Wide sensory filters underlie performance in memory-based discrimination and generalization.

The way animals respond to a stimulus depends largely on an internal comparison between the current sensation and the memory of previous stimuli and outcomes. We know little about the accuracy with which the physical properties of the stimuli influence this type of memory-based discriminative decisions. Research has focused largely on discriminations between stimuli presented in quick succession, where animals can make relative inferences (same or different; higher or lower) from trial to trial. In the current study we used a memory-based task to explore how the stimulus' physical properties, in this case tone frequency, affect auditory discrimination and generalization in mice. Mice performed ad libitum while living in groups in their home quarters. We found that the frequency distance between safe and conditioned sounds had a constraining effect on discrimination. As the safe-to-conditioned distance decreased across groups, performance deteriorated rapidly, even for frequency differences significantly larger than reported discrimination thresholds. Generalization width was influenced both by the physical distance and the previous experience of the mice, and was not accompanied by a decrease in sensory acuity. In conclusion, memory-based discriminations along a single stimulus dimension are inherently hard, reflecting a high overlap between the memory traces of the relevant stimuli. Memory-based discriminations rely therefore on wide sensory filters

Neuroligin-3 regulates excitatory synaptic transmission and EPSP-spike coupling in the dentate gyrus in vivo

Neuroligin-3 (Nlgn3), a neuronal adhesion protein implicated in autism spectrum disorder (ASD), is expressed at excitatory and inhibitory postsynapses and hence may regulate neuronal excitation/inhibition balance. To test this hypothesis, we recorded field excitatory postsynaptic potentials (fEPSPs) in the dentate gyrus of Nlgn3 knockout (KO) and wild-type mice. Synaptic transmission evoked by perforant path stimulation was reduced in KO mice, but coupling of the fEPSP to the population spike was increased, suggesting a compensatory change in granule cell excitability. These findings closely resemble those in neuroligin-1 (Nlgn1) KO mice and could be partially explained by the reduction in Nlgn1 levels we observed in hippocampal synaptosomes from Nlgn3 KO mice. However, unlike Nlgn1, Nlgn3 is not necessary for long-term potentiation. We conclude that while Nlgn1 and Nlgn3 have distinct functions, both are required for intact synaptic transmission in the mouse dentate gyrus. Our results indicate that interactions between neuroligins may play an important role in regulating synaptic transmission and that ASD-related neuroligin mutations may also affect the synaptic availability of other neuroligins

Auditory midbrain coding of statistical learning that results from discontinuous sensory stimulation

<div><p>Detecting regular patterns in the environment, a process known as statistical learning, is essential for survival. Neuronal adaptation is a key mechanism in the detection of patterns that are continuously repeated across short (seconds to minutes) temporal windows. Here, we found in mice that a subcortical structure in the auditory midbrain was sensitive to patterns that were repeated discontinuously, in a temporally sparse manner, across windows of minutes to hours. Using a combination of behavioral, electrophysiological, and molecular approaches, we found changes in neuronal response gain that varied in mechanism with the degree of sound predictability and resulted in changes in frequency coding. Analysis of population activity (structural tuning) revealed an increase in frequency classification accuracy in the context of increased overlap in responses across frequencies. The increase in accuracy and overlap was paralleled at the behavioral level in an increase in generalization in the absence of diminished discrimination. Gain modulation was accompanied by changes in gene and protein expression, indicative of long-term plasticity. Physiological changes were largely independent of corticofugal feedback, and no changes were seen in upstream cochlear nucleus responses, suggesting a key role of the auditory midbrain in sensory gating. Subsequent behavior demonstrated learning of predictable and random patterns and their importance in auditory conditioning. Using longer timescales than previously explored, the combined data show that the auditory midbrain codes statistical learning of temporally sparse patterns, a process that is critical for the detection of relevant stimuli in the constant soundscape that the animal navigates through.</p></div

