Abstract
We are interested in an asynchronous graph based model, $\boldsymbol {\mathcal {G}(N,E)}$ of cognition or cognitive dysfunction, where the nodes N provide computation at the neuron level and the edges E i→j between nodes N i and node N j specify internode calculation. We discuss how to improve update and evaluation needs for fast calculation using approximations of neural processing for first and second messenger systems as well as the axonal pulse of a neuron. These approximations give rise to a low memory footprint profile for implementation on multicore platforms using functional programming languages such as Erlang, Clojure and Haskell when we have no shared memory and all states are immutable. The implementation of cognitive models using these tools on such platforms will allow the possibility of fully realizable lesion and longitudinal studies.
Highlights
We are interested in an asynchronous graph based model, G(N, E) of cognition or cognitive dysfunction, where the nodes N provide computation at the neuron level and the edges Ei→j between nodes Ni and node Nj specify internode calculation
We have shown the BFV captures the characteristics of the output pulse well enough to classify neurotransmitter inputs on the basis of how they change the BFV (Peterson and Khan 2006) and we will use modulations of the BFV induced by second messengers in our nodal computations
We focus on Erlang’s model as we feel a neuron in a brain model is a computational unit which performs a calculation on the basis of current inputs and sends its output to other nodes based on its forward link set
Summary
We are interested in an asynchronous graph based model, G(N , E) of cognition or cognitive dysfunction, where the nodes N provide computation at the neuron level and the edges Ei→j between nodes Ni and node Nj specify internode calculation. Connectivity is time dependent, we can think of a useful brain model as a sequence of such graphs of nodes and edges For simulation purposes, this means there is a finite sequence of times, {t1, t2, . Here we are concerned with how to approximate the details of nodal processing so that computations can be performed efficiently with low memory footprint using the no shared memory model of a functional programming language such as Erlang, Haskell and Clojure.
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