## Review of ‘Searching for Collective Behavior in a Large Network of Sensory Neurons’

Last time I reviewed the principle of maximum entropy. Today I am looking at a paper which uses it to create a simplified probabilistic representation of neural dynamics. The idea is to measure the spike trains of each neuron individually (in this case there are around 100 neurons from a salamander retina being measured) and simultaneously. In this way, all correlations in the network are preserved, which allows the construction of a probability distribution describing some features of the network.

Naturally, a probability distribution describing the full network dynamics would need a model of the whole network dynamics, which is not what the authors are aiming at here. Instead, they wish to just capture the correct statistics of the network states. What are the network states? Imagine you bin time into small windows. In each window, each neuron will be spiking or not. Then, for each time point you will have a binary word with 100 bits, where each a 1 corresponds to a spike and a -1 to silence. This is a network state, which we will represent by $\boldsymbol{\sigma}$.

So, the goal is to get $P(\boldsymbol{\sigma})$. It would be more interesting to have something like $P(\boldsymbol{\sigma}_{t+1}|\boldsymbol{\sigma}_t)$ (subscript denoting time) but we don’t always get what we want, now do we? It is a much harder problem to get this conditional probability, so we’ll have to settle for the overall probability of each state. According to maximum entropy, this distribution will be given by $$P(\boldsymbol{\sigma})=\frac{1}{Z}\exp\left(-\sum_i \lambda_i f_i(\boldsymbol{\sigma})\right)$$ Continue reading “Review of ‘Searching for Collective Behavior in a Large Network of Sensory Neurons’”

## Simulating tissues with pressure

One small project I did was to code up a simulation of a growing tissue which feels pressure and where each cell has a dynamic state which depends on its neighbors and the pressure it feels. The idea is to reproduce some essential properties of morphogenesis. You can look at the code here. I am going to talk about the most interesting parts of the code.

I initialize the cells in an ordered lattice, with random perturbations in their positions, except those which are in the borders (bottom, left, right). Those are static and do not evolve in the simulation like the others. They are there just to represent the pressure from the rest of the body (huge) on the simulated tissue (tiny). This is not a very realistic assumption because many developmental systems have a size of the order of the body size, but we have to start somewhere!

All cells are connected with springs, which simulate adhesive and pressure forces in the tissue. If left alone, the system relaxes into a hexagonal configuration, since this minimises the spring potential energy. I integrate the harmonic oscillator equations using a fourth order Adams Moulton algorithm.

Now, it is important to realize that there are two time scales in the system: the pressure equilibration and cell lifetimes. We can assume the mechanical pressure equilibrates very fast, while cell divisions take their time. So what we do is run the oscillator system until equlibrium for each time step of the cellular state evolution, which we will talk about later.

## Simulating networks of nonlinear stochastic systems

arXiv:1209.3700

In this paper we attempt to find a computationally efficient way to numerically simulate networks with nonlinear stochastic dynamics. With this I mean a continuous dynamical model where the differential equation for each variable depends nonlinearly on some or all variables of the system and has additive noise. If $x$ is a vector with all variables and $\eta$ is a random vector of the same size as $x$ with some unspecified distribution, the dynamics can be compactly described as $$\frac{d x}{dt}=f(x,t)+\eta$$

The challenge lies in the nonlinearity combined with stochasticity. Were only one of them to be present, the problem would be simple. A deterministic nonlinear problem can be straightforwardly be integrated with an ODE package, while a linear stochastic system can be reduced to a system of ODEs for the moments of the probability distribution function (PDF). A full solution would require a Monte Carlo algorithm to simulate a sufficient number of paths to allow us to estimate the PDF of $x$ at each time point. For networks with many nodes we are haunted by the curse of dimensionality, as the volume needed to be sampled increases exponentially and so do the number of simulated paths required to get a good approximation of the distribution at later time points. In systems where there is a well defined mode around which most of the probability mass is concentrated we should be able to derive an analytic approximation which is more tractable. This is exactly what we try to do in the paper. Continue reading “Simulating networks of nonlinear stochastic systems”