Neuronal Computation Underlying Inferential Reasoning in Humans and Mice.

Every day we make decisions critical for adaptation and survival. We repeat actions with known consequences. But we also draw on loosely related events to infer and imagine the outcome of entirely novel choices. These inferential decisions are thought to engage a number of brain regions; however, the underlying neuronal computation remains unknown. Here, we use a multi-day cross-species approach in humans and mice to report the functional anatomy and neuronal computation underlying inferential decisions. We show that during successful inference, the mammalian brain uses a hippocampal prospective code to forecast temporally structured learned associations. Moreover, during resting behavior, coactivation of hippocampal cells in sharp-wave/ripples represent inferred relationships that include reward, thereby "joining-the-dots" between events that have not been observed together but lead to profitable outcomes. Computing mnemonic links in this manner may provide an important mechanism to build a cognitive map that stretches beyond direct experience, thus supporting flexible behavior.

Memory recall involves a transient break in excitatory-inhibitory balance.

The brain has a remarkable capacity to acquire and store memories that can later be selectively recalled. These processes are supported by the hippocampus which is thought to index memory recall by reinstating information stored across distributed neocortical circuits. However, the mechanism that supports this interaction remains unclear. Here, in humans, we show that recall of a visual cue from a paired associate is accompanied by a transient increase in the ratio between glutamate and GABA in visual cortex. Moreover, these excitatory-inhibitory fluctuations are predicted by activity in the hippocampus. These data suggest the hippocampus gates memory recall by indexing information stored across neocortical circuits using a disinhibitory mechanism.

Memories are stored by distributed groups of neurons in the brain, with individual neurons contributing to multiple memories. In a part of the brain called the neocortex, memories are held in a silent state through a balance between excitatory and inhibitory activity. This is to prevent them from being disrupted by incoming information. When a memory is recalled, an area of the brain called the hippocampus is thought to instruct the neocortex to activate the appropriate neuronal network. But how the hippocampus and neocortex coordinate their activity to switch memories 'on' and 'off' is unclear. The answer may lie in the fact that neurons in the neocortex consist of two broad types: excitatory and inhibitory. Excitatory neurons increase the activity of other neurons. They do this by releasing a chemical called glutamate. Inhibitory neurons reduce the activity of other neurons, by releasing a chemical called GABA. Koolschijn, Shpektor et al. hypothesized that the hippocampus activates memories by changing the balance of excitatory and inhibitory activity in neocortex. To test this idea, Koolschijn, Shpektor et al. invited healthy volunteers to explore a virtual reality environment. The volunteers learned that specific sounds in the environment predicted the appearance of particular visual patterns. The next day, the volunteers returned to the environment and viewed these patterns again. After each pattern, they were invited to open a virtual box. Volunteers learned that some patterns led to money in the virtual box, while other patterns did not. Finally, on day three, the volunteers listened to the sounds from day one again, this time while lying in a brain scanner. The volunteers' task was to infer whether each of the sounds would lead to money. Given that the sounds were never directly paired with the content of the virtual box, the volunteers had to solve the task by recalling the associated visual patterns. As they did so, the brain scanner measured their overall brain activity. It also assessed the relative levels of excitatory and inhibitory activity in visual areas of the neocortex, by measuring glutamate and GABA. The results revealed that as the volunteers recalled the visual cues, activity in both the hippocampus and the visual neocortex increased. Moreover, the ratio of glutamate to GABA in visual neocortex also increased which was predicted by activity in the hippocampus. This suggests that the hippocampus reactivates memories stored in neocortex by temporarily increasing excitatory activity to release memories from inhibitory control. Disturbances in the balance of excitation and inhibition occur in various neuropsychiatric disorders, including schizophrenia, autism, epilepsy and Tourette's syndrome. Damage to the hippocampus is known to cause amnesia. The current findings suggest that memories may become inaccessible ­ or may be activated inappropriately ­ when the interaction between the hippocampus and neocortex goes awry. Future studies could test this possibility in clinical populations.

Multiple associative structures created by reinforcement and incidental statistical learning mechanisms.

Learning the structure of the world can be driven by reinforcement but also occurs incidentally through experience. Reinforcement learning theory has provided insight into how prediction errors drive updates in beliefs but less attention has been paid to the knowledge resulting from such learning. Here we contrast associative structures formed through reinforcement and experience of task statistics. BOLD neuroimaging in human volunteers demonstrates rigid representations of rewarded sequences in temporal pole and posterior orbito-frontal cortex, which are constructed backwards from reward. By contrast, medial prefrontal cortex and a hippocampal-amygdala border region carry reward-related knowledge but also flexible statistical knowledge of the currently relevant task model. Intriguingly, ventral striatum encodes prediction error responses but not the full RL- or statistically derived task knowledge. In summary, representations of task knowledge are derived via multiple learning processes operating at different time scales that are associated with partially overlapping and partially specialized anatomical regions.

Effects of Transcranial Alternating Current Stimulation on the Primary Motor Cortex by Online Combined Approach with Transcranial Magnetic Stimulation.

Transcranial Alternating Current Stimulation (tACS) is a neuromodulatory technique able to act through sinusoidal electrical waveforms in a specific frequency and in turn modulate ongoing cortical oscillatory activity. This neurotool allows the establishment of a causal link between endogenous oscillatory activity and behavior. Most of the tACS studies have shown online effects of tACS. However, little is known about the underlying action mechanisms of this technique because of the AC-induced artifacts on Electroencephalography (EEG) signals. Here we show a unique approach to investigate online physiological frequency-specific effects of tACS of the primary motor cortex (M1) by using single pulse Transcranial Magnetic Stimulation (TMS) to probe cortical excitability changes. In our setup, the TMS coil is placed over the tACS electrode while Motor Evoked Potentials (MEPs) are collected to test the effects of the ongoing M1-tACS. So far, this approach has mainly been used to study the visual and motor systems. However, the current tACS-TMS setup can pave the way for future investigations of cognitive functions. Therefore, we provide a step-by-step manual and video guidelines for the procedure.

Primary motor cortex functionally contributes to language comprehension: An online rTMS study.

Among various questions pertinent to grounding human cognitive functions in a neurobiological substrate, the association between language and motor brain structures is a particularly debated one in neuroscience and psychology. While many studies support a broadly distributed model of language and semantics grounded, among other things, in the general modality-specific systems, theories disagree as to whether motor and sensory cortex activity observed during language processing is functional or epiphenomenal. Here, we assessed the role of motor areas in linguistic processing by investigating the responses of 28 healthy volunteers to different word types in semantic and lexical decision tasks, following repetitive transcranial magnetic stimulation (rTMS) of primary motor cortex. We found that early rTMS (delivered within 200ms of word onset) produces a left-lateralised and meaning-specific change in reaction speed, slowing down behavioural responses to action-related words, and facilitating abstract words - an effect present only during semantic, but not lexical, decision. We interpret these data in light of action-perception theory of language, bolstering the claim that motor cortical areas play a functional role in language comprehension.