Computational neuroscience
Recent articles
Static pay, shrinking prospects fuel neuroscience postdoc decline
Postdoctoral researchers sponsored by the National Institutes of Health now toil longer than ever before, for less money. They are responding accordingly.
Static pay, shrinking prospects fuel neuroscience postdoc decline
Postdoctoral researchers sponsored by the National Institutes of Health now toil longer than ever before, for less money. They are responding accordingly.
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The majority of cells in the cerebral cortex are unspecialized, according to an unpublished analysis—and scientists need to take care in naming neurons, the researchers warn.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The majority of cells in the cerebral cortex are unspecialized, according to an unpublished analysis—and scientists need to take care in naming neurons, the researchers warn.
This paper changed my life: ‘A massively parallel architecture for a self-organizing neural pattern recognition machine,’ by Carpenter and Grossberg
This paper taught me that we can use mathematical modeling to understand how neural networks are organized—and led me to a doctoral program in the department led by its authors.
This paper changed my life: ‘A massively parallel architecture for a self-organizing neural pattern recognition machine,’ by Carpenter and Grossberg
This paper taught me that we can use mathematical modeling to understand how neural networks are organized—and led me to a doctoral program in the department led by its authors.
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.
Explaining ‘the largest unexplained number in brain science’: Q&A with Markus Meister and Jieyu Zheng
The human brain takes in sensory information roughly 100 million times faster than it can respond. Neuroscientists need to explore this perceptual paradox to better understand the limits of the brain, Meister and Zheng say.
Explaining ‘the largest unexplained number in brain science’: Q&A with Markus Meister and Jieyu Zheng
The human brain takes in sensory information roughly 100 million times faster than it can respond. Neuroscientists need to explore this perceptual paradox to better understand the limits of the brain, Meister and Zheng say.
What are recurrent networks doing in the brain?
The cortex is filled with excitatory local synapses, but we know little about their role in brain function. New experimental tools, along with ideas from artificial intelligence, are poised to change that.
What are recurrent networks doing in the brain?
The cortex is filled with excitatory local synapses, but we know little about their role in brain function. New experimental tools, along with ideas from artificial intelligence, are poised to change that.
Imagining the ultimate systems neuroscience paper
A growing body of papers on systems neuroscience and on giant simulations of neural circuits involves data beyond the point that anyone can reasonably understand end to end. Looking ahead, “paper-bots” could solve that problem.
Imagining the ultimate systems neuroscience paper
A growing body of papers on systems neuroscience and on giant simulations of neural circuits involves data beyond the point that anyone can reasonably understand end to end. Looking ahead, “paper-bots” could solve that problem.
NeuroAI: A field born from the symbiosis between neuroscience, AI
As the history of this nascent discipline reveals, neuroscience has inspired advances in artificial intelligence, and AI has provided a testing ground for models in neuroscience, accelerating progress in both fields.
NeuroAI: A field born from the symbiosis between neuroscience, AI
As the history of this nascent discipline reveals, neuroscience has inspired advances in artificial intelligence, and AI has provided a testing ground for models in neuroscience, accelerating progress in both fields.
What the brain can teach artificial neural networks
The brain offers valuable lessons to artificial neural networks to boost their data and energy efficiency, flexibility and more.
What the brain can teach artificial neural networks
The brain offers valuable lessons to artificial neural networks to boost their data and energy efficiency, flexibility and more.
Widely distributed brain areas sync to orchestrate decisions in rodents
Multiple brain areas synchronize their activity to help a rodent accumulate the evidence it needs to make a choice, two new studies suggest.
Widely distributed brain areas sync to orchestrate decisions in rodents
Multiple brain areas synchronize their activity to help a rodent accumulate the evidence it needs to make a choice, two new studies suggest.
Explore more from The Transmitter
‘Digital humans’ in a virtual world
By combining large language models with modular cognitive control architecture, Robert Yang and his collaborators have built agents that are capable of grounded reasoning at a linguistic level. Striking collective behaviors have emerged.
‘Digital humans’ in a virtual world
By combining large language models with modular cognitive control architecture, Robert Yang and his collaborators have built agents that are capable of grounded reasoning at a linguistic level. Striking collective behaviors have emerged.
Food for thought: Neuronal fuel source more flexible than previously recognized
The cells primarily rely on glucose—rather than lactate from astrocytes—to generate energy, according to recent findings in mice.
Food for thought: Neuronal fuel source more flexible than previously recognized
The cells primarily rely on glucose—rather than lactate from astrocytes—to generate energy, according to recent findings in mice.
Claims of necessity and sufficiency are not well suited for the study of complex systems
The earliest studies on necessary and sufficient neural populations were performed on simple invertebrate circuits. Does this logic still serve us as we tackle more sophisticated outputs?
Claims of necessity and sufficiency are not well suited for the study of complex systems
The earliest studies on necessary and sufficient neural populations were performed on simple invertebrate circuits. Does this logic still serve us as we tackle more sophisticated outputs?