Page 45 - Volume 12, Issue 2 - Spring 2012

Fig. 2. Left: Montage shows low-magnification view of auditory cortex neurons loaded with Ca2+ dye. Black shadows are blood vessels. Right: two adjacent imaged fields in A1. The best frequency (BF) is indicated by color. Note the local variability of BFs. Adapted from Bandyopadhyay et al. (2010).
large degree of plasticity in individual neurons. A1 neurons can rapidly change their tuning depending on the demands of a behavioral task. Thus, the large sampling space of supragran- ular neurons might determine the repertoire for such rapid adaptive shifts in a neuron’s selectivity. Higher-order cortical regions likely control the behavioral induced shifts. To eluci- date this plasticity, we are currently using micro-stimulation of top-down projections to AI to test the extent to which A1 neu- rons can rapidly change tuning their properties.
By monitoring behavior and recording the sound- evoked responses of large populations of A1 neurons, we will be able to examine how A1 circuits adjust depending on behavioral demands. In addition, by imaging single-cells and stimulating selected neurons to probe connectivity, we will be able to reverse-engineer A1 circuits. Ultimately, we hope to obtain a wiring diagram of A1 for different behavioral condi- tions, which will then contribute towards understanding how stimuli are detected and classified. Future improvements of optical techniques, which will allow imaging over longer time frames and at higher temporal rates, will allow us to gain a more detailed view of cortical processing and how it changes as learning occurs. Collectively, the results gained with the application of single-cell imaging techniques provide insight into how sensory information is represented and adaptively transformed in auditory cortex.
Brain-state dependent modulation of auditory cortical neurons
Auditory cortex (AC) experiments are commonly per- formed in anesthetized animals thus prohibiting normal behavioral reactions. Even in recordings from unanes- thetized animals, movements are generally constrained and animals are not fully able to behave adaptively in response to stimuli that predict discomfort or reward. To assess natural situations, a full spectrum of complex, sound-induced behav- iors must be considered. However, sounds delivered under different behavioral conditions and, consequently, brain states may be processed and perceived differently. Indeed, response variability to the same sensory stimulus has been shown to depend on the state of the brain at the time the
stimulus is presented, such as under attentive versus inatten- tive conditions (e.g., Hubel et al., 1959), or in conditions where animals could escape, or were prevented from escap- ing, during signals that predicted a threat (Seligman and Beagley, 1975).
When higher-order brain regions are activated, they may induce changes in behavior (Fritz et al., 2010). Therefore, behavioral and brain-state shifts are expected to adaptively modify the responses of sensory neurons. We confirmed this phenomena in guinea pigs in which a behavioral shift was induced by suddenly changing the ambient illumination from light to dark—this maneuver shifted guinea pigs from a passive/stationary to an active/exploratory state. When the light was turned off, guinea pigs typically stood up from a sit- ting position and started walking. This behavioral shift did not require training and was evoked in >70% of the trials in non-sleeping animals (Ojima et al., 2010).
We also examined the electrophysiological correlates of guinea pig behavior. When guinea pigs were sitting quietly, many neurons in the supragranular layers of cortical field A were silent, or their spontaneous firing was at very low rates. In infragranular layers, some neurons showed an intermediate- to-high rate of background discharges. When illumination changed from light to dark, which induced exploration activi- ty, a fraction of the infragranular neurons showed dramatic changes in background discharge from high to low rates. The low firing rates typically continued for several minutes, and did not increase, even when the animal was transiently immo- bile during the dark condition (Ojima et al., 2010).
We also assessed state-dependent processing differences for a variety of sounds, including a set of natural sounds as well as pure tones and band-passed noises. Preferred sounds vigorously generated discharges whether ambient light was either on or off. However, in the illumination-on condition, with animals sitting quietly, sounds evoked a burst of spikes, which often obscured stimulus-synchronized responses. By contrast, during the illumination-off condition, usually only a single burst of spikes was evoked. This resulted in much reduced interference between the evoked spikes and the low spontaneous background activity, and thus a better signal-to-
44 Acoustics Today, April 2012