In recent years, simple GO/NOGO behavioural tasks have become popular due to the relative ease with which they can be combined with technologies such as in vivo multiphoton imaging. To date, it has been assumed that behavioural performance can be captured by the average performance across a session, however this neglects the effect of motivation on behaviour within individual sessions. We investigated the effect of motivation on mice performing a GO/NOGO visual discrimination task. Performance within a session tended to follow a stereotypical trajectory on a Receiver Operating Characteristic (ROC) chart, beginning with an over-motivated state with many false positives, and transitioning through a more or less optimal regime to end with a low hit rate after satiation. Our observations are reproduced by a new model, the Motivated Actor-Critic, introduced here. Our results suggest that standard measures of discriminability, obtained by averaging across a session, may significantly underestimate behavioural performance.
In recent years, simple GO/NOGO behavioural tasks have become popular due to the relative ease with which they can be combined with technologies such as in vivo multiphoton imaging. To date, it has been assumed that behavioural performance can be captured by the average performance across a session, however this neglects the effect of motivation on behaviour within individual sessions. We investigated the effect of motivation on mice performing a GO/NOGO visual discrimination task. Performance within a session tended to follow a stereotypical trajectory on a Receiver Operating Characteristic (ROC) chart, beginning with an over-motivated state with many false positives, and transitioning through a more or less optimal regime to end with a low hit rate after satiation. Our observations are reproduced by a new model, the Motivated Actor-Critic, introduced here. Our results suggest that standard measures of discriminability, obtained by averaging across a session, may significantly underestimate behavioural performance.What is the impact of motivation on behaviour? Reinforcement learning theory assumes that an animal optimises behaviour according to the value placed on the goal of an action under different levels of deprivation 1,2 . Beyond the value placed on the goal (directional effect), motivational state is also critically important in regulating the overall effort and rate of activity (activating effect) an animal engages in 3,4 . The combination of behavioural training with sophisticated electrophysiological and imaging techniques is beginning to provide unprecedented insight into the functioning of neural circuits underpinning perceptually-driven decision making 5,6 . A commonly employed paradigm for studying perceptual decisions is the two-category GO/NOGO task, where the animal performs a response to obtain a reward during a 'Go' stimulus, and needs to withhold the response for the 'NoGo' stimulus. This paradigm has been commonly used in recent years to draw inferences about sensory computations 7-12 . Mice are readily able to perform such tasks, however natural biases 13 can interfere with performance. Motivational influences on behaviour have also been documented both in rodents 3,14 and other species 15 . These factors increase the difficulty of devising appropriate training protocols and can impact on the interpretation of results 6 . The typical timeline for observation during simple decision-making tasks in modern neuroscience extends to hundreds of trials, thus capturing a range of motivational levels throughout a single behaviour session. While there have been mentions of changes in motivation within individual sessions previously 16,17 , these effects are often ignored 3,13 or factored out of analyses 18 . Here, we have analysed the effect of motivation on a GO/NOGO visual discrimination task at the single session level. To our knowledge, this is the first detailed exploration of these factors within the context of the GO/NOGO paradigm, although previously published work 14 can be re-examined to s...
Multiple studies have shown how dendrites enable some neurons to perform linearly non-separable computations. These works focus on cells with an extended dendritic arbor where voltage can vary independently, turning dendritic branches into local non-linear subunits. However, these studies leave a large fraction of the nervous system unexplored. Many neurons, e.g. granule cells, have modest dendritic trees and are electrically compact. It is impossible to decompose them into multiple independent subunits. Here, we upgraded the integrate and fire neuron to account for saturating dendrites. This artificial neuron has a unique membrane voltage and can be seen as a single layer. We present a class of linearly non-separable computations and how our neuron can perform them. We thus demonstrate that even a single layer neuron with dendrites has more computational capacity than without. Because any neuron has one or more layer, and all dendrites do saturate, we show that any dendrited neuron can implement linearly non-separable computations.
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