Using relatively simple animals, and focusing primarily on olfaction, our unit combines electrophysiological, anatomical, behavioral, computational, and genetic techniques to examine the ways intact neural circuits, driven by sensory stimuli, process information. Interestingly, across diverse phyla, olfactory systems show remarkable cases of convergent evolution - in many instances, common olfactory circuit motifs evolved independently, suggesting there may be one best way to process information about odorants. The cross-species similarities extend from structural to functional, physiological levels. Thus, our results, obtained from simple animals like insects, appear to apply directly to other species as well, and reveal fundamental rules about the processing of information by neural circuits. ? ? In the last year our unit focused on mechanisms used by sensory systems to create neural codes that represent information within the brain. We examined how of populations of neurons in the olfactory pathway create an internal representation of odorants, how noisy, spontaneous activity in the nervous system arises and is controlled, and how neural representations of sensory stimuli become associated with other stimuli when the organism learns something new. ? ? All sensation and sensory processing begins with the peripheral receptor neurons. In the past year, we have developed new techniques that allow us to make the first systematic recordings from olfactory receptor neurons in the locust. The results have been surprising: we found that many of the characteristics of neural codes for odors thought to arise further along the olfactory pathway actually arise at the earliest stage, in the periphery. ? ? To systematically examine the significance of the diverse odor responses in the peripheral olfactory receptor neurons, we constructed a two-part computational model. The first part simulated the responses of a population of receptor neurons. The second part realistically modeled the responses of a downstream olfactory processing area, the antennal lobe, to this diversity of input. We found that our combined model generated responses that accurately matched the complexity, duration, and other important characteristics of spatiotemporal odor codes recorded from the antennal lobe. Thus, with a new type of physiological recording and a new computational approach, we determined how neural codes for odors arise.? ? Noisy or spontaneous activity poses a challenge to neural systems. Activity in the absence of obvious stimuli can occur throughout the central and peripheral nervous system. We used the locust olfactory system to investigate spontaneous activity at multiple points along the sensory pathway. Our recordings from olfactory receptor neurons and their immediate and more distant followers revealed high levels of spontaneous activity in the receptors and the local neurons and projection neurons of the antennal lobe, but very little spontaneous activity in the targets of the projection neurons, the Kenyon cells. We found we could reversibly silence the receptor neurons by cooling the antenna. Silencing the receptors nearly abolished spontaneous as well as odor-elicited spiking in the neurons of the antennal lobe, and the Kenyon cells as well. Thus, spontaneous activity arises in the periphery, and is inherited directly by follower cells. ? ? To understand why spontaneous activity originating in the receptors is passed to the secondary projection neurons but then sharply attenuated before tertiary Kenyon cells, we developed a simple computational model to simulate the success of Kenyon cells in discriminating signal from noise. We found that, given the ongoing barrage of activity from the receptor neurons, odor detection is optimal when projection neurons have a low response threshold, but that Kenyon cells have a high threshold. Our exploration of noise sources in the locust olfactory system provides a specific example of how a sensory system, bombarded by noise at the first stage of processing, balances the competing challenges of maintaining sensitivity to a wide range of stimuli and setting thresholds to eliminate noise and sparsen neural codes. Such strategies may apply to other sensory systems that employ multiple stages of processing and circuitry convergence to achieve optimal detection.? ? What form does information take in the brain, and how does one type of information become associated with another type of information when learning occurs? Needless to say, within the brain, the odorants themselves are not matched with conditioning reinforcements; rather, neural representations of odors, presumably spiking activity in olfactory neurons, must undergo this matching process. The brain areas known as the mushroom bodies have long been linked to associative learning and memory. To understand how neural representations of odors become associated with reinforcement stimuli, we first sought to characterize physiological responses of neurons along the olfactory pathway to odor pulses within the context of an associative learning procedure. The moth Manduca sexta has proved accessible for intracellular recording and is also capable of performing an appetitive olfactory learning task, proboscis extension reflex conditioning. Thus, in the moth, we examined neural representations of odor, and trained them under identical conditions. ? ? With intracellular and multiunit recordings, we found that Kenyon cells were almost silent at rest; odor responses typically consisted of single spikes in a small population of Kenyon cells. Interestingly, spiking in Kenyon cells occurred almost entirely upon an odor pulses onset and sometimes offset, with few spikes in between. This response feature provided us a unique opportunity to examine the ability of spiking in an identified population of neurons to support associative conditioning.? ? Having characterized the responses of Kenyon cells to these odor stimuli, we then used a set of behavioral studies to test a key requirement of a form of Hebbian learning, spike-timing-dependent plasticity: that pre- and post-synaptic neurons must both fire spikes nearly simultaneously. Our results indicate that reinforcement delivered many seconds after the conclusion of spiking responses in Kenyon cells was nonetheless able to support the formation and recall of associative memory. Thus, the acquisition of short-term memory does not require the concurrence of spikes in Kenyon cells with activation of a reward pathway in the moth. And, therefore, spikes in Kenyon cells cannot, by themselves, constitute the odor representation that coincides with appetitive reinforcement. We suggest that the odor representation in Kenyon cells that is paired with reward may be a sustained biochemical, perhaps second messenger responses that are triggered by very transient spiking.? ? Together, our work is advancing fundamental knowledge of neural mechanisms for processing information about environmental stimuli.
| Vislay-Meltzer, Rebecca L; Stopfer, Mark (2007) Olfactory coding: a plastic approach to timing precision. Curr Biol 17:R797-9 |
| Stopfer, Mark (2007) Olfactory processing: massive convergence onto sparse codes. Curr Biol 17:R363-4 |
| Assisi, Collins; Stopfer, Mark; Laurent, Gilles et al. (2007) Adaptive regulation of sparseness by feedforward inhibition. Nat Neurosci 10:1176-84 |
| Kay, Leslie M; Stopfer, Mark (2006) Information processing in the olfactory systems of insects and vertebrates. Semin Cell Dev Biol 17:433-42 |
| Bazhenov, Maxim; Stopfer, Mark; Sejnowski, Terrence J et al. (2005) Fast odor learning improves reliability of odor responses in the locust antennal lobe. Neuron 46:483-92 |
| Brown, Stacey L; Joseph, Joby; Stopfer, Mark (2005) Encoding a temporally structured stimulus with a temporally structured neural representation. Nat Neurosci 8:1568-76 |
| Stopfer, Mark; Jayaraman, Vivek; Laurent, Gilles (2003) Intensity versus identity coding in an olfactory system. Neuron 39:991-1004 |
