  The mammalian brain could contain dense and sparse network connectivity
structures, including both excitatory and inhibitory neurons, but is without
any clearly defined output layer. The neurons have time constants, which mean
that the integrated network structure has state memory. The network structure
contains complex mutual interactions between the neurons under different
conditions, which depend on the internal state of the network. The internal
state can be defined as the distribution of activity across all individual
neurons across the network. Therefore, the state of a neuron/network becomes a
defining factor for how information is represented within the network. Towards
this study, we constructed a fully connected (with dense/sparse coding
strategies) recurrent network comprising of both excitatory and inhibitory
neurons, driven by pseudo-random inputs of varying frequencies. In this study
we assessed the impact of varying specific intrinsic parameters of the neurons
that enriched network state dynamics, such as initial neuron activity, amount
of inhibition in combination with thresholded neurons and conduction delays.
The impact was assessed by quantifying the changes in mutual interactions
between the neurons within the network for each given input. We found such
effects were more profound in sparsely connected networks than in densely
connected networks. However, also densely connected networks could make use of
such dynamic changes in the mutual interactions between neurons, as a given
input could induce multiple different network states.
