  Understanding how the dynamics of neural networks is shaped by the
computations they perform is a fundamental question in neuroscience. Recently,
the framework of efficient coding proposed a theory of how spiking neural
networks can compute low-dimensional stimulus signals with high efficiency.
Efficient spiking networks are based on time-dependent minimization of a loss
function related to information coding with spikes. To inform the understanding
of the function and dynamics of biological networks in the brain, however, the
mathematical models have to be informed by biology and obey the same
constraints as biological networks. Currently, spiking network models of
efficient coding have been extended to include some features of biological
plausibility, such as architectures with excitatory and inhibitory neurons.
However, biological realism of efficient coding theories is still limited to
simple cases and does not include single neuron and network properties that are
known to be key in biological circuits. Here, we revisit the theory of
efficient coding with spikes to develop spiking neural networks that are closer
to biological circuits. Namely, we find a biologically plausible spiking model
realizing efficient coding in the case of a generalized leaky
integrate-and-fire network with excitatory and inhibitory units, equipped with
fast and slow synaptic currents, local homeostatic currents such as
spike-triggered adaptation, hyperpolarization-activated rebound current,
heterogeneous firing thresholds and resets, heterogeneous postsynaptic
potentials, and structured, low-rank connectivity. We show how the complexity
of E-E connectivity matrix shapes network responses.
