Olfactory learning modifies the expression of odour‐induced oscillatory responses in the gamma (60–90 Hz) and beta (15–40 Hz) bands in the rat olfactory bulb

172

165

In the first relay of information processing, the olfactory bulb (OB), odors are known to generate specific spatial patterns of activity. Recently, in freely behaving rats, we demonstrated that learning modulated oscillatory activity in local field potential (LFP), in response to odors, in both ␤ (15-40 Hz) and ␥ (60 -90 Hz) bands. The present study further characterized this odor-induced oscillatory activity with emphasis on its spatiotemporal distribution over the olfactory bulb and on its relationship with improvement of behavioral performances along training. For that purpose, LFPs were simultaneously recorded from four locations in the OB in freely moving rats performing an olfactory discrimination task. Electrodes were chronically implanted near relay neurons in the mitral cell body layer. Time-frequency methods were used to extract signal characteristics (amplitude, frequency, and time course) in the two frequency bands. Before training, odor presentation produced, on each site, a power decrease in ␥ oscillations and a weak but significant increase in power of ␤ oscillations (ϳ25 Hz). When the training was achieved, these two phenomena were amplified. Interestingly, the ␤ oscillatory response showed several significant differences between the anterodorsal and posteroventral regions of the OB. In addition, clear-cut ␤ responses occurred in the signal as soon as animals began to master the task. As a whole, our results point to the possible functional importance of ␤ oscillatory activity in the mammalian OB, particularly in the context of olfactory learning.

Section: Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 87%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Learning Modulation of Odor-Induced Oscillatory Responses in the Rat Olfactory Bulb: A Correlate of Odor Recognition?

Martin¹,

Gervais²,

Hugues³

et al. 2004

172

165

“…Oscillations are sensitive to tetanization of excitatory afferents from the olfactory receptor neurons (Friedman and Strowbridge, 2003), actions exerted by neuromodulatory systems (Gray et al, 1986;Gervais et al, 1990), or feedback from the piriform and the entorhinal cortices (Haberly and Price, 1978). This modulation can take part in several processes such as memory [in insects (Stopfer and Laurent, 1999) and in mammals (Ravel et al, 2003)] and habituation (Gray and Skinner, 1988). In line with this, granule cells, among the key elements for generating LFP oscillations, undergo a permanent renewal (Altman, 1969).…”

Section: ␥ Oscillations and Olfactory Information Processingmentioning

confidence: 95%

Interplay between Local GABAergic Interneurons and Relay Neurons Generates γ Oscillations in the Rat Olfactory Bulb

Lagier¹,

Carleton²,

Lledo³

2004

241

223

Olfactory stimuli have been known for a long time to elicit oscillations in olfactory brain areas. In the olfactory bulb (OB), odors trigger synchronous oscillatory activity that is believed to arise from the coherent and rhythmic discharges of large numbers of neurons. These oscillations are known to take part in encoding of sensory information before their transfer to higher subcortical and cortical areas. To characterize the cellular mechanisms underlying ␥ (30 -80 Hz) local field potential (LFP) oscillations, we simultaneously recorded multiunit discharges, intracellular responses, and LFP in rat OB slices. We showed that a single and brief electrical stimulation of olfactory nerve elicited LFP oscillations in the mitral cell body layer lasting Ͼ1 sec. Both action potentials and subthreshold oscillations of mitral/tufted cells, the bulbar output neurons, were precisely synchronized with LFP oscillations. This synchronization arises from the interaction between output neurons and granule cells, the main population of local circuit inhibitory interneurons, through dendrodendritic synapses. Interestingly enough, the synchronization exerted by reciprocal synaptic interactions did not require action potentials initiated in granule cell somata. Finally, local application of a GABA A receptor antagonist at the mitral cell and external plexiform layers confirmed the exclusive role of the granule cell reciprocal synapses in generating the evoked oscillations. We concluded that interneurons located in the granule cell layer generate synaptic activity capable of synchronizing activity of output neurons by interacting with both their subthreshold and spiking activity.

“…There is now a large and rapidly expanding literature that describes the spectral content of electrical activity in different cognitive states, in different tasks, and in different parts of the brain Tallon-Baudry, 2004). These studies also document how spectral content can change over the process of learning (Ravel et al, 2003). Finally, many studies have documented how spectral content differs from controls in a variety of mental disorders (Phillips and Silverstein, 2003;Uhlhaas and Singer, 2006).…”

Section: Classical Rhythms and Their Discontentsmentioning

confidence: 84%

Neurotech for Neuroscience: Unifying Concepts, Organizing Principles, and Emerging Tools

Silver¹,

Boahen²,

Grillner³

et al. 2007

The ability to tackle analysis of the brain at multiple levels simultaneously is emerging from rapid methodological developments. The classical research strategies of "measure," "model," and "make" are being applied to the exploration of nervous system function. These include novel conceptual and theoretical approaches, creative use of mathematical modeling, and attempts to build brain-like devices and systems, as well as other developments including instrumentation and statistical modeling (not covered here). Increasingly, these efforts require teams of scientists from a variety of traditional scientific disciplines to work together. The potential of such efforts for understanding directed motor movement, emergence of cognitive function from neuronal activity, and development of neuromimetic computers are described by a team that includes individuals experienced in behavior and neuroscience, mathematics, and engineering. Funding agencies, including the National Science Foundation, explore the potential of these changing frontiers of research for developing research policies and long-term planning.