86 research outputs found
An Efficient Method for online Detection of Polychronous Patterns in Spiking Neural Network
Polychronous neural groups are effective structures for the recognition of
precise spike-timing patterns but the detection method is an inefficient
multi-stage brute force process that works off-line on pre-recorded simulation
data. This work presents a new model of polychronous patterns that can capture
precise sequences of spikes directly in the neural simulation. In this scheme,
each neuron is assigned a randomized code that is used to tag the post-synaptic
neurons whenever a spike is transmitted. This creates a polychronous code that
preserves the order of pre-synaptic activity and can be registered in a hash
table when the post-synaptic neuron spikes. A polychronous code is a
sub-component of a polychronous group that will occur, along with others, when
the group is active. We demonstrate the representational and pattern
recognition ability of polychronous codes on a direction selective visual task
involving moving bars that is typical of a computation performed by simple
cells in the cortex. The computational efficiency of the proposed algorithm far
exceeds existing polychronous group detection methods and is well suited for
online detection.Comment: 17 pages, 8 figure
A supervised learning approach based on STDP and polychronization in spiking neuron networks
We propose a network model of spiking neurons, without preimposed topology and driven by STDP (Spike-Time-Dependent Plasticity), a temporal Hebbian unsupervised learning mode, biologically observed. The model is further driven by a supervised learning algorithm, based on a margin criterion, that has effect on the synaptic delays linking the network to the output neurons, with classification as a goal task. The network processing and the resulting performance are completely explainable by the concept of polychronization, proposed by Izhikevich~\cite{Izh06NComp}. The model emphasizes the computational capabilities of this concept
Deep Neural Networks - A Brief History
Introduction to deep neural networks and their history.Comment: 14 pages, 14 figure
Accurate detection of spiking motifs in multi-unit raster plots
Recently, interest has grown in exploring the hypothesis that neural activity
conveys information through precise spiking motifs. To investigate this
phenomenon, various algorithms have been proposed to detect such motifs in
Single Unit Activity (SUA) recorded from populations of neurons. In this study,
we present a novel detection model based on the inversion of a generative model
of raster plot synthesis. Using this generative model, we derive an optimal
detection procedure that takes the form of logistic regression combined with
temporal convolution. A key advantage of this model is its differentiability,
which allows us to formulate a supervised learning approach using a gradient
descent on the binary cross-entropy loss. To assess the model's ability to
detect spiking motifs in synthetic data, we first perform numerical
evaluations. This analysis highlights the advantages of using spiking motifs
over traditional firing rate based population codes. We then successfully
demonstrate that our learning method can recover synthetically generated
spiking motifs, indicating its potential for further applications. In the
future, we aim to extend this method to real neurobiological data, where the
ground truth is unknown, to explore and detect spiking motifs in a more natural
and biologically relevant context
Multi-modal association learning using spike-timing dependent plasticity (STDP)
We propose an associative learning model that can integrate facial images with speech signals to target a subject in a reinforcement learning (RL) paradigm. Through this approach, the rules of learning will involve associating paired stimuli (stimulus–stimulus, i.e., face–speech), which is also known as predictor-choice pairs.
Prior to a learning simulation, we extract the features of the biometrics used in the study. For facial features, we experiment by using two approaches: principal component analysis (PCA)-based Eigenfaces and singular value decomposition (SVD). For speech features, we use wavelet packet decomposition (WPD). The
experiments show that the PCA-based Eigenfaces feature extraction approach produces better results than SVD. We implement the proposed learning model by using the Spike- Timing-Dependent Plasticity (STDP) algorithm, which depends on the time and rate of pre-post synaptic spikes. The key contribution of our study is the implementation of learning rules via STDP and firing rate in spatiotemporal neural networks based on the Izhikevich spiking model. In our learning, we implement learning for response group association by following the reward-modulated STDP in terms of RL, wherein the firing rate of the response groups determines the reward that will be given. We perform a number of experiments that use existing face samples from the Olivetti Research Laboratory (ORL) dataset, and speech samples from TIDigits. After several experiments and simulations are performed to recognize a subject, the results show that the proposed learning model can associate the
predictor (face) with the choice (speech) at optimum performance rates of 77.26% and 82.66% for training and testing, respectively. We also perform learning by using real data, that is, an experiment is conducted on a sample of face–speech data, which have been collected in a manner similar to that of the initial data. The performance results are 79.11% and 77.33% for training and testing, respectively. Based on these results, the proposed learning model can produce high learning performance in terms
of combining heterogeneous data (face–speech). This finding opens possibilities to expand RL in the field of biometric authenticatio
Training Multi-layer Spiking Neural Networks using NormAD based Spatio-Temporal Error Backpropagation
Spiking neural networks (SNNs) have garnered a great amount of interest for
supervised and unsupervised learning applications. This paper deals with the
problem of training multi-layer feedforward SNNs. The non-linear
integrate-and-fire dynamics employed by spiking neurons make it difficult to
train SNNs to generate desired spike trains in response to a given input. To
tackle this, first the problem of training a multi-layer SNN is formulated as
an optimization problem such that its objective function is based on the
deviation in membrane potential rather than the spike arrival instants. Then,
an optimization method named Normalized Approximate Descent (NormAD),
hand-crafted for such non-convex optimization problems, is employed to derive
the iterative synaptic weight update rule. Next, it is reformulated to
efficiently train multi-layer SNNs, and is shown to be effectively performing
spatio-temporal error backpropagation. The learning rule is validated by
training -layer SNNs to solve a spike based formulation of the XOR problem
as well as training -layer SNNs for generic spike based training problems.
Thus, the new algorithm is a key step towards building deep spiking neural
networks capable of efficient event-triggered learning.Comment: 19 pages, 10 figure
Face-voice association towards multimodal-based authentication using modulated spike-time dependent learning
We propose a reward based learning to associate face and voice stimuli. In particular, we implement learning in a spiking neural network
paradigm using modulated spike-time dependent plasticity (STDP).The face and voice stimuli are paired with a temporal delay, and the network
is trained to associate the paired face-voice with a target response.The learning rule is dependent on a reward policy in which the network is given
a positive reward for a correct response to a face-voice stimulus pair, or the network receives a negative reward for an incorrect response. Despite a stochastic environment, the learning result of real images and sound indicates a
good performance with 77.33% accuracy.The result demonstrates that a machine can be trained to associate a pair of biometric inputs to a target response
- …