It is often assumed that cellular and synaptic properties need to be regulated to specific values to allow a neuronal network to function properly. To determine how tightly neuronal properties and synaptic strengths need to be tuned to produce a given network output, we simulated more than 20 million versions of a three-cell model of the pyloric network of the crustacean stomatogastric ganglion using different combinations of synapse strengths and neuron properties. We found that virtually indistinguishable network activity can arise from widely disparate sets of underlying mechanisms, suggesting that there could be considerable animal-to-animal variability in many of the parameters that control network activity, and that many different combinations of synaptic strengths and intrinsic membrane properties can be consistent with appropriate network performance.

Neurons in most animals live a very long time relative to the half-lives of all of the proteins that govern excitability and synaptic transmission. Consequently, homeostatic mechanisms are necessary to ensure stable neuronal and network function over an animal's lifetime. To understand how these homeostatic mechanisms might function, it is crucial to understand how tightly regulated synaptic and intrinsic properties must be for adequate network performance, and the extent to which compensatory mechanisms allow for multiple solutions to the production of similar behaviour. Here, we use examples from theoretical and experimental studies of invertebrates and vertebrates to explore several issues relevant to understanding the precision of tuning of synaptic and intrinsic currents for the operation of functional neuronal circuits.

Rhythmic movements are produced by central pattern-generating networks whose output is shaped by sensory and neuromodulatory inputs to allow the animal to adapt its movements to changing needs. This review discusses cellular, circuit, and computational analyses of the mechanisms underlying the generation of rhythmic movements in both invertebrate and vertebrate nervous systems. Attention is paid to exploring the mechanisms by which synaptic and cellular processes interact to play specific roles in shaping motor patterns and, consequently, movement.

All nervous systems are subject to neuromodulation. Neuromodulators can be delivered as local hormones, as cotransmitters in projection neurons, and through the general circulation. Because neuromodulators can transform the intrinsic firing properties of circuit neurons and alter effective synaptic strength, neuromodulatory substances reconfigure neuronal circuits, often massively altering their output. Thus, the anatomical connectome provides a minimal structure and the neuromodulatory environment constructs and specifies the functional circuits that give rise to behavior.

Central pattern generators are neuronal circuits that when activated can produce rhythmic motor patterns such as walking, breathing, flying, and swimming in the absence of sensory or descending inputs that carry specific timing information. General principles of the organization of these circuits and their control by higher brain centers have come from the study of smaller circuits found in invertebrates. Recent work on vertebrates highlights the importance of neuro-modulatory control pathways in enabling spinal cord and brain stem circuits to generate meaningful motor patterns. Because rhythmic motor patterns are easily quantified and studied, central pattern generators will provide important testing grounds for understanding the effects of numerous genetic mutations on behavior. Moreover, further understanding of the modulation of spinal cord circuitry used in rhythmic behaviors should facilitate the development of new treatments to enhance recovery after spinal cord damage.

Studies of the stomatogastric nervous systems of lobsters and crabs have led to numerous insights into the cellular and circuit mechanisms that generate rhythmic motor patterns. The small number of easily identifiable neurons allowed the establishment of connectivity diagrams among the neurons of the stomatogastric ganglion. We now know that (a) neuromodulatory substances reconfigure circuit dynamics by altering synaptic strength and voltage-dependent conductances and (b) individual neurons can switch among different functional circuits. Computational and experimental studies of single-neuron and network homeostatic regulation have provided insight into compensatory mechanisms that can underlie stable network performance. Many of the observations first made using the stomatogastric nervous system can be generalized to other invertebrate and vertebrate circuits.

It is often assumed that all neurons of the same cell type have identical intrinsic properties, both within an animal and between animals. We exploited the large size and small number of unambiguously identifiable neurons in the crab stomatogastric ganglion to test this assumption at the level of channel mRNA expression and membrane currents (measured in voltage-clamp experiments). In lateral pyloric (LP) neurons, we saw strong correlations between measured current and the abundance of Shal and BK-KCa mRNAs (encoding the Shal-family voltage-gated potassium channel and large-conductance calcium-activated potassium channel, respectively). We also saw two- to fourfold interanimal variability for three potassium currents and their mRNA expression. Measurements of channel expression in the two electrically coupled pyloric dilator (PD) neurons showed significant interanimal variability, but copy numbers for IH (encoding the hyperpolarization-activated, inward-current channel) and Shal mRNA in the two PD neurons from the same crab were similar, suggesting that the regulation of some currents may be shared in electrically coupled neurons.

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.