Olfactory coding: unusual conductances contribute to sparse neural representation. Focus on "Intrinsic membrane properties and inhibitory synaptic input of Kenyon cells as mechanisms for sparse coding?".

At the interface of animal and world, neurons transform environmental stimuli into spikes of electrical current known as action potentials. As sensory information makes its way from point to point along neural pathways, the numbers of neurons participating in the response and the numbers of spikes they generate often dramatically decrease—that is, the sensory representation becomes sparse (Barlow 1972). Sparse neural representations have been observed in many sensory systems (visual: Vinje and Gallant 2000; Weliky et al. 2003; Young and Yamane 1992; auditory: DeWeese et al. 2003; olfactory: Ito et al. 2008; Lin et al. 2006; Perez-Orive et al. 2002) and in many brain areas (Quiroga et al. 2005; Rolls and Tovee 1995; Vinje and Gallant 2000). An extreme and canonical example of sparse coding would be the hypothetical “grandmother cell,” one that responds only to a specific, complex percept or concept (see review by Gross 2002). But many degrees of sparseness are possible. Sparseness can be measured in temporal terms as the amount of spiking in a single neuron over time (lifetime sparseness), or in spatial terms, over an ensemble of neurons, as the likelihood any given neuron will spike in a period of time (population sparseness). These two forms of sparseness need not be correlated (Willmore and Tolhurst 2001). Theoretical and computational studies suggest that sparse coding formats offer several advantages to neurons processing information (see review by Olshausen and Field 2004). From a metabolic point of view, each spike is costly: energy is required to restore ionic balances perturbed by synaptic and action potentials and for neurotransmitter release and reuptake. Signaling accounts for the majority of energy consumed by the brain (Laughlin 2001); Lennie (2003) estimated that energy constrains would permit 1% of human cortical neurons to fire concurrently. And, perhaps more importantly, sparsely coded information can maximize coding space for representations of sensory stimuli. This increases the brain’s memory capacity, reduces the number of synapses that must be modified to stabilize learned associations (Laurent 2002), and permits plasticity by simple local rules such as Hebbian mechanisms (Marr 1971; Willshaw et al. 1969). How does sparse coding arise in sensory systems? A recent article by Demmer and Kloppenburg (2009) characterizes the circuit and intrinsic mechanisms underlying sparse coding in Kenyon cells (KCs), a particularly interesting population of neurons found in the brains of many insects. These neurons are interesting for several reasons. They integrate multiple modes of input. They also appear subject to modulation from neurons bearing reward transmitters and have been linked to learning and memory. And they appear to play a special role in olfactory coding, distilling barrages of spiky input into specific and very sparse output. Such sparse olfactory responses have been reported in the KCs of several species (Ito et al. 2008; PerezOrive et al. 2002; Szyszka et al. 2005; Wang et al. 2004), but mechanisms responsible for this sparsening have probably been most intensively studied in the locust. There, each of the 50,000 KCs in each brain hemisphere receives excitatory inputs from hundreds of projection neurons (Jortner et al. 2007), each of which fires spontaneously and can respond to odors with great bursts of spikes. Despite receiving densely convergent active inputs from so many projection neurons, individual KCs are nearly silent at rest, and odor responses within the population of KCs are extremely sparse; they typically consist of very few spikes in a small subset of the neurons (Perez-Orive et al. 2002; Stopfer et al. 2003). In locust KCs, responses to odors are sparsened by both circuit and intrinsic properties. Odor-elicited spikes in groups of projection neurons are corralled by periodic inhibitory input from local GABAergic interneurons in the antennal lobe (Macleod and Laurent 1996) into 20-Hz oscillatory waves of synchronized excitatory output that impinges on the KCs. But in addition to synapsing on KCs, projection neurons also send branches to a small group of inhibitory cells in a structure called the lateral horn. These lateral horn interneurons, in turn, project feed-forward GABAergic outputs onto the KCs. The net effect of this circuitry is to provide the KCs with rapidly alternating cycles of input each consisting of a wave of excitation directly from projection neurons followed by a wave of inhibition from the lateral horn interneurons. Thus each 50-ms oscillatory cycle defines a time window for integrating input. During each cycle, a KC can briefly integrate information-bearing synaptic inputs from projection neurons before the cycle closes with a wave of inhibition from the lateral horn. This circuit function, likely with help from other inhibitory neurons, effectively sparsens odor responses in KCs; abolishing this inhibition, for example, by injecting the GABA blocker picrotoxin into the vicinity of the KCs broadened their excitatory postsynaptic potentials (EPSPs) and reduced the odor selectivity and sparseness of their responses (Perez-Orive et al. 2002, 2004). The KCs themselves are known to have intrinsic properties that restrict responses to the highly coincident input provided by the synchronized spiking of projection neurons (PerezOrive et al. 2002, 2004). However, the conductances responsible for these properties are poorly understood. Working on cockroaches, Demmer and Kloppenburg conducted a rigorous and detailed study of the intrinsic ionic properties of KCs and Address for reprint requests and other correspondence: (E-mail: stopferm @mail.nih.gov). J Neurophysiol 103: 2–3, 2010. First published November 11, 2009; doi:10.1152/jn.00330.2009.