A role for descending auditory cortical projections in songbird vocal learning

Many learned motor behaviors are acquired by comparing ongoing behavior with an internal representation of correct performance, rather than using an explicit external reward. For example, juvenile songbirds learn to sing by comparing their song with the memory of a tutor song. At present, the brain regions subserving song evaluation are not known. In this study, we report several findings suggesting that song evaluation involves an avian 'cortical' area previously shown to project to the dopaminergic midbrain and other downstream targets. We find that this ventral portion of the intermediate arcopallium (AIV) receives inputs from auditory cortical areas, and that lesions of AIV result in significant deficits in vocal learning. Additionally, AIV neurons exhibit fast responses to disruptive auditory feedback presented during singing, but not during nonsinging periods. Our findings suggest that auditory cortical areas may guide learning by transmitting song evaluation signals to the dopaminergic midbrain and/or other subcortical targets.National Institutes of Health (U.S.) (grant R01 MH067105)McGovern Institute for Brain Research at MIT (Internal funding

A role for descending auditory cortical projections in songbird vocal learning

Many learned motor behaviors are acquired by comparing ongoing behavior with an internal representation of correct performance, rather than using an explicit external reward. For example, juvenile songbirds learn to sing by comparing their song with the memory of a tutor song. At present, the brain regions subserving song evaluation are not known. In this study, we report several findings suggesting that song evaluation involves an avian 'cortical' area previously shown to project to the dopaminergic midbrain and other downstream targets. We find that this ventral portion of the intermediate arcopallium (AIV) receives inputs from auditory cortical areas, and that lesions of AIV result in significant deficits in vocal learning. Additionally, AIV neurons exhibit fast responses to disruptive auditory feedback presented during singing, but not during nonsinging periods. Our findings suggest that auditory cortical areas may guide learning by transmitting song evaluation signals to the dopaminergic midbrain and/or other subcortical targets.National Institutes of Health (U.S.) (Grant R01 MH067105

Processing of low-probability sounds by cortical neurons. Nature neuroscience 6

Neuronal adaptation, the decline over time of neuronal responses during sensory stimulation, is ubiquitous in the brain. Adaptation contributes to cortical gain control 1 , enhances stimulus discriminability 2 and maximizes information transmission by matching the coding strategy to stimulus statistics 3 . Studies in both visual and auditory sensory areas have shown that adaptation is often stimulus-specific 2,4-11 . For example, neurons in auditory cortex, after having been presented with a repetitive, single-frequency tone for several minutes, show a specific decrease in response to subsequent test tones near that frequency 8 . Similarly, neurons in visual cortex, presented with a high-contrast 'adapting' stimulus of a certain orientation, show decreased responses specifically near that orientation 2, Studies of such stimulus-specific adaptation (SSA), in both visual and auditory modalities, usually use one of two experimental approaches. The first is a method that uses long adapting sequences followed later by test stimuli 4-8 . This approach does not mimic natural scenarios particularly well; in natural sounds, the common background (mimicked by the long adapting sequence) is often intermixed with rare events (mimicked by the test stimuli). Furthermore, the common background may itself be changing over time scales of seconds, requiring fast, online adaptation. The second approach addresses these concerns by using pairs of stimuli-an adapting stimulus followed by a test stimulus. By this method, it has been shown that in some cases SSA can occur rapidly 2,9,10 . Several lines of evidence suggest that, in fact, it is natural to interpret neuronal adaptation in terms of the statistics of the stimulus ensemble. First, visual neurons can adapt in real time to the statistical distribution of input stimuli, and this adaptation can serve to maximize information transmission; thus, neuronal adaptation is tightly linked to the notion of optimal neural coding 3 . Second, natural acoustic backgrounds are highly variable and change rapidly in a stochastic manner 12 . Therefore, an important step in mimicking a naturalistic soundscape scenario Processing of low-probability sounds by cortical neurons The ability to detect rare auditory events can be critical for survival. We report here that neurons in cat primary auditory cortex (A1) responded more strongly to a rarely presented sound than to the same sound when it was common. For the rare stimuli, we used both frequency and amplitude deviants. Moreover, some A1 neurons showed hyperacuity for frequency deviants-a frequency resolution one order of magnitude better than receptive field widths in A1. In contrast, auditory thalamic neurons were insensitive to the probability of frequency deviants. These phenomena resulted from stimulus-specific adaptation in A1, which may be a single-neuron correlate of an extensively studied cortical potential-mismatch negativity-that is evoked by rare sounds. Our results thus indicate that A1 neurons, in addition to processing the acoustic features of sounds, may also be involved in sensory memory and novelty detection. is to use probabilistic stimuli. Third, the sensitivity of the mammalian auditory system to stimulus statistics is exemplified by the mismatch negativity (MMN) In the present study, we used probabilistic stimuli to study adaptation in the auditory system. Our data, collected from both cortex and thalamus, provide evidence for a novel form of SSA that is present in primary auditory cortex but is absent in the auditory thalamus. This form of SSA is rapid, shows very high frequency sensitivity (hyperacuity) and is strongly dependent on the statistical properties of the stimulus ensemble. Furthermore, this form of SSA shares a large number of properties with the MMN, and we therefore propose that it is a neural correlate of MMN. RESULTS To demonstrate SSA in single neurons of cat primary auditory cortex Responses of A1 neurons in an oddball design To study the specific effect of probability on SSA, two frequencies were used in an oddball design protocol: tones with a devian

Natural switches in behaviour rapidly modulate hippocampal coding.

Throughout their daily lives, animals and humans often switch between different behaviours. However, neuroscience research typically studies the brain while the animal is performing one behavioural task at a time, and little is known about how brain circuits represent switches between different behaviours. Here we tested this question using an ethological setting: two bats flew together in a long 135 m tunnel, and switched between navigation when flying alone (solo) and collision avoidance as they flew past each other (cross-over). Bats increased their echolocation click rate before each cross-over, indicating attention to the other bat1-9. Hippocampal CA1 neurons represented the bat's own position when flying alone (place coding10-14). Notably, during cross-overs, neurons switched rapidly to jointly represent the interbat distance by self-position. This neuronal switch was very fast-as fast as 100 ms-which could be revealed owing to the very rapid natural behavioural switch. The neuronal switch correlated with the attention signal, as indexed by echolocation. Interestingly, the different place fields of the same neuron often exhibited very different tuning to interbat distance, creating a complex non-separable coding of position by distance. Theoretical analysis showed that this complex representation yields more efficient coding. Overall, our results suggest that during dynamic natural behaviour, hippocampal neurons can rapidly switch their core computation to represent the relevant behavioural variables, supporting behavioural flexibility

Species-specific differences in synaptic transmission and plasticity

Synaptic transmission and plasticity in the hippocampus are integral factors in learning and memory. While there has been intense investigation of these critical mechanisms in the brain of rodents, we lack a broader understanding of the generality of these processes across species. We investigated one of the smallest animals with conserved hippocampal macroanatomy—the Etruscan shrew, and found that while synaptic properties and plasticity in CA1 Schaffer collateral synapses were similar to mice, CA3 mossy fiber synapses showed striking differences in synaptic plasticity between shrews and mice. Shrew mossy fibers have lower long term plasticity compared to mice. Short term plasticity and the expression of a key protein involved in it, synaptotagmin 7 were also markedly lower at the mossy fibers in shrews than in mice. We also observed similar lower expression of synaptotagmin 7 in the mossy fibers of bats that are evolutionarily closer to shrews than mice. Species specific differences in synaptic plasticity and the key molecules regulating it, highlight the evolutionary divergence of neuronal circuit functions