255 research outputs found
An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks
In this article, we propose a novel Winner-Take-All (WTA) architecture
employing neurons with nonlinear dendrites and an online unsupervised
structural plasticity rule for training it. Further, to aid hardware
implementations, our network employs only binary synapses. The proposed
learning rule is inspired by spike time dependent plasticity (STDP) but differs
for each dendrite based on its activation level. It trains the WTA network
through formation and elimination of connections between inputs and synapses.
To demonstrate the performance of the proposed network and learning rule, we
employ it to solve two, four and six class classification of random Poisson
spike time inputs. The results indicate that by proper tuning of the inhibitory
time constant of the WTA, a trade-off between specificity and sensitivity of
the network can be achieved. We use the inhibitory time constant to set the
number of subpatterns per pattern we want to detect. We show that while the
percentage of successful trials are 92%, 88% and 82% for two, four and six
class classification when no pattern subdivisions are made, it increases to
100% when each pattern is subdivided into 5 or 10 subpatterns. However, the
former scenario of no pattern subdivision is more jitter resilient than the
later ones.Comment: 11 pages, 10 figures, journa
Liquid State Machine with Dendritically Enhanced Readout for Low-power, Neuromorphic VLSI Implementations
In this paper, we describe a new neuro-inspired, hardware-friendly readout
stage for the liquid state machine (LSM), a popular model for reservoir
computing. Compared to the parallel perceptron architecture trained by the
p-delta algorithm, which is the state of the art in terms of performance of
readout stages, our readout architecture and learning algorithm can attain
better performance with significantly less synaptic resources making it
attractive for VLSI implementation. Inspired by the nonlinear properties of
dendrites in biological neurons, our readout stage incorporates neurons having
multiple dendrites with a lumped nonlinearity. The number of synaptic
connections on each branch is significantly lower than the total number of
connections from the liquid neurons and the learning algorithm tries to find
the best 'combination' of input connections on each branch to reduce the error.
Hence, the learning involves network rewiring (NRW) of the readout network
similar to structural plasticity observed in its biological counterparts. We
show that compared to a single perceptron using analog weights, this
architecture for the readout can attain, even by using the same number of
binary valued synapses, up to 3.3 times less error for a two-class spike train
classification problem and 2.4 times less error for an input rate approximation
task. Even with 60 times larger synapses, a group of 60 parallel perceptrons
cannot attain the performance of the proposed dendritically enhanced readout.
An additional advantage of this method for hardware implementations is that the
'choice' of connectivity can be easily implemented exploiting address event
representation (AER) protocols commonly used in current neuromorphic systems
where the connection matrix is stored in memory. Also, due to the use of binary
synapses, our proposed method is more robust against statistical variations.Comment: 14 pages, 19 figures, Journa
A DNA approach to the Road-Coloring Problem
The Road-Coloring Problem in graph theory can be stated as follows: Is any irreducible aperiodic directed graph with constant outdegree 2 road-colorable? In other words, does such a graph have a synchronizing instruction? That is to say: can we label (or color) the two outgoing edges at each vertex, one with “b” or blue color and the other with “r” or red color, in such a manner that there will be an instruction in the form of a finite sequence in “b”s and “r”s (example: rrbrbbbr) such that this instruction will lead each vertex to the same “target” vertex? This thesis is concerned with writing a DNA algorithm which can be followed in the laboratory to produce an explicit solution of a given Road-Coloring problem. This kind of DNA approach was first introduced by Adleman to find an effective method of finding the solution of a given Hamiltonian Path Problem. The Road-Coloring Problem, though introduced over 30 years ago in 1977 by Adler, Goodwyn, and Weiss was only recently solved by Trahtman. But his solution does not give explicitly the synchronizing instruction
Scheduling Resources for Executing a Partial Set of Jobs
In this paper, we consider the problem of choosing a minimum cost set of
resources for executing a specified set of jobs. Each input job is an interval,
determined by its start-time and end-time. Each resource is also an interval
determined by its start-time and end-time; moreover, every resource has a
capacity and a cost associated with it. We consider two versions of this
problem. In the partial covering version, we are also given as input a number
k, specifying the number of jobs that must be performed. The goal is to choose
k jobs and find a minimum cost set of resources to perform the chosen k jobs
(at any point of time the capacity of the chosen set of resources should be
sufficient to execute the jobs active at that time). We present an O(log
n)-factor approximation algorithm for this problem.
We also consider the prize collecting version, wherein every job also has a
penalty associated with it. The feasible solution consists of a subset of the
jobs, and a set of resources, to perform the chosen subset of jobs. The goal is
to find a feasible solution that minimizes the sum of the costs of the selected
resources and the penalties of the jobs that are not selected. We present a
constant factor approximation algorithm for this problemComment: Full version of paper accepted to FSTTCS'201
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