In this paper, we systematically investigate both the synfire propagation and
firing rate propagation in feedforward neuronal network coupled in an
all-to-all fashion. In contrast to most earlier work, where only reliable
synaptic connections are considered, we mainly examine the effects of
unreliable synapses on both types of neural activity propagation in this work.
We first study networks composed of purely excitatory neurons. Our results show
that both the successful transmission probability and excitatory synaptic
strength largely influence the propagation of these two types of neural
activities, and better tuning of these synaptic parameters makes the considered
network support stable signal propagation. It is also found that noise has
significant but different impacts on these two types of propagation. The
additive Gaussian white noise has the tendency to reduce the precision of the
synfire activity, whereas noise with appropriate intensity can enhance the
performance of firing rate propagation. Further simulations indicate that the
propagation dynamics of the considered neuronal network is not simply
determined by the average amount of received neurotransmitter for each neuron
in a time instant, but also largely influenced by the stochastic effect of
neurotransmitter release. Second, we compare our results with those obtained in
corresponding feedforward neuronal networks connected with reliable synapses
but in a random coupling fashion. We confirm that some differences can be
observed in these two different feedforward neuronal network models. Finally,
we study the signal propagation in feedforward neuronal networks consisting of
both excitatory and inhibitory neurons, and demonstrate that inhibition also
plays an important role in signal propagation in the considered networks.Comment: 33pages, 16 figures; Journal of Computational Neuroscience
(published
A guide to time-resolved and parameter-free measures of spike train synchrony
Measures of spike train synchrony have proven a valuable tool in both
experimental and computational neuroscience. Particularly useful are
time-resolved methods such as the ISI- and the SPIKE-distance, which have
already been applied in various bivariate and multivariate contexts. Recently,
SPIKE-Synchronization was proposed as another time-resolved synchronization
measure. It is based on Event-Synchronization and has a very intuitive
interpretation. Here, we present a detailed analysis of the mathematical
properties of these three synchronization measures. For example, we were able
to obtain analytic expressions for the expectation values of the ISI-distance
and SPIKE-Synchronization for Poisson spike trains. For the SPIKE-distance we
present an empirical formula deduced from numerical evaluations. These
expectation values are crucial for interpreting the synchronization of spike
trains measured in experiments or numerical simulations, as they represent the
point of reference for fully randomized spike trains.Comment: 8 pages, 4 figure
Personalised modelling with spiking neural networks integrating temporal and static information.
This paper proposes a new personalised prognostic/diagnostic system that supports classification, prediction and pattern recognition when both static and dynamic/spatiotemporal features are presented in a dataset. The system is based on a proposed clustering method (named d2WKNN) for optimal selection of neighbouring samples to an individual with respect to the integration of both static (vector-based) and temporal individual data. The most relevant samples to an individual are selected to train a Personalised Spiking Neural Network (PSNN) that learns from sets of streaming data to capture the space and time association patterns. The generated time-dependant patterns resulted in a higher accuracy of classification/prediction (80% to 93%) when compared with global modelling and conventional methods. In addition, the PSNN models can support interpretability by creating personalised profiling of an individual. This contributes to a better understanding of the interactions between features. Therefore, an end-user can comprehend what interactions in the model have led to a certain decision (outcome). The proposed PSNN model is an analytical tool, applicable to several real-life health applications, where different data domains describe a person's health condition. The system was applied to two case studies: (1) classification of spatiotemporal neuroimaging data for the investigation of individual response to treatment and (2) prediction of risk of stroke with respect to temporal environmental data. For both datasets, besides the temporal data, static health data were also available. The hyper-parameters of the proposed system, including the PSNN models and the d2WKNN clustering parameters, are optimised for each individual
NeuSort: An Automatic Adaptive Spike Sorting Approach with Neuromorphic Models
Objective. Spike sorting, a critical step in neural data processing, aims to
classify spiking events from single electrode recordings based on different
waveforms. This study aims to develop a novel online spike sorter, NeuSort,
using neuromorphic models, with the ability to adaptively adjust to changes in
neural signals, including waveform deformations and the appearance of new
neurons. Approach. NeuSort leverages a neuromorphic model to emulate
template-matching processes. This model incorporates plasticity learning
mechanisms inspired by biological neural systems, facilitating real-time
adjustments to online parameters. Results. Experimental findings demonstrate
NeuSort's ability to track neuron activities amidst waveform deformations and
identify new neurons in real-time. NeuSort excels in handling non-stationary
neural signals, significantly enhancing its applicability for long-term spike
sorting tasks. Moreover, its implementation on neuromorphic chips guarantees
ultra-low energy consumption during computation. Significance. NeuSort caters
to the demand for real-time spike sorting in brain-machine interfaces through a
neuromorphic approach. Its unsupervised, automated spike sorting process makes
it a plug-and-play solution for online spike sorting
- …