PMC:2570072 / 20407-21864 JSONTXT

{"project":"2_test","denotations":[{"id":"18982114-17134316-38625811","span":{"begin":137,"end":141},"obj":"17134316"},{"id":"18982114-17845070-38625812","span":{"begin":1006,"end":1010},"obj":"17845070"}],"text":"Balancing excitation and inhibition in both background and persistent states\nA possible solution was proposed recently by Renart et al. (2007). The idea is to have both inhibitory and excitatory neurons involved in the sub-population which is activated by any given external stimulus. In this way, both excitatory and inhibitory increase their rates in the persistent states; if both are balanced, the mean inputs to both populations does not increase in a pronounced fashion, but the variance does increase significantly compared to the background state. Since CV increases with variance when the mean is constant, this model explains the high CV in the persistent state observed experimentally. However, a limitation of this model is that the bistability range is small in this scenario, and that it in fact vanishes in the limit of large number of connections per neuron.\nAnother scenario involves a Hopfield-type memory structure on top of unstructured random excitatory connections (Roudi and Latham, 2007), together with inhibitory neurons maintaining a balance with excitation in both background and persistent states, due to the increase of inhibition triggered by the increase of the firing rate of activated excitatory neurons. These authors showed that this mechanism does produce CVs which are comparable in background and persistent states, however at a quantitative level the values of the CVs seem significantly below 1 – on the order of 0.8."}

{"project":"TEST0","denotations":[{"id":"18982114-60-68-765960","span":{"begin":137,"end":141},"obj":"[\"17134316\"]"},{"id":"18982114-131-139-765961","span":{"begin":1006,"end":1010},"obj":"[\"17845070\"]"}],"text":"Balancing excitation and inhibition in both background and persistent states\nA possible solution was proposed recently by Renart et al. (2007). The idea is to have both inhibitory and excitatory neurons involved in the sub-population which is activated by any given external stimulus. In this way, both excitatory and inhibitory increase their rates in the persistent states; if both are balanced, the mean inputs to both populations does not increase in a pronounced fashion, but the variance does increase significantly compared to the background state. Since CV increases with variance when the mean is constant, this model explains the high CV in the persistent state observed experimentally. However, a limitation of this model is that the bistability range is small in this scenario, and that it in fact vanishes in the limit of large number of connections per neuron.\nAnother scenario involves a Hopfield-type memory structure on top of unstructured random excitatory connections (Roudi and Latham, 2007), together with inhibitory neurons maintaining a balance with excitation in both background and persistent states, due to the increase of inhibition triggered by the increase of the firing rate of activated excitatory neurons. These authors showed that this mechanism does produce CVs which are comparable in background and persistent states, however at a quantitative level the values of the CVs seem significantly below 1 – on the order of 0.8."}

{"project":"0_colil","denotations":[{"id":"18982114-17134316-765960","span":{"begin":137,"end":141},"obj":"17134316"},{"id":"18982114-17845070-765961","span":{"begin":1006,"end":1010},"obj":"17845070"}],"text":"Balancing excitation and inhibition in both background and persistent states\nA possible solution was proposed recently by Renart et al. (2007). The idea is to have both inhibitory and excitatory neurons involved in the sub-population which is activated by any given external stimulus. In this way, both excitatory and inhibitory increase their rates in the persistent states; if both are balanced, the mean inputs to both populations does not increase in a pronounced fashion, but the variance does increase significantly compared to the background state. Since CV increases with variance when the mean is constant, this model explains the high CV in the persistent state observed experimentally. However, a limitation of this model is that the bistability range is small in this scenario, and that it in fact vanishes in the limit of large number of connections per neuron.\nAnother scenario involves a Hopfield-type memory structure on top of unstructured random excitatory connections (Roudi and Latham, 2007), together with inhibitory neurons maintaining a balance with excitation in both background and persistent states, due to the increase of inhibition triggered by the increase of the firing rate of activated excitatory neurons. These authors showed that this mechanism does produce CVs which are comparable in background and persistent states, however at a quantitative level the values of the CVs seem significantly below 1 – on the order of 0.8."}