Entorhinal cortex directs learning-related changes in CA1 representations

Abstract

Learning-related changes in brain activity are thought to underlie adaptive behaviours1,2. For instance, the learning of a reward site by rodents requires the development of an over-representation of that location in the hippocampus3–6. How this learning-related change occurs remains unknown. Here we recorded hippocampal CA1 population activity as mice learned a reward location on a linear treadmill. Physiological and pharmacological evidence suggests that the adaptive over-representation required behavioural timescale synaptic plasticity (BTSP)7. BTSP is known to be driven by dendritic voltage signals that we proposed were initiated by input from entorhinal cortex layer 3 (EC3). Accordingly, the CA1 over-representation was largely removed by optogenetic inhibition of EC3 activity. Recordings from EC3 neurons revealed an activity pattern that could provide an instructive signal directing BTSP to generate the over-representation. Consistent with this function, our observations show that exposure to a second environment possessing a prominent reward-predictive cue resulted in both EC3 activity and CA1 place field density that were more elevated at the cue than at the reward. These data indicate that learning-related changes in the hippocampus are produced by synaptic plasticity directed by an instructive signal from the EC3 that seems to be specifically adapted to the behaviourally relevant features of the environment.

Goal-seeking compresses neural codes for space in the human hippocampus and orbitofrontal cortex

“…The context provided by a spatial goal can also distort the representation of space without provoking gross changes in the neural code. Place cells tend to over-represent behaviourally significant spatial locations, and can accumulate around [27][28][29] or fire excess spikes at [30] a rewarded position. Place cells may also encode information about prospective as well as current locations on the spatial trajectory [31][32][33], and information about future states has also been observed in both hippocampal BOLD signals [34,35] and intracranial recordings from the human medial temporal lobe (MTL) [13,14,36].…”

The formation of an expanding memory representation in the hippocampus

“…To investigate what mechanisms might be responsible for the different levels of stability observed in the above two populations we more closely examined the activity of each PC on the first day that it became active (first day of appearance). Specifically, we looked for the known signatures of behavioral timescale synaptic plasticity (BTSP) within each population [23][24][25] . BTSP is a directed form of synaptic weight plasticity that is induced when input from the entorhinal cortex (EC3) drives Ca 2+ plateau potentials in the dendrites of CA1 neurons.…”

“…BTSP is a directed form of synaptic weight plasticity that is induced when input from the entorhinal cortex (EC3) drives Ca 2+ plateau potentials in the dendrites of CA1 neurons. Accumulating evidence suggests BTSP is the primary mechanism of PF formation and learning-related changes in CA1 population activity [23][24][25][26][27][28] . Here, we found that both the transient and sustained groups showed prominent signatures of BTSP induction on the first day of appearance (Fig.…”

Efficient inference of synaptic plasticity rule with Gaussian process regression

“…In the case of indirect inference from network activities, two-photon calcium imaging can be used because stable imaging over consecutive days and weeks enables tracing learning-related changes in vivo. 32 , 51 , 52 Also, recent technical advancement allows cortex-wide monitoring of neuronal circuits at a cellular resolution. 53 , 54 Thus, tracing pre- and postsynaptic activities together during learning might be available in the near future, and our method to infer plasticity rules from network activities can be applied.…”

“…Recent experimental developments in optical recording and manipulating neural activities have been applied to investigate synaptic plasticity in vivo. 30 , 31 , 32 Inspired by these studies, we explored the method to efficiently infer the complete form of synaptic plasticity. For this, we considered various firing-rate-dependent synaptic plasticity rules obtained from a few representative models, Hebbian-type covariance rules, 33 phenomenological models describing synaptic plasticity with a triplet of spikes, 13 and biologically inspired models based on postsynaptic calcium dynamics.…”