Perturbed Hippocampal Synaptic Inhibition and γ-Oscillations in a Neuroligin-4 Knockout Mouse Model of Autism

Loss-of-function mutations in the synaptic adhesion protein Neuroligin-4 are among the most common genetic abnormalities associated with autism spectrum disorders, but little is known about the function of Neuroligin-4 and the consequences of its loss. We assessed synaptic and network characteristics in Neuroligin-4 knockout mice, focusing on the hippocampus as a model brain region with a critical role in cognition and memory, and found that Neuroligin-4 deletion causes subtle defects of the protein composition and function of GABAergic synapses in the hippocampal CA3 region. Interestingly, these subtle synaptic changes are accompanied by pronounced perturbations of γ-oscillatory network activity, which has been implicated in cognitive function and is altered in multiple psychiatric and neurodevelopmental disorders. Our data provide important insights into the mechanisms by which Neuroligin-4-dependent GABAergic synapses may contribute to autism phenotypes and indicate new strategies for therapeutic approaches

Sound exposure does not affect ongoing behavior in the Audiobox.

<p>(A) Schematic representation of the Audiobox and exposure protocols. Water was available in the water corner and food in the food area. Sound exposure took place in the water corner in every visit (predictable group, center), at random times in the food area (random group, right), or not at all (control group, left). (B) Schematic representation of the temporal association between visits to the water corner (“C”) and visits to the food area (“F-A”) and the sound in the predictable (top) and random (bottom) groups. (C) Cumulative distribution of the intervisit time interval to the water corner area. The dotted lines indicate the fraction of visits within 1 minute of intervisit time. (D) Mean daily time spent in the water corner area was similar between groups (ANOVA, F_2,60 = 0.24, <i>p</i> = 0.78). For B-D: control <i>n</i> = 21; predictable <i>n</i> = 29; random <i>n</i> = 13. All animals used for electrophysiology were included here. (E) Mean daily percentage of time spent in the water corner area without drinking was similar between groups (ANOVA, F_2,60 = 0.98, <i>p</i> = 0.38). Error bars represent SEM. Numerical data for this figure can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005114#pbio.2005114.s001" target="_blank">S1 Data</a>.</p

Predictable sound exposure does not affect evoked activity in the cochlear nucleus.

<p>(A) Average frequency response areas of cochlear nucleus neurons evoked by 70 dB tone bursts, classified as bushy cells. Units with a CF between 6 and 24 kHz were grouped by CF into 2 octave bins (CF group 6–12 kHz, control <i>n</i> = 3, predictable <i>n</i> = 2; and CF group 12–24 kHz, control <i>n</i> = 11; predictable <i>n</i> = 6; wilcoxon signed rank test, <i>p</i> > 0.05 for all comparisons). (B) Same as in A but for other cell types, mostly unipolar (CF group 6–12 kHz, control <i>n</i> = 9, predictable <i>n</i> = 11; and CF group 12–24 kHz, control <i>n</i> = 19, predictable <i>n</i> = 39; wilcoxon signed rank test, <i>p</i> > 0.05 for all comparisons). (C) Spontaneous firing rate distributions of cochlear nucleus units were comparable between control and predictable group (binning as in A-B, two-sample Kolmogorov-Smirnov test, <i>p</i> > 0.05 for all comparisons). Error bars represent SEM. Numerical data for this figure found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005114#pbio.2005114.s001" target="_blank">S1 Data</a>. CF, characteristic frequency.</p

Predictable sound exposure modifies structural tuning.

<p>(A) Scheme of the local tuning curves and the structural tuning along the collicular tonotopic axis for the predictable (red) and the control (black) groups. (B) Same as A for the random (green) and the control (black) groups. (C) Mean normalized structural tuning, evoked response across depths, for a subset of frequencies (ANOVA, group × depth × frequency interaction F_168,1869 = 2.34, <i>p</i> < 0.0001). Animals and recording sites: control <i>n</i> = 10 and 98; predictable <i>n</i> = 14 and 162; random <i>n</i> = 7 and 91. Numerical data for this figure found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005114#pbio.2005114.s001" target="_blank">S1 Data</a>.</p

Predictable sound exposure leads to increased overlap in structural tuning and better classification accuracy.

