229,140 research outputs found
Evidence accumulation in a Laplace domain decision space
Evidence accumulation models of simple decision-making have long assumed that
the brain estimates a scalar decision variable corresponding to the
log-likelihood ratio of the two alternatives. Typical neural implementations of
this algorithmic cognitive model assume that large numbers of neurons are each
noisy exemplars of the scalar decision variable. Here we propose a neural
implementation of the diffusion model in which many neurons construct and
maintain the Laplace transform of the distance to each of the decision bounds.
As in classic findings from brain regions including LIP, the firing rate of
neurons coding for the Laplace transform of net accumulated evidence grows to a
bound during random dot motion tasks. However, rather than noisy exemplars of a
single mean value, this approach makes the novel prediction that firing rates
grow to the bound exponentially, across neurons there should be a distribution
of different rates. A second set of neurons records an approximate inversion of
the Laplace transform, these neurons directly estimate net accumulated
evidence. In analogy to time cells and place cells observed in the hippocampus
and other brain regions, the neurons in this second set have receptive fields
along a "decision axis." This finding is consistent with recent findings from
rodent recordings. This theoretical approach places simple evidence
accumulation models in the same mathematical language as recent proposals for
representing time and space in cognitive models for memory.Comment: Revised for CB
A spectral scheme for Kohn-Sham density functional theory of clusters
Starting from the observation that one of the most successful methods for
solving the Kohn-Sham equations for periodic systems -- the plane-wave method
-- is a spectral method based on eigenfunction expansion, we formulate a
spectral method designed towards solving the Kohn-Sham equations for clusters.
This allows for efficient calculation of the electronic structure of clusters
(and molecules) with high accuracy and systematic convergence properties
without the need for any artificial periodicity. The basis functions in this
method form a complete orthonormal set and are expressible in terms of
spherical harmonics and spherical Bessel functions. Computation of the occupied
eigenstates of the discretized Kohn-Sham Hamiltonian is carried out using a
combination of preconditioned block eigensolvers and Chebyshev polynomial
filter accelerated subspace iterations. Several algorithmic and computational
aspects of the method, including computation of the electrostatics terms and
parallelization are discussed. We have implemented these methods and algorithms
into an efficient and reliable package called ClusterES (Cluster Electronic
Structure). A variety of benchmark calculations employing local and non-local
pseudopotentials are carried out using our package and the results are compared
to the literature. Convergence properties of the basis set are discussed
through numerical examples. Computations involving large systems that contain
thousands of electrons are demonstrated to highlight the efficacy of our
methodology. The use of our method to study clusters with arbitrary point group
symmetries is briefly discussed.Comment: Manuscript submitted (with revisions) to Journal of Computational
Physic
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