Single cell plasticity and population coding stability in auditory thalamus upon associative learning

Cortical and limbic brain areas are regarded as centres for learning. However, how thalamic sensory relays participate in plasticity upon associative learning, yet support stable long-term sensory coding remains unknown. Using a miniature microscope imaging approach, we monitor the activity of populations of auditory thalamus (medial geniculate body) neurons in freely moving mice upon fear conditioning. We find that single cells exhibit mixed selectivity and heterogeneous plasticity patterns to auditory and aversive stimuli upon learning, which is conserved in amygdala-projecting medial geniculate body neurons. Activity in auditory thalamus to amygdala-projecting neurons stabilizes single cell plasticity in the total medial geniculate body population and is necessary for fear memory consolidation. In contrast to individual cells, population level encoding of auditory stimuli remained stable across days. Our data identifies auditory thalamus as a site for complex neuronal plasticity in fear learning upstream of the amygdala that is in an ideal position to drive plasticity in cortical and limbic brain areas. These findings suggest that medial geniculate body’s role goes beyond a sole relay function by balancing experience-dependent, diverse single cell plasticity with consistent ensemble level representations of the sensory environment to support stable auditory perception with minimal affective bias.


Statistics
For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.
n/a Confirmed The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection

Data analysis
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable:  The only exclusion criterion was post-hoc validation of wrongly targeted viral injection and GRIN lens or fiber placements. E.g. Virus injection outside of the amygdala or medial geniculate body.
The exact number of repetitions (individual data points from separate cells and/or animals) are indicated in figures and legens. Averaging across multiple trials per cell/animal is indicated where apllicable and n/N numbers always refer to data from individual cells/animals, no samples were measured repeatedly for statistical analysis. All attempts of replication were successful across animals and reflected in the numbers reported.
Multiple rounds of experimentation were required, i.e., from multiple mice, which were averaged for the presented datasets. Data was acquired from mice from multiple litters, and responses from individual cells were collected from at least three mice per group. No results were included that were not observed in multiple animals. No issues were identified in reproducing any of the reported findings within groups. However, we did not use replication per se (as in multiple separate cohorts of several subjects).
Mice of the same age and sex (male) were used for the imaging and behavioural experiments in this study (8-11 weeks at the start of the experiment). Litter mates were randomly assigned to the experimental groups without predetermined criteria.
During the optogenetic experiments and analysis, the experimenter was blind to the experimental condition. For miniscope imaging, blinding was not necessary as there were no experimental groups with different treatments. To classify different functional neuronal types, all cells from all animals were pooled together, clustered on the total population of neurons within this study and then reassigned to each animal to avoid bias.