Neural coding
Recent articles
Why the 21st-century neuroscientist needs to be neuroethically engaged
Technological advances in decoding brain activity and in growing human brain cells raise new ethical issues. Here is a framework to help researchers navigate them.
Why the 21st-century neuroscientist needs to be neuroethically engaged
Technological advances in decoding brain activity and in growing human brain cells raise new ethical issues. Here is a framework to help researchers navigate them.
Tracking single neurons in the human brain reveals new insight into language and other human-specific functions
Better technologies to stably monitor cell populations over long periods of time make it possible to study neural coding and dynamics in the human brain.
Tracking single neurons in the human brain reveals new insight into language and other human-specific functions
Better technologies to stably monitor cell populations over long periods of time make it possible to study neural coding and dynamics in the human brain.
It’s time to examine neural coding from the message’s point of view
In studying the brain, we almost always take the neuron’s perspective. But we can gain new insights by reorienting our frame of reference to that of the messages flowing over brain networks.
It’s time to examine neural coding from the message’s point of view
In studying the brain, we almost always take the neuron’s perspective. But we can gain new insights by reorienting our frame of reference to that of the messages flowing over brain networks.
Dmitri Chklovskii outlines how single neurons may act as their own optimal feedback controllers
From logical gates to grandmother cells, neuroscientists have employed many metaphors to explain single neuron function. Chklovskii makes the case that neurons are actually trying to control how their outputs affect the rest of the brain.
Dmitri Chklovskii outlines how single neurons may act as their own optimal feedback controllers
From logical gates to grandmother cells, neuroscientists have employed many metaphors to explain single neuron function. Chklovskii makes the case that neurons are actually trying to control how their outputs affect the rest of the brain.
Most neurons in mouse cortex defy functional categories
The majority of cells in the cerebral cortex are unspecialized, according to an unpublished analysis—and scientists need to take care in naming neurons, the researchers warn.
Most neurons in mouse cortex defy functional categories
The majority of cells in the cerebral cortex are unspecialized, according to an unpublished analysis—and scientists need to take care in naming neurons, the researchers warn.
Eli Sennesh talks about bridging predictive coding and NeuroAI
Predictive coding is an enticing theory of brain function. Building on decades of models and experimental work, Eli Sennesh proposes a biologically plausible way our brain might implement it.
Eli Sennesh talks about bridging predictive coding and NeuroAI
Predictive coding is an enticing theory of brain function. Building on decades of models and experimental work, Eli Sennesh proposes a biologically plausible way our brain might implement it.
The Transmitter’s favorite essays and columns of 2024
From sex differences in Alzheimer’s disease to enduring citation bias, experts weighed in on important scientific and practical issues in neuroscience.
The Transmitter’s favorite essays and columns of 2024
From sex differences in Alzheimer’s disease to enduring citation bias, experts weighed in on important scientific and practical issues in neuroscience.
Rajesh Rao reflects on predictive brains, neural interfaces and the future of human intelligence
Twenty-five years ago, Rajesh Rao proposed a seminal theory of how brains could implement predictive coding for perception. His modern version zeroes in on actions.
Rajesh Rao reflects on predictive brains, neural interfaces and the future of human intelligence
Twenty-five years ago, Rajesh Rao proposed a seminal theory of how brains could implement predictive coding for perception. His modern version zeroes in on actions.
Averaging is a convenient fiction of neuroscience
But neurons don’t take averages. This ubiquitous practice hides from us how the brain really works.
Averaging is a convenient fiction of neuroscience
But neurons don’t take averages. This ubiquitous practice hides from us how the brain really works.
Reconstructing dopamine’s link to reward
The field is grappling with whether to modify the long-standing theory of reward prediction error—or abandon it entirely.
Reconstructing dopamine’s link to reward
The field is grappling with whether to modify the long-standing theory of reward prediction error—or abandon it entirely.
Explore more from The Transmitter
Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.
Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.
Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.
Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.