A fundamental property of neuronal circuits is the ability to adapt to altered sensory inputs. It is well established that the functional synaptic changes underlying this adaptation are reflected by structural modifications in excitatory neurons. In contrast, the degree to which structural plasticity in inhibitory neurons accompanies functional changes is less clear. Here, we use two-photon imaging to monitor the fine structure of inhibitory neurons in mouse visual cortex after deprivation induced by retinal lesions. We find that a subset of inhibitory neurons carry dendritic spines, which form glutamatergic synapses. Removal of visual input correlates with a rapid and lasting reduction in the number of inhibitory cell spines. Similar to the effects seen for dendritic spines, the number of inhibitory neuron boutons dropped sharply after retinal lesions. Together, these data suggest that structural changes in inhibitory neurons may precede structural changes in excitatory circuitry, which ultimately result in functional adaptation following sensory deprivation.

In hippocampal slice models of epilepsy, two behaviors are seen: short bursts of electrical activity lasting 100 msec and seizure-like electrical activity lasting seconds. The bursts originate from the CA3 region, where there is a high degree of recurrent excitatory connections. Seizures originate from the CA1, where there are fewer recurrent connections. In attempting to explain this behavior, we simulated model networks of excitatory neurons using several types of model neurons. The model neurons were connected in a ring containing predominantly local connections and some long-distance random connections, resulting in a small-world network connectivity pattern. By changing parameters such as the synaptic strengths, number of synapses per neuron, proportion of local versus long-distance connections, we induced “normal,” “seizing,” and “bursting” behaviors. Based on these simulations, we made a simple mathematical description of these networks under well-defined assumptions. This mathematical description explains how specific changes in the topology or synaptic strength in the model cause transitions from normal to seizing and then to bursting. These behaviors appear to be general properties of excitatory networks.