Superficial-layer versus deep-layer lateral entorhinal cortex: Coding of allocentric space, egocentric space, speed, boundaries, and corners.

Entorhinal cortex is the major gateway between the neocortex and the hippocampus and thus plays an essential role in subserving episodic memory and spatial navigation. It can be divided into the medial entorhinal cortex (MEC) and the lateral entorhinal cortex (LEC), which are commonly theorized to be critical for spatial (context) and non-spatial (content) inputs, respectively. Consistent with this theory, LEC neurons are found to carry little information about allocentric self-location, even in cue-rich environments, but they exhibit egocentric spatial information about external items in the environment. The superficial and deep layers of LEC are believed to mediate the input to and output from the hippocampus, respectively. As earlier studies mainly examined the spatial firing properties of superficial-layer LEC neurons, here we characterized the deep-layer LEC neurons and made direct comparisons with their superficial counterparts in single unit recordings from behaving rats. Because deep-layer LEC cells received inputs from hippocampal regions, which have strong selectivity for self-location, we hypothesized that deep-layer LEC neurons would be more informative about allocentric position than superficial-layer LEC neurons. We found that deep-layer LEC cells showed only slightly more allocentric spatial information and higher spatial consistency than superficial-layer LEC cells. Egocentric coding properties were comparable between these two subregions. In addition, LEC neurons demonstrated preferential firing at lower speeds, as well as at the boundary or corners of the environment. These results suggest that allocentric spatial outputs from the hippocampus are transformed in deep-layer LEC into the egocentric coding dimensions of LEC, rather than maintaining the allocentric spatial tuning of the CA1 place fields.

Effects of early life stress during stress hyporesponsive period (SHRP) on anxiety and curiosity in adolescent rats.

Repeated exposure to adverse experiences in early life, termed Early Life Stress (ELS), can increase anxiety disorders later in life. Anxiety is directly associated with curiosity, a form of intrinsic drive state associated with increased novelty-seeking behaviour and risk taking for challenging opportunities and could probably modulate learning and memory. In humans, elevated curiosity during adolescence tends to elicit increased exploration, novelty seeking, high risk-taking behaviour and heightened emotionality. Such behaviours are beneficial in maintaining social skills and cognitive functions later in life. We investigated whether ELS-induced anxiety impacts curiosity-like behaviour at adolescence in an animal model. ELS was induced by subjecting Sprague Dawley rat pups to maternal separation and isolation (MS) stress during the stress hyporesponsive period (SHRP) from post-natal days (PND) 4-PND 14. This rat model was tested for anxiety, spontaneous exploratory behaviour and curiosity-like behaviour in a custom-designed arena during adolescence (PND 30-45). ELS-induced changes in the stress were confirmed by corticosterone, while, basal dopamine level was estimated to understand the neurochemical basis of MS stress-induced changes in curiosity. We observed an increase in the levels of anxiety and intrinsic drive state such as curiosity-like behaviour, which was associated with elevated plasma corticosterone and dopamine in MS animals during adolescence suggesting the impact of ELS during SHRP on adolescent behaviour.

Scale Invariance in Lateral Head Scans During Spatial Exploration.

Universality connects various natural phenomena through physical principles governing their dynamics, and has provided broadly accepted answers to many complex questions, including information processing in neuronal systems. However, its significance in behavioral systems is still elusive. Lateral head scanning (LHS) behavior in rodents might contribute to spatial navigation by actively managing (optimizing) the available sensory information. Our findings of scale invariant distributions in LHS lifetimes, interevent intervals and event magnitudes, provide evidence for the first time that the optimization takes place at a critical point in LHS dynamics. We propose that the LHS behavior is responsible for preprocessing of the spatial information content, critical for subsequent foolproof encoding by the respective downstream neural networks.