8/8: A huge thanks to Katharina Karkowski and everyone contributing! @humansingleneuron.bsky.social, Florian Mormann, Yair Lakretz, @martinhebart.bsky.social, @standehaene.bsky.social, @alanadarcher.bsky.social @treber.bsky.social, @yjqin.bsky.social, Philipp Karkowski, Valeri Borger & Rainer Surges

7/8: Which semantic models best explain these neuronal responses?
Continuous, vector-based embeddings capture MTL activity more accurately than categorical or network-based models, shedding light on how meaning is structured in the human brain.

6/8: Investigating graded responses across neurons revealed region-specific tuning of MTL subregions. Amygdala neurons, for example, showed a preference for food items. These tuning patterns link single-neuron coding with large-scale activity seen in human brain imaging.

5/8: This example amygdala neuron responds to several food- and drink-related items but not to unrelated items (such as the bus).

4/8: Human MTL neurons exhibit semantic tuning, with graded responses that follow narrow and steep tuning curves (steepest in hippocampus).
Neurons preferentially respond to semantically related stimuli, and their firing rates correlate with semantic similarity to preferred stimuli.

3/8: Neuronal tuning curves are core coding principles in neuroscience (e.g., in vision and audition). They can vary in width and steepness. Using semantic embeddings and standardised image sets (THINGS), we presented items at controlled semantic distances to map the local semantic vicinity.

2/8: The discovery of concept cells sparked debate about binary vs distributed coding. Here, we developed a closed-loop algorithm that adaptively presents images semantically related to response-eliciting stimuli to explore the semantic tuning of MTL neurons.