Multiple models to capture the variability in biological neurons and networks

Experimental work suggests that synaptic and intrinsic neuronal properties vary considerably across identified neurons in different animals. The authors propose that instead of building a single model that captures the average behavior of a neuron or circuit, one could construct a population of models with different underlying structure and similar behaviors, as a way of investigating compensatory mechanisms that contribute to neuron and network function. How tightly tuned are the synaptic and intrinsic properties that give rise to neuron and circuit function? Experimental work shows that these properties vary considerably across identified neurons in different animals. Given this variability in experimental data, this review describes some of the complications of building computational models to aid in understanding how system dynamics arise from the interaction of system components. We argue that instead of trying to build a single model that captures the generic behavior of a neuron or circuit, it is beneficial to construct a population of models that captures the behavior of the population that provided the experimental data. Studying a population of models with different underlying structure and similar behaviors provides opportunities to discover unsuspected compensatory mechanisms that contribute to neuron and network function.