Several experimental studies show the existence of leader neurons in
population bursts of 2D living neural networks. A leader neuron is, basically,
a neuron which fires at the beginning of a burst (respectively network spike)
more often that we expect by looking at its whole mean neural activity. This
means that leader neurons have some burst triggering power beyond a simple
statistical effect. In this study, we characterize these leader neuron
properties. This naturally leads us to simulate neural 2D networks. To build
our simulations, we choose the leaky integrate and fire (lIF) neuron model. Our
lIF model has got stable leader neurons in the burst population that we
simulate. These leader neurons are excitatory neurons and have a low membrane
potential firing threshold. Except for these two first properties, the
conditions required for a neuron to be a leader neuron are difficult to
identify and seem to depend on several parameters involved in the simulations
themself. However, a detailed linear analysis shows a trend of the properties
required for a neuron to be a leader neuron. Our main finding is: A leader
neuron sends a signal to many excitatory neurons as well as to a few inhibitory
neurons and a leader neuron receives only a few signals from other excitatory
neurons. Our linear analysis exhibits five essential properties for leader
neurons with relative importance. This means that considering a given neural
network with a fixed mean number of connections per neuron, our analysis gives
us a way of predicting which neuron can be a good leader neuron and which
cannot. Our prediction formula gives us a good statistical prediction even if,
considering a single given neuron, the success rate does not reach hundred
percent.Comment: 25 pages, 13 figures, 2 table
