Complexities and uncertainties of neuronal network function

Abstract

The nervous system generates behaviours through the activity in groups of neurons assembled into networks. Understanding these networks is thus essential to our understanding of nervous system function. Understanding a network requires information on its component cells, their interactions and their functional properties. Few networks come close to providing complete information on these aspects. However, even if complete information were available it would still only provide limited insight into network function. This is because the functional and structural properties of a network are not fixed but are plastic and can change over time. The number of interacting network components, their (variable) functional properties, and various plasticity mechanisms endows networks with considerable flexibility, but these features inevitably complicate network analyses. This review will initially discuss the general approaches and problems of network analyses. It will then examine the success of these analyses in a model spinal cord locomotor network in the lamprey, to determine to what extent in this relatively simple vertebrate system it is possible to claim detailed understanding of network function and plasticity.

Figure 1

Basic nervous system properties. (a) Example of a neuronal network containing input, intermediate, and output elements, and feedback and feed-forward connections. The large circles indicate single types of cells or cell populations. The small circles indicate connections between cells (open circles are excitatory, filled circles inhibitory). (b(i)) Neurons send signals by generating action potentials. The entry of positively charged sodium ions makes the inside of the cell positive. The effect is transient due to the exit of positively charged potassium ions. (ii and iii) The relative contribution of sodium and potassium ions can alter the functional properties of cells by changing the number or frequency of action potentials. (c) Schematic diagram of synaptic transmission. An action potential in the presynaptic cell results in the opening of voltage-activated calcium channels in the synaptic terminal. This results in the release of neurotransmitters from synaptic vesicles. The transmitter diffuses to the postsynaptic cell where it binds to ionotropic receptors (I) that result in the direct entry or exit of ions, or metabotropic receptors (M) that activate intracellular pathways (PK, protein kinase). (d) The membrane potential of a cell during network activity reflects the integration of excitatory and inhibitory inputs (RMP, the resting potential of cell). If the membrane potential is depolarized above the threshold level (TH) an action potential is generated.

Figure 2

(a) Traditional mechanisms of network plasticity. Activity-dependent and neuromodulator-evoked changes act on cells and synapses to alter the network output. (b) Metaplastic effects. Interactions can occur between activity-dependent plasticity (metaplasticity) and between neuromodulators (metamodulation). Neuromodulators can also influence activity-dependent plasticity, and activity can influence neuromodulation. These individual and interactive effects act on, or are altered by ongoing cellular, synaptic, and network activity. (c) Adaptive changes that ensure that plasticity does not alter ongoing function. If the synaptic input exceeds a threshold level the synapse is potentiated. With a fixed potentiation threshold this will make it more likely that subsequent inputs will exceed the potentiation threshold, resulting in further potentiation. Conversely, synapses can be depressed if the input falls below a depression threshold. This will make it more likely that subsequent inputs fall below the threshold, and the synapse will be successively weakened. The potentiation and depression threshold could instead be altered to prevent disruption of network activity.

Figure 3

Models of the lamprey segmental locomotor network. (a) The initial putative network scheme proposed by Buchanan & Grillner 1987). The model consists of hemisegmental networks on the left and right sides of the spinal cord that coordinate muscle activity on the left and right sides of the body. Connections within one hemisegment are shown on the left, crossing connections between hemisegments on the right. The dashed line indicates a connection that had not been identified. This model is assumed to generate a rhythmic output in the following way. Given that there is a tonic excitatory input, EIN on one side (e.g. assume the left hemisegment) will become activated. The left EINs in turn activate left motor neurons, to cause muscle contraction on the left side of the body, and also activate the left CC interneurons, which inhibit neurons in the right hemisegment: to ensure that motor neuron and muscle activity only occurs on the left side. A number of cellular or synaptic mechanisms could contribute to the termination of activity on the left. When this happens EINs on the right side are relieved of inhibition and become active. This activates motor neurons on the right side, and right side CC interneurons to inhibit the left hemisegment. Right-side activity will then terminate, and the left side again becomes active. Given a constant excitatory input, this model could generate a rhythmic motor pattern. (b) Recent locomotor network scheme proposed by Grillner (2003). Crossing inputs are not specified in this diagram, but they actually reflect the CC interneurons and the smaller crossing interneurons (ScIN), and both are claimed to inhibit all cells in the opposite hemisegment. It is also assumed that the EINs excite all neurons within a hemisegment. Neither assumption is based on experimental analyses. In (c), the actual experimentally identified connectivity of the inhibitory ScINs and CC interneurons is shown, dashed lines showing the connections that have not been identified experimentally (there are also excitatory CC interneurons and ScINs, and the experimental information available on the connectivity of these cells is the same as shown in c). (d) The information available about the connectivity of neurons within one hemisegment is shown. The dashed lines again show connections that have not been verified experimentally. E, excitatory glutamatergic interneuron; MN, motor neuron; LIN, glycinergic lateral inhibitory interneuron; CC, glycinergic crossed caudal inhibitory interneuron; ScIN, small crossing inhibitory interneuron; SiIN, small ipsilateral inhibitory interneuron. Inhibitory crossing neurons are not specified in b, and are referred to as I. In all diagrams large open circles reflect cell bodies, small open circles excitatory synaptic connections, small filled circles inhibitory synapses.

