180 research outputs found
Genetic algorithmic parameter optimisation of a recurrent spiking neural network model
Neural networks are complex algorithms that loosely model the behaviour of
the human brain. They play a significant role in computational neuroscience and
artificial intelligence. The next generation of neural network models is based
on the spike timing activity of neurons: spiking neural networks (SNNs).
However, model parameters in SNNs are difficult to search and optimise.
Previous studies using genetic algorithm (GA) optimisation of SNNs were focused
mainly on simple, feedforward, or oscillatory networks, but not much work has
been done on optimising cortex-like recurrent SNNs. In this work, we
investigated the use of GAs to search for optimal parameters in recurrent SNNs
to reach targeted neuronal population firing rates, e.g. as in experimental
observations. We considered a cortical column based SNN comprising 1000
Izhikevich spiking neurons for computational efficiency and biologically
realism. The model parameters explored were the neuronal biased input currents.
First, we found for this particular SNN, the optimal parameter values for
targeted population averaged firing activities, and the convergence of
algorithm by ~100 generations. We then showed that the GA optimal population
size was within ~16-20 while the crossover rate that returned the best fitness
value was ~0.95. Overall, we have successfully demonstrated the feasibility of
implementing GA to optimise model parameters in a recurrent cortical based SNN.Comment: 6 pages, 6 figure
A Study on the Challenges Regarding Conservation of Water Resources in Rajasthan
Water can not be produced but conserving water is equivalent to production of water. Rajasthan has worked on two side water conservation theories. (i) Supply side (ii) Demand side water Supply Side Water Conservation Activities;- Harvesting the available surplus rainfall runoff by Efficient use of existing resources ,Water Harvesting, Artificial Ground water recharging,Increase in use of treated water,Demand Side Water Conservation Activities;- Various interventions taken for demand side water conservations are as under Diversification in cropping pattern,Micro irrigation system,IEC activities to use water optimally
A Mathematical Model to Explore the Interdependence Between the Serotonin and Orexin/Hypocretin Systems
Multi-dimensional relationships among dementia, depression and prescribed drugs in England and Wales hospitals
Additional file 5. Standardised features
Orexin and Circadian Influences in Sleep and Psychiatric Disorders: A Review of Experimental and Computational Modelling Studies
Long- and short-range connectivity and neuronal types affect prefrontal dorsal raphe circuit dynamics differently
A Parallel Monte Carlo Code for Simulating Collisional N-body Systems
We present a new parallel code for computing the dynamical evolution of
collisional N-body systems with up to N~10^7 particles. Our code is based on
the the Henon Monte Carlo method for solving the Fokker-Planck equation, and
makes assumptions of spherical symmetry and dynamical equilibrium. The
principal algorithmic developments involve optimizing data structures, and the
introduction of a parallel random number generation scheme, as well as a
parallel sorting algorithm, required to find nearest neighbors for interactions
and to compute the gravitational potential. The new algorithms we introduce
along with our choice of decomposition scheme minimize communication costs and
ensure optimal distribution of data and workload among the processing units.
The implementation uses the Message Passing Interface (MPI) library for
communication, which makes it portable to many different supercomputing
architectures. We validate the code by calculating the evolution of clusters
with initial Plummer distribution functions up to core collapse with the number
of stars, N, spanning three orders of magnitude, from 10^5 to 10^7. We find
that our results are in good agreement with self-similar core-collapse
solutions, and the core collapse times generally agree with expectations from
the literature. Also, we observe good total energy conservation, within less
than 0.04% throughout all simulations. We analyze the performance of the code,
and demonstrate near-linear scaling of the runtime with the number of
processors up to 64 processors for N=10^5, 128 for N=10^6 and 256 for N=10^7.
The runtime reaches a saturation with the addition of more processors beyond
these limits which is a characteristic of the parallel sorting algorithm. The
resulting maximum speedups we achieve are approximately 60x, 100x, and 220x,
respectively.Comment: 53 pages, 13 figures, accepted for publication in ApJ Supplement
Revealing the dynamic relationship between neural population activities in corticoraphe system
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