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      "body": "<html> <p>https://cdn.steemitimages.com/DQmRqiqiQvrqgkD9DzAEwTkK28isUmfQV9qYFfzz8L5MTEx/image.png</p> <p><br></p> <p>Can you tell the smell of a rose from the scent of a lilac? If so, you have your brain's piriform cortex to thank. Compared to many parts of the brain, the piriform cortex -- which lets animals and humans process information about smells -- looks like a messy jumble of connections between cells called neurons. Now, Salk Institute researchers have illuminated how the randomness of the piriform cortex is actually critical to how the brain distinguishes between similar odors.</p> <p><br></p> <p>'<em>The standard paradigm is that information in the brain is encoded by which cells are active, but that's not true for the olfactory system</em>,' says Charles Stevens, Distinguished Professor Emeritus in Salk's Molecular Neurobiology Laboratory and coauthor of the new work. 'In the olfactory system, it turns out it's not a matter of which cells are active, but how many cells are active and how active they are.'</p> <p><br></p> <p>Aside from better understanding how smells are processed, the new research, published in the Journal of Comparative Neurology on July 17, 2018, could also lead to greater insight into how some parts of the brain organize information.</p> <p><br></p> <p>When odorant molecules -- the signature of any given smell -- bind to the receptors in a person's nose, the signal is transmitted to the olfactory bulb, and from there to the piriform cortex. In other sensory systems -- like the visual system -- information maintains a strict order as it moves through the brain. Particular parts of the eye, for instance, always transmit information to specific parts of the visual cortex. But researchers have long known that this order is missing in the piriform cortex.</p> <p><br></p> <p>'<em>We haven't been able to discern any order in the piriform cortex connections in any species</em>,' says coauthor Shyam Srinivasan, an assistant project scientist at the <a href='http://www.kavlifoundation.org/'>University of California San Diego's Kavli Institute for Brain and Mind.</a> '<em>Any given odor lights up about 10 percent of neurons that seem to be scattered all over the piriform cortex</em>.'</p> <p><br></p> <p>To start working out the details of how the piriform cortex encodes odor information -- and whether its connections are truly random -- Stevens and Srinivasan analyzed the piriform cortices of nine mice using a variety of staining and microscopy techniques that let them visualize different cell types in the brain region. Their first goal: to quantify the number and density of cells in the piriform cortex.</p> <p><br></p> <blockquote>'This was really like a survey,' explains Srinivasan. 'We counted the cells in different representative areas and averaged them across the whole region.'</blockquote> <p><br></p> <p>The mouse piriform cortex, they concluded, has around half a million neurons in it, divided equally between the larger, less dense posterior piriform and the smaller, more dense anterior piriform.</p> <p><br></p> <p>Using this initial information on density and neuron number, as well as knowledge from previous studies on the number of neurons in the olfactory bulb and how many neuronal connections -- or synapses -- connect the olfactory bulb to the piriform cortex, the pair of researchers was able to draw a surprising finding: each neuron in the olfactory bulb is connected to nearly every single neuron in the piriform cortex.</p> <p><br></p> <p>'<em>Every cell in the piriform is getting information from essentially every odor receptor there is</em>,' says Stevens. '<em>There's not one 'coffee smell' neuron but a whole bunch of coffee cell neurons all over the place.</em>' Rather than a single receptor detecting one odor and lighting up one cluster of telltale neurons, he explains, each odor has a fingerprint that's based more on the strength of the connections -- while the smell of coffee may activate nearly the same neurons in the piriform cortex as the smell of chocolate, they'll activate each neuron to a different degree.</p> <p><br></p> <blockquote>'One advantage to this system is that it can encode very complex information,' says Srinivasan. 'It also makes it very robust to noise.' If one neuron sends a 'noisy' signal -- stronger or weaker activation than it should -- the noise gets cancelled out by the many other neurons sending simultaneous, more accurate signals.</blockquote> <p><br></p> <p>The researchers would like to repeat the work in other animals to see where similarities and differences lie. They also are interested in looking into other areas of the brain that have long been assumed to be dominated by seemingly random connections to see if they're organized in the same way.</p> <p><br></p> <p>Stevens and Srinivasan, who also had a paper come out in the <a href='http://www.jneurosci.org/'>Journal of Neuroscience</a> on July 13 about using the fruit fly olfactory learning circuit to improve the current crop of deep learning algorithms, were funded by the Kavli Institute for Brain and Mind at UC San Diego and the <a href='http://www.nsf.org/'>National Science Foundation.</a></p> </html>",
