A federal judge ruled that the Trump administration violated the Constitution when it froze nearly $2.7 billion in research funding to Harvard, striking down the freeze in its entirety and delivering ...

Sequencing of oligonucleotide barcodes holds promise as a high-throughput approach for reconstructing synaptic connectivity at scale. Rabies viruses can act as a vehicle for barcode transmission, than...

A budget blueprint released on Friday advances, in hard numbers and biting words, President Trump’s assault on the nation’s universities and scientific research enterprise.

US science funder also plans to screen grant applications for compliance with ‘agency priorities’.

No government—regardless of which party is in power—should dictate what private universities can teach, whom they can admit and hire, and which areas of study and inquiry they can pursue

Dr. Ardem Patapoutian says he watches “with deep sadness as the United States’ remarkable scientific enterprise, which took generations of hard work and national investment to build, faces a concerted...

Understanding how stimuli from the sensory periphery are progressively reformatted to yield useful representations is a fundamental challenge in neuroscience. In olfaction, assessing odor concentration is key for many behaviors, such as tracking and navigation. Initially, as odor concentration increases, the average response of first-order sensory neurons also increases. However, the average response of second-order neurons remains flat with increasing concentration – a transformation that is believed to help with concentration-invariant odor identification, but that seemingly discards concentration information before it could be sent to higher brain regions. By combining neural data analyses from diverse species with computational modeling, we propose strategies by which second-order neurons preserve concentration information, despite flat mean responses at the population level. We find that individual second-order neurons have diverse concentration response curves that are unique to each odorant — some neurons respond more with higher concentration and others respond less — and together this diversity generates distinct combinatorial representations for different concentrations. We show that this encoding scheme can be recapitulated using a circuit computation, called divisive normalization, and we derive sufficient conditions for this diversity to emerge. We then discuss two mechanisms (spike rate vs. timing based) by which higher order brain regions may decode odor concentration from the reformatted representations. Since vertebrate and invertebrate olfactory systems likely evolved independently, our findings suggest that evolution converged on similar algorithmic solutions despite stark differences in neural circuit architectures. Finally, in land vertebrates a parallel olfactory pathway has evolved whose second-order neurons do not exhibit such diverse response curves; rather neurons in this pathway represent concentration information in a more monotonic fashion on average, potentially allowing for easier odor localization and identification at the expense of increased energy use. ### Competing Interest Statement The authors have declared no competing interest.

A government spending bill, approved today by the House of Representatives, allocates 20 percent less funding for the program than last year.

The burden of proof is on us, as researchers, to explain why what we do is valuable to society.