Can we reverse engineer the brain like a computer?

Can we reverse engineer the brain like a computer?

Neuroscientists have a dizzying array of methods to listen in on hundreds or even thousands of neurons in the brain and have even developed tools to manipulate the activity of individual cells. Will this unprecedented access to the brain allow us to finally crack the mystery of how it works? Here we revisit a 2017 paper claiming that modern neuroscience approaches wouldn’t even allow us to understand the simplest “brain” (a microprocessor) and we re-evaluate that critique in the context of some exciting new research.

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Using a Brain Computer Interface to Probe Neural Redundancy

Using a Brain Computer Interface to Probe Neural Redundancy

There are many ways for your brain to let your arm know it’s time to move. Just as verbal languages have different phrases that mean the same thing, the neural language also has groups of phrases that are thought to be redundant. Jay Hennig and his colleagues use a brain computer interface to try to understand the guiding principles for how neurons choose a specific phrase given so many equivalent options.

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In Search of a Better Treatment for Depression

In Search of a Better Treatment for Depression

There are over 300 million people living with depression in the world, yet our biological understanding of depression and our ability to treat it remains woefully inadequate. Recently, a new drug has come into the spotlight as a potential solution to this need—ketamine. Dr. Hailan Hu and her colleagues try to shed light on the mechanism by which ketamine could alleviate symptoms of depression.

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There's more to metabolism than glucose and ATP

There's more to metabolism than glucose and ATP

In grade school, you probably learned that cells make energy through special signaling pathways in the mitochondria. New research, however, suggests that not all cells in the body may use the same strategies when it comes to metabolism. Your brain is one of the energy-hungriest organs in the body - how exactly do its cells generate enough power to keep you upright and functioning?

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Finding the Right Balance: AMPA Receptor Palmitoylation Regulates Network Excitability

Finding the Right Balance: AMPA Receptor Palmitoylation Regulates Network Excitability

Epilepsy, one of the most common neurological diseases, affects approximately 1 percent of the U.S. population and is characterized by recurrent, unprovoked seizures (Stafstrom and Carmant, 2015). During seizures, neurons synchronize in one region of the brain and the aberrant neural activity can spread to other regions (Staley, 2015). Thus, epilepsy can arise when the balance between neural excitation and neural inhibition (E/I balance) is disrupted. Current therapeutic options aim to either lower excitatory activity or increase inhibitory activity in the brain, to restore the E/I balance. Nevertheless, one-third of epileptic individuals will not respond to currently available treatments (Stafstrom and Carmant, 2015), indicating the need to understand the underlying mechanisms that lead to neuronal hyperexcitability.

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How does the eye talk to the brain?

How does the eye talk to the brain?

Two eyes are better than one! Our brains integrate two slightly different images coming from our two eyes to give us depth perception. How does this happen? To answer this, we will look at where the neural pathways from the two eyes converge. Retinal ganglion cells (RGCs) are neurons in the eye that send visual information to the brain. RGCs are divided into many subtypes based on the shapes of the cells. The lateral geniculate nucleus (LGN) is the primary brain region where RGCs first deliver visual information to brain cells. Previous studies have suggested that some LGN cells receive input from only one eye (Chen and Regehr, 2000; Sincich et al., 2007), while others receive input from both eyes (Hammer et al., 2015; Morgan et al., 2016). To fully understand how the brain integrates the images coming from two eyes, we need to understand the pattern of connectivity between RGCs and LGN cells at the level of individual neuronal connections.

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