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      "body": "<html> <p>https://cdn.steemitimages.com/DQmWurKB493DETjXQQW1iQuyszXz8FgTmYi76DuVST7gzSD/image.png</p> <p><strong>Subthreshold direction selectivity is inherited from synaptic conductances when co-tuned. Differential tuning of excitation (Ge) and inhibition (Gi) can preferentially suppress excitation and enhance subthreshold selectivity. With differential tuning, inhibition can be either bidirectional or oppositely tunned for direction relative to excitation.</strong></p> <p><br></p> <p>Our brains do a remarkable job of encoding visual information about the world around us, providing an almost instantaneous report about rapidly changing conditions that is critical for guiding our behavior. Integral to the brain's encoding mechanism is the presence of neurons that respond selectively to specific visual features, generating electrical activity that reliably signals properties such as the orientation of edges, their position in space, and their direction of motion. By using new tools to probe the principles of connectivity that neural circuits use to generate selective responses, scientists at the <strong>Max Planck</strong> Florida Institute for Neuroscience are gaining a host of new insights into the fundamental mechanisms underlying brain function.</p> <p>Understanding how neural circuits build response selectivity poses an enormous challenge since a single neuron receives thousands of synaptic inputs derived from other neurons and these inputs can differ in their response properties and how they can affect the neuron. Some inputs are excitatory, making the neuron more likely to generate an electrical signal, while others are inhibitory, reducing the likelihood that the neuron generates a signal. Somehow a neuron integrates all of these excitatory and inhibitory synaptic inputs to generate responses that are selective, a mysterious 'input/output transform' that has been the subject of intense research interest.</p> <p>Previous studies have suggested that there are some simple rules that govern excitatory and inhibitory functional connections. A prominent rule that has emerged for excitatory connections is the notion '<em>like connects with like.</em>' For example, in the visual cortex, neurons that respond selectively to a particular direction of motion are thought to receive their excitatory inputs from other neurons that respond selectively to the same direction of motion. An equally important rule has been postulated for inhibitory inputs: the idea that the properties of the inhibitory inputs a neuron receives match the properties of its excitatory inputs. Because of the '<em>matching rule</em>,' inhibitory inputs are thought to adjust the strength of the excitatory inputs, but not to alter the selectivity conveyed to the neuron by its excitatory inputs.</p> <p>Now in a recent publication in Nature, researchers in<strong> David Fitzpatrick's</strong> Lab, <strong>Daniel Wilson</strong>, Ph.D., and <strong>Benjamin Scholl</strong>, Ph.D., have accumulated multiple lines of evidence that challenge both of these principles, providing a new perspective on how circuits in visual cortex employ excitation and inhibition to generate neuronal responses that are selective for an object's direction of motion.</p> <p>MPFI researchers first needed a better picture of the direction selectivity supplied by a neuron's excitatory synaptic inputs. To do this they used in vivo two-photon microscopy to characterize the direction selectivity of individual excitatory synaptic inputs onto the dendrites of a neuron, comparing this with the neuron's overall direction preference. Surprisingly, what they discovered goes against the grain of traditional thinking. Although, many of the synapses aligned with the overall directional preference of the neuron, a large number were found to respond best to the opposite (null) direction of motion, a pattern of connectivity that contrasts sharply with '<em>like connects with like</em>' rule. They also noticed a conspicuous mismatch between the strength of a neuron's direction selectivity, and that predicted by its excitatory synaptic inputs. The degree of direction selectivity that the neurons exhibited was significantly greater than what would have been expected from such a broad range of excitatory inputs.</p> <p>To further probe the factor(s) responsible for this puzzling difference between the neuron's excitatory inputs and its output, they turned to a different set of experiments using in vivo whole-cell patch-clamp electrophysiology. This technique makes it possible to measure the total sum of synaptic inputs contributing to a neuron's response and to compare the contribution of excitatory and inhibitory synaptic inputs. The results for the excitatory inputs were consistent with the two-photon imaging data confirming a significant amount of excitatory input for both the preferred and the null direction of motion. The results for inhibition provided the team with another challenge to traditional thinking and a potential explanation for the puzzling input/output difference: In fact, the tuning of the inhibitory inputs did not match the excitatory inputs. For many neurons the strength of inhibition was greatest for the null direction of motion, suggesting that excitatory synaptic inputs to the null direction were being selectively dampened through inhibition.</p> <p>These findings predict that cortical inhibitory neurons make a substantial number of synaptic inputs to excitatory neurons that prefer the opposite direction of motion. The researchers applied two novel approaches to examine this question, first charting the anatomical connections of functionally defined inhibitory neurons, and then using optogenetics (selectively activating inhibitory neurons with light) to map the source of inhibitory inputs to single excitatory neurons. In tandem, these techniques confirmed that inhibitory connections to excitatory neurons often originated from neurons that preferred the opposite direction of motion.</p> <p>Beyond disentangling the mechanism responsible for direction selectivity, these discoveries emphasize the flexible ways in which neural circuits can integrate excitatory and inhibitory inputs to build the variety of selective response properties critical for neural coding. Like doesn't always connect with like and excitation doesn't always match inhibition, but you can count on brain circuits to have evolved the combination of inputs necessary to ensure high levels of functional performance.</p> <p><br></p> </html>",
      "title": "Unconventional connections How inhibition hones cortical selectivity",
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