<p>(A) Scheme that illustrates the computing of the ROC curves using the local tuning curves, in which f1 is the BF. (B) Scheme that illustrates the example of a ROC curve. (C) Same as D but for the structural tuning. (D) AUROCC calculated from the tuning curves with BF of 16 kHz ± 1.1% (tuned region) across groups and ΔF. Each point is the comparison between an f1 of 16 kHz and an f2 of a frequency separated by a given ΔF. (ANOVA, group F_2,324 = 11.78, <i>p</i> < 0.0001, ΔF F_11,324 = 14.49, <i>p</i> < 0.0001). Animals: control <i>n</i> = 10; predictable <i>n</i> = 14; random <i>n</i> = 7. Corrected pair comparisons: <i>p</i> < 0.0001 random versus control, <i>p</i> < 0.0001 random versus predictable. (E) Same as D for tuning curves with BF of 11.31 kHz 1.1% (adjacent region). Here, f1 was 11.3 kHz throughout. (ANOVA, group F_2,552 = 8.17, <i>p</i> < 0.0001, ΔF F_11,552 = 17.08, <i>p</i> < 0.0001). Animals: control <i>n</i> = 10; predictable <i>n</i> = 14; random <i>n</i> = 7. Corrected pair comparisons: <i>p</i> = 0.019 random versus control, <i>p</i> = 0.0003 predictable versus control. (F) AUROCC calculated from the structural tuning curves with BF of 16 kHz ± 1.1% (tuned region) across groups and ΔF. Each point is the comparison between the mean of responses to f1 of 16 kHz and individual responses to f2. (ANOVA, group F_2,336 = 9.37, <i>p</i> = 0.0001, ΔF F_11,336 = 12.1, <i>p</i> < 0.0001). Animals: control <i>n</i> = 10; predictable <i>n</i> = 14; random <i>n</i> = 7. Corrected pair comparisons: <i>p</i> = 0.0003 predictable versus control, <i>p</i> = 0.0053 predictable versus random. (G) Same as in F, using an f1 of 11.3 kHz. (ANOVA, group F_2,335 = 33.34, <i>p</i> < 0.0001, ΔF F_11,335 = 3.94, <i>p</i> < 0.0001). Animals: control <i>n</i> = 10; predictable <i>n</i> = 14; random <i>n</i> = 7. Corrected pair comparisons: <i>p</i> < 0.0001 predictable versus control, <i>p</i> = 0.023 random versus control, <i>p</i> = 0.0001 predictable versus random. (H) Scheme illustrating the relationship between ROC and classification accuracy (labeled “c.a.”). Upward arrow equals increased classification accuracy. (I) Mean classification accuracy probability for frequencies in the adjacent (BF of 10–13 kHz) and tuned (BF of 16–19 kHz) regions. Error bars represent SEM. (ANOVA, group F_2,247 = 7.37, <i>p</i> = 0.0008, region F_1,247 = 5.78, <i>p</i> = 0.017, frequency F_3,247 = 2.49, <i>p</i> = 0.061. In the tuned region: ANOVA, group F_2,123 = 9.44, <i>p</i> = 0.000, corrected pair comparisons: <i>p</i> = 0.011 predictable versus control, <i>p</i> = 0.0001 random versus control. For control group: ANOVA, region F_1,79 = 0.07, <i>p</i> = 0.78. For predictable group: ANOVA, region F_1,111 = 4.04, <i>p</i> = 0.046. For random group: ANOVA, region F_1,55 = 7.01, <i>p</i> = 0.010). Animals: control <i>n</i> = 10; predictable <i>n</i> = 14; random <i>n</i> = 7. Numerical data for this figure found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005114#pbio.2005114.s001" target="_blank">S1 Data</a>. AUROCC, area under the ROC curve; BF, best frequency; ROC, receiver operating characteristic.</p

Sound exposure results in increases in response gain in the inferior colliculus.

