Contextual and pure time coding for self and other in the hippocampus

Hippocampal coding of identity, sex, hierarchy, and affiliation in a social group of wild fruit bats

Social animals live in groups and interact volitionally in complex ways. However, little is known about neural responses under such natural conditions. Here, we investigated hippocampal CA1 neurons in a mixed-sex group of five to 10 freely behaving wild Egyptian fruit bats that lived continuously in a laboratory-based cave and formed a stable social network. In-flight, most hippocampal place cells were socially modulated and represented the identity and sex of conspecifics. Upon social interactions, neurons represented specific interaction types. During active observation, neurons encoded the bat's own position and head direction, together with the position, direction, and identity of multiple conspecifics. Identity-coding neurons encoded the same bat across contexts. The strength of identity coding was modulated by sex, hierarchy, and social affiliation. Thus, hippocampal neurons form a multidimensional sociospatial representation of the natural world.

Integration and competition between space and time in the hippocampus

Episodic memory is organized in both spatial and temporal contexts. The hippocampus is crucial for episodic memory and has been demonstrated to encode spatial and temporal information. However, how the representations of space and time interact in the hippocampal memory system is still unclear. Here, we recorded the activity of hippocampal CA1 neurons in mice in a variety of one-dimensional navigation tasks while systematically varying the speed of the animals. For all tasks, we found neurons simultaneously represented space and elapsed time. There was a negative correlation between the preferred space and lap duration, e.g., the preferred spatial position shifted more toward the origin when the lap duration became longer. A similar relationship between the preferred time and traveled distance was also observed. The results strongly suggest a competitive and integrated representation of space-time by single hippocampal neurons, which may provide the neural basis for spatiotemporal contexts.

Mice integrate conspecific and contextual information in forming social episodic-like memories under spontaneous recognition task conditions

The ability to remember unique past events (episodic memory) may be an evolutionarily conserved function, with accumulating evidence of episodic-(like) memory processing in rodents. In humans, it likely contributes to successful complex social networking. Rodents, arguably the most used laboratory models, are also rather social animals. However, many behavioural paradigms are devoid of sociality, and commonly-used social spontaneous recognition tasks (SRTs) are open to non-episodic strategies based upon familiarity. We address this gap by developing new SRT variants. Here, in object-in-context SRTs, we asked if context could be specified by the presence/absence of either a conspecific (experiment 1) or an additional local object (experiment 2). We show that mice readily used the conspecific as contextual information to distinguish unique episodes in memory. In contrast, no coherent behavioural response emerged when an additional object was used as a potential context specifier. Further, in a new social conspecific-in-context SRT (experiment 3) where environment-based change was the context specifier, mice preferably explored a more recently-seen familiar conspecific associated with contextual mismatch, over a less recently-seen familiar conspecific presented in the same context. The results argue that, in incidental SRT conditions, mice readily incorporate conspecific cue information into episodic-like memory. Thus, the tasks offer different ways to assess and further understand the mechanisms at work in social episodic-like memory processing.

Understanding collective behavior through neurobiology

A variety of organisms exhibit collective movement, including schooling fish and flocking birds, where coordinated behavior emerges from the interactions between group members. Despite the prevalence of collective movement in nature, little is known about the neural mechanisms producing each individual's behavior within the group. Here we discuss how a neurobiological approach can enrich our understanding of collective behavior by determining the mechanisms by which individuals interact. We provide examples of sensory systems for social communication during collective movement, highlight recent discoveries about neural systems for detecting the position and actions of social partners, and discuss opportunities for future research. Understanding the neurobiology of collective behavior can provide insight into how nervous systems function in a dynamic social world.

Distinct neural adaptations to time demand in the striatum and the hippocampus

How do neural codes adjust to track time across a range of resolutions, from milliseconds to multi-seconds, as a function of the temporal frequency at which events occur? To address this question, we studied time-modulated cells in the striatum and the hippocampus, while macaques categorized three nested intervals within the sub-second or the supra-second range (up to 1, 2, 4, or 8 s), thereby modifying the temporal resolution needed to solve the task. Time-modulated cells carried more information for intervals with explicit timing demand, than for any other interval. The striatum, particularly the caudate, supported the most accurate temporal prediction throughout all time ranges. Strikingly, its temporal readout adjusted non-linearly to the time range, suggesting that the striatal resolution shifted from a precise millisecond to a coarse multi-second range as a function of demand. This is in line with monkey's behavioral latencies, which indicated that they tracked time until 2 s but employed a coarse categorization strategy for durations beyond. By contrast, the hippocampus discriminated only the beginning from the end of intervals, regardless of the range. We propose that the hippocampus may provide an overall poor signal marking an event's beginning, whereas the striatum optimizes neural resources to process time throughout an interval adapting to the ongoing timing necessity.

Brain-inspired multimodal hybrid neural network for robot place recognition

Place recognition is an essential spatial intelligence capability for robots to understand and navigate the world. However, recognizing places in natural environments remains a challenging task for robots because of resource limitations and changing environments. In contrast, humans and animals can robustly and efficiently recognize hundreds of thousands of places in different conditions. Here, we report a brain-inspired general place recognition system, dubbed NeuroGPR, that enables robots to recognize places by mimicking the neural mechanism of multimodal sensing, encoding, and computing through a continuum of space and time. Our system consists of a multimodal hybrid neural network (MHNN) that encodes and integrates multimodal cues from both conventional and neuromorphic sensors. Specifically, to encode different sensory cues, we built various neural networks of spatial view cells, place cells, head direction cells, and time cells. To integrate these cues, we designed a multiscale liquid state machine that can process and fuse multimodal information effectively and asynchronously using diverse neuronal dynamics and bioinspired inhibitory circuits. We deployed the MHNN on Tianjic, a hybrid neuromorphic chip, and integrated it into a quadruped robot. Our results show that NeuroGPR achieves better performance compared with conventional and existing biologically inspired approaches, exhibiting robustness to diverse environmental uncertainty, including perceptual aliasing, motion blur, light, or weather changes. Running NeuroGPR as an overall multi-neural network workload on Tianjic showcases its advantages with 10.5 times lower latency and 43.6% lower power consumption than the commonly used mobile robot processor Jetson Xavier NX.