Interneurons are types of nerve cells, typically found in integrative areas of the central nervous system, whose axons (and dendrites) are limited to a single brain area. This feature distinguishes them from principal cells, which often have axonal projections outside the brain area where their cell bodies and dendrites are located.
Principal neurons and their networks underlie local information processing/storage and represent the major sources of output from any brain region, whereas interneurons, by definition, have local axons that govern ensemble activity.
While principal cells are mostly excitatory, using glutamate as a neurotransmitter, interneurons most often use gamma-aminobutyric acid (GABA) to inhibit their targets. Since GABA acts mainly through opening ion channels permeable to chloride/bicarbonate or potassium ions in the postsynaptic neuron, interneurons achieve their functional effects via hyperpolarizing large groups of principal cells.
Interneurons in the spinal cord may use glycine, or both GABA and glycine, to inhibit principal cells, whereas interneurons of cortical areas or the basal ganglia may release various neuropeptides (cholecystokinin, somatostatin, vasoactive intestinal polypeptide, enkephalins, neuropeptide Y, galanin, etc.) in addition to GABA. In some regions, such as the basal ganglia and the cerebellum, principal neurons are also GABAergic.
Most types of interneuron innervate predominantly local principal cells.
The dominant excitatory drive of interneurons may originate from outside the territory (i.e., area/subfield/layer) of their neuronal targets, in which case they are said to mediate feed-forward inhibition. Conversely, if the dominant source of input to an interneuron is the same population of cells which is targeted by the interneuron, it is considered to provide feed-back inhibition (Figure 1).
Figure 1. Schematic representation of two fundamental forms of inhibition in cortical areas of the brain. Black color represents excitatory principal neurons. Purple and blue represent feed-forward and feed-back inhibitory interneurons, respectively. (+) indicates that the axon terminal excites the other neuron. (-) indicates that the axon terminal is inhibitory. In case of feed-forward inhibition, when a distant neuron excites the interneuron, it is going to inhibit several other neurons; however, when feed-back inhibition occurs, the interneuron receives excitation from the same excitatory neuronal population that it is going to inhibit in return. Credit: Dr. Ganor Nyin
The major functional roles of interneurons are thought to be the control of activity levels within a brain area, coordination/generation of rhythmic and intermittent discharge patters of neuronal ensembles, as well as the gating and gain control of excitatory inputs to principal cells. The outcome of interneuron activity is often synchronization of firing in large principal cell populations, which facilitates synaptic potentiation (enhancement of synaptic efficacy) and the formation of potentiated cell ensembles or networks as correlates of memory traces.
Diversity of interneurons, both in terms of structure and function, increases with the complexity of local networks within a given brain area, and this likely correlates with the complexity of the functions carried out by that brain area. Accordingly, the 6-layered neocortex, as the center of the highest nervous functions such as conscious perception or cognition, has the largest number of interneuron types.
A simpler version of cortical architecture can be found in the hippocampus, where the fundamental principles of interneuronal connectivity are the same as in the neocortex, but due to the simple lamination, these circuits can be dissected and the functional roles extrapolated with greater precision. The present description is therefore going to focus largely on interneurons of the hippocampus and neocortex, but occasionally mention analogies and homologies with other areas of the central nervous system.
Functions Of Cortical Interneurons
Compared to the abundance of information on the morphological, physiological, and molecular properties of interneurons and their synaptic connections, there is still a relative paucity of direct information on the functional roles of interneurons in cortical computations. In this section, we attempt to briefly summarize some of the more compelling ideas about the possible functions of interneurons.
Perhaps the oldest and simplest idea is that interneurons maintain physiological activity levels in the brain, preventing runaway excitation in recurrent cortical networks. A similar role in the stabilization of network dynamics has been ascribed to the feedback inhibition mediated by Renshaw cells in motor regions of the spinal cord. This notion also offers a simplistic explanation for the critical involvement of interneurons in epilepsy.
There is evidence that enduring changes in the level of excitation are accompanied by a corresponding change in the overall level of inhibition; however, transient imbalances between excitation and inhibition can also be induced. In the hippocampus as well as in the neocortex, changes in the level of interneuronal firing have been observed to accompany behaviourally relevant novel experiences, and probably contribute to enabling the plastic changes that are induced by such learning events.
Interneurons make a critical contribution to the generation of network oscillations, and synchronize the activity of principal cells during oscillatory and transient brain states (see Mann and Paulsen, 2007). Perisomatic interneurons in particular are thought to be indispensable for the generation of fast (gamma frequency) population rhythms, although the exact nature of their contribution could vary between different regions.
Some forms of gamma oscillation may be generated through an interaction between excitatory and inhibitory neuronal populations; however, pure interneuronal networks, connected via GABAergic synapses (and often through electrical synapses formed by gap junctions as well), are also known to be capable of generating rhythmic, synchronous activity in the gamma frequency range.
The rhythmic inhibitory input generated by such interneuron populations can in turn effectively synchronize the activity of local principal cell ensembles. This may enhance the efficiency of information transmission by increasing the likelihood of coincident spiking, which is an effective trigger of action potential firing in downstream neurons.
Oscillatory activity in neuronal populations can also serve as a temporal reference signal for phase encoding, a form of temporal coding where the timing of action potentials with respect to the phase of an ongoing oscillation carries information.
Such an encoding scheme may be utilized by hippocampal principal cells (also known as “place cells”), whose phase of firing in relation to the local theta oscillation (as well as their firing rate) changes systematically as the animal moves around in its environment (a phenomenon known as “phase precession”).
In addition to maintaining homeostasis and providing a temporal framework for principal cell activity, interneurons likely play some more direct roles in cortical computations. Interneurons targeting specific dendritic regions can selectively gate excitatory input from different sources, thereby changing their relative contributions to the output of the cell.
Dendritic inhibition may also control various forms of synaptic and cellular-level plasticity through its interaction with active dendritic processes. At the network level, both feed-forward and feed-back inhibition act to reduce the number of simultaneously active principal cells, working towards the creation of sparse representations (Acsády and Káli, 2007), which are thought to be advantageous for both sensory processing (Olshausen and Field, 2004) and long-term memory (Treves and Rolls, 1994).
Feedback inhibition also introduces direct competition among members of a local principal cell population, whereby an increase in the activity of one cell tends to decrease the activity of its fellows. Such competition can be a simple but effective means of noise suppression, and – especially if complemented by local recurrent excitation – mediates selection between competing inputs, and may even implement complex computations such as working memory and decision-making in the neocortex (see. e.g., Machens et al., 2005).
Authors: Dr. Tamas Freund, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary, Szabolcs Kali, Infobionic and Neurobiological Plasticity Research Group, Hungarian Academy of Sciences
Originally published on Scholarpedia (Tamas Freund and Szabolcs Kali (2008), Scholarpedia, 3(9):4720.) Republished via Creative Commons.