<p>(A) Left, schematic representation of the recording approach in the inferior colliculus using linear multielectrode array. Inset: Schematic representation of positioning of most superficial recording site, aligned with dura. (B) Right, representative dorsoventral electrode penetration track (DiI) through dorsal cortex and central nucleus. “DC”: dorsal cortex; “ICC”: central nucleus; “LC”: lateral cortex. Scale bar 500 μm. (C) Mean tuning curves of simultaneously recorded evoked responses (70 dB) for different depths in the inferior colliculus (linear mixed effects model; group × depth interaction F_2,8412 = 4.21, <i>p</i> < 0.05). Animals and recording sites: control <i>n</i> = 10 and 98; predictable <i>n</i> = 14 and 162; and random <i>n</i> = 7 and 91. (D) Mean collicular BF for different depths in the inferior colliculus (ANOVA, group F_3,334 = 10.89; <i>p</i> < 0.0001). Animals and recording sites for D-E: home cage <i>n</i> = 6 and 72; control <i>n</i> = 10 and 98; predictable <i>n</i> = 14 and 162; and random <i>n</i> = 7 and 91. (E) Mean BF difference across the tonotopic axis with respect to the mean BF of control group (ANOVA, group F_3,386 = 9.97, <i>p</i> < 0.0001. Corrected pair comparisons: *<i>p</i> < 0.05, **<i>p</i> < 0.01, ***<i>p</i> < 0.001). Error bars represent SEM. Numerical data for this figure can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005114#pbio.2005114.s001" target="_blank">S1 Data</a>. BF, best frequency; DiI, 1,1'-dioactedecyl-3,3,3,3'-tethramethyl indocarbocyanide.</p

Cortical feedback does not influence sound exposure–induced collicular plasticity.

<p>(A) Schematic representation of simultaneous collicular recordings and cortical inactivation. (B) Average tuning curves at 600 μm for control (left) and predictable (right) groups before (continuous lines) and after cortical inactivation (dashed lines). (C) Pairwise comparison between activity before and after cortical inactivation (wilcoxon rank sum test, <i>p</i> = 0.4). Animals and recording sites: control <i>n</i> = 7 and 62, predictable <i>n</i> = 6 and 64. (D) Mean response reliability for the adjacent (left, ANOVA, group F_1,88 = 1.22, <i>p</i> = 0.27) and tuned (right, ANOVA, group F_1,90 = 1.62, <i>p</i> = 0.2) areas, before and after cortical inactivation. (E) Mean spontaneous activity for the adjacent (left, ANOVA, group F_1,92 = 13.23, <i>p</i> < 0.001) and tuned (right, ANOVA, group F_1,94 = 3.98, <i>p</i> < 0.05. Corrected pair comparisons: *<i>p</i> < 0.05) areas, before and after cortical inactivation. (F) Mean bandwidth as a function of sound intensity measured at the base (left, ANOVA, group F_1,229 = 0.71, <i>p</i> = 0.4; muscimol F_1,229 = 9.17, <i>p</i> < 0.01) or at the half-maximum (right, ANOVA, group F_1,229 = 0.76, <i>p</i> = 0.38; muscimol F_1,229 = 4.86, <i>p</i> < 0.05) of the tuning curve before and after cortical inactivation. Animals and recording sites: control <i>n</i> = 7 and 30; predictable <i>n</i> = 6 and 34. Error bars represent SEM. Numerical data for this figure found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005114#pbio.2005114.s001" target="_blank">S1 Data</a>. AC, auditory cortex; IC, inferior colliculus.</p