The hippocampus computes diverse information involving spatial memory, anxiety, or reward and directly projects to several brain areas. Are different computations transmitted to all downstream targets uniformly, or does the hippocampus selectively route information according to content and target region? By recording from ventral hippocampal CA1 neurons in rats during different behavioral tasks and determining axonal projections with optogenetics, we observed subsets of neurons changing firing at places of elevated anxiety or changing activity during goal approach. Anxiety-related firing was selectively increased in neurons projecting to the prefrontal cortex. Goal-directed firing was most prominent in neurons targeting the nucleus accumbens; and triple-projecting neurons, targeting the prefrontal cortex, amygdala, and nucleus accumbens, were most active during tasks and sharp wave/ripples. Thus, hippocampal neurons route distinct behavior-contingent information selectively to different target areas.
Successful behaviour depends on the right balance between maximising reward and soliciting information about the world. Here, we show how different types of information-gain emerge when casting behaviour as surprise minimisation. We present two distinct mechanisms for goal-directed exploration that express separable profiles of active sampling to reduce uncertainty. ‘Hidden state’ exploration motivates agents to sample unambiguous observations to accurately infer the (hidden) state of the world. Conversely, ‘model parameter’ exploration, compels agents to sample outcomes associated with high uncertainty, if they are informative for their representation of the task structure. We illustrate the emergence of these types of information-gain, termed active inference and active learning, and show how these forms of exploration induce distinct patterns of ‘Bayes-optimal’ behaviour. Our findings provide a computational framework for understanding how distinct levels of uncertainty systematically affect the exploration-exploitation trade-off in decision-making.
Highlights d Oscillatory, not pulsatile, stimulation of vHPC-mPFC at 8 Hz increased avoidance d Oscillatory stimulation of vHPC-mPFC at 2 or 20 Hz did not increase avoidance d Oscillatory stimulation of vHPC-mPFC facilitated neural transmission in this pathway d 8-Hz oscillatory stimulation increased vHPC-mPFC theta synchrony during the EPM
Damage involving the anterior thalamic and adjacent rostral thalamic nuclei may result in a severe anterograde amnesia, similar to the amnesia resulting from damage to the hippocampal formation. Little is known, however, about the information represented in these nuclei. To redress this deficit, we recorded units in three rostral thalamic nuclei in freely-moving rats [the parataenial nucleus (PT), the anteromedial nucleus (AM) and nucleus reuniens NRe]. We found units in these nuclei possessing previously unsuspected spatial properties. The various cell types show clear similarities to place cells, head direction cells, and perimeter/border cells described in hippocampal and parahippocampal regions. Based on their connectivity, it had been predicted that the anterior thalamic nuclei process information with high spatial and temporal resolution while the midline nuclei have more diffuse roles in attention and arousal. Our current findings strongly support the first prediction but directly challenge or substantially moderate the second prediction. The rostral thalamic spatial cells described here may reflect direct hippocampal/parahippocampal inputs, a striking finding of itself, given the relative lack of place cells in other sites receiving direct hippocampal formation inputs. Alternatively, they may provide elemental thalamic spatial inputs to assist hippocampal spatial computations. Finally, they could represent a parallel spatial system in the brain.
Successful behaviour depends on the right balance between maximising reward and soliciting information about the world. Here, we show how different types of information-gain emerge when casting behaviour as surprise minimisation. We present two distinct mechanisms for goal-directed exploration that express separable profiles of active sampling to reduce uncertainty. 'Hidden state' exploration motivates agents to sample unambiguous observations to accurately infer the (hidden) state of the world. Conversely, 'model parameter' exploration, compels agents to sample outcomes associated with high uncertainty, if they are informative for their representation of the task structure.We illustrate the emergence of these types of information-gain, termed active inference and active learning, and show how these forms of exploration induce distinct patterns of 'Bayes-optimal' 2 behaviour. Our findings provide a computational framework to understand how distinct levels of uncertainty induce different modes of information-gain in decision-making.
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