Figure 4

Effects of 5-HT on the lamprey locomotor network. (a) 5-HT (1 μM) slows the frequency of network activity evoked by bath application of the glutamate receptor antagonist NMDA. The traces show activity recorded extracellularly in ventral roots on the left and right sides of the body in control and 10 min after 5-HT application (the effects of 5-HT are shown in blue in all traces). (b(i)) 5-HT (1 μM) does not usually affect the spiking in response to a depolarizing current pulse or the sAHP in the EINs (ii). (c(i)) 5-HT (1 μM) increases the number of spikes in response to depolarizing current injection in an SiIN and reduces the slow afterhyperpolarization (sAHP) following the action potential (ii). (d) 5-HT (1 μM) can hyperpolarize the membrane potential of motor neurons. The bar shows the onset and duration of 5-HT application. (e(i)) 5-HT potentiates inhibitory synaptic inputs from CC interneurons, but reduces the amplitude of glutamatergic synaptic inputs from EINs (ii).

Figure 5

Substance P (1 μM) has neuron and synapse-specific effects on cellular and synaptic properties. (a) Effects of substance P on spiking in different types of network neurons evoked by depolarizing current injection (all effects of substance P are shown in red). (b) The effects of substance P on synaptic inputs from different types of network neurons. The synaptic input is evoked by a train of presynaptic action potentials evoked at 20 Hz.

Figure 6

Effects of substance P (1 μM for 10 min) on the locomotor network. (a) Substance P increases the frequency of NMDA-evoked network activity. The bars above and below the traces show the interval between successive bursts in a single ventral root in control, and 9 h after the start of substance P wash-off. The reduced variability of the interval between bursts reflects the improved regularity of network activity by substance P. (b) Schematic diagram showing the effects of substance P on presynaptic and postsynaptic properties that induce the network plasticity. Substance P acts through an unidentified mechanism to potentiate transmitter release from the presynaptic cell, and through protein kinase C to potentiate the NMDA component of the postsynaptic glutamatergic input. (c) (i) Substance P results in short-term (less than 1 h) potentiation of glutamatergic inputs from the EINs to motor neurons, but results in the long-term conversion of the activity-dependent depression of EIN inputs to motor neurons during spike trains into facilitation (ii), providing an example of metaplasticity. (iii) Summary diagram showing the effects that are assumed to trigger the metaplastic facilitation. Substance P increases the number of neurotransmitter containing vesicles, but reduces the probability of releasing these vesicles. The net effect is that the initial EPSP amplitude stays constant, but the input facilitates during the spike train (Zucker & Regehr 2002). (d) Summary diagram of the cells and synapses in which the effects of substance P have been examined (shown in red). (e) Graph showing the developmental differences in substance P effects in larval and adult animals. The traces below show the significant potentiating effect of substance P in an adult motor neuron, but the lack of effect in a larval motor neuron. (f). Graph showing developmental differences in the amplitude of network inhibitory (SiIN), network excitatory (EIN), and descending inputs to the spinal cord (RS). The traces below show examples of differences in synaptic properties over time in adults.