During spatial navigation, neural activity in the hippocampus and the medial entorhinal cortex (MEC) is correlated to navigational variables like location1,2, head direction3, speed4, and proximity to boundaries5. These activity patterns are thought to provide a map-like representation of physical space. However, the hippocampal/entorhinal circuit is involved not only in spatial navigation, but in a variety of memory-guided behaviors6. The relationship between this general function and the specialized spatial activity patterns is unclear. A conceptual framework reconciling these views is that spatial representation is just one example of a more general mechanism for encoding continuous, task-relevant variables7–10. We tested this idea by recording hippocampal and entorhinal neurons in a task that required rats to use a joystick to manipulate sound along a continuous frequency axis. We found neural representation of the entire behavioral task, including activity that formed discrete firing fields at particular sound frequencies. Neurons involved in this representation overlapped with the known spatial cell types in the circuit like place cells and grid cells. These results suggest that common circuit mechanisms in the hippocampal/entorhinal system are used for representations of diverse behavioral tasks, possibly supporting cognitive processes beyond spatial navigation.

Hippocampal neurons fire selectively in local behavioral contexts such as the position in an environment or phase of a task, 1-3 and are thought to form a cognitive map of task-relevant variables. 1, 4, 5 However, their activity varies over repeated behavioral conditions, 6 such as di↵erent runs through the same position or repeated trials. Although widely observed across the brain, 7-10 such variability is not well understood, and could reflect noise or structure, such as the encoding of additional cognitive information. 6,[11][12][13] Here, we introduce a conceptual model to explain variability in terms of underlying, population-level structure in single-trial neural activity. To test this model, we developed a novel unsupervised learning algorithm incorporating temporal dynamics, in order to characterize population activity as a trajectory on a nonlinear manifold-a space of possible network states. The manifold's structure captures correlations between neurons and temporal relationships between states, constraints arising from underlying network architecture and inputs. Using measurements of activity over time but no information about exogenous behavioral variables, we recovered hippocampal activity manifolds during spatial and non-spatial cognitive tasks in rats. Manifolds were low-dimensional and smoothly encoded task-related variables, but contained an extra dimension reflecting information beyond the measured behavioral variables. Consistent with our model, neurons fired as a function of overall network state, and fluctuations in their activity across trials corresponded to variation in the underlying trajectory on the manifold. In particular, the extra dimension allowed the system to take di↵erent trajectories despite repeated behavioral conditions. Furthermore, the trajectory could temporarily decouple from current behavioral conditions and traverse neighboring manifold points corresponding to past, future, or nearby behavioral states. Our results suggest that trial-to-trial variability in the hippocampus is structured, and may reflect the operation of internal cognitive processes. The manifold structure of population activity is well-suited for organizing information to support memory, 1, 5, 14 planning, 12, 15, 16 and reinforcement learning. 17, 18 In general, our approach could find broader use in probing the organization and computational role of circuit dynamics in other brain regions.

Anatomic evaluation is an important aspect of many studies in neuroscience; however, it often lacks information about the three-dimensional structure of the brain. Micro-CT imaging provides an excellent, nondestructive, method for the evaluation of brain structure, but current applications to neurophysiological or lesion studies require removal of the skull as well as hazardous chemicals, dehydration, or embedding, limiting their scalability and utility. Here we present a protocol using eosin in combination with bone decalcification to enhance contrast in the tissue and then employ monochromatic and propagation phase-contrast micro-CT imaging to enable the imaging of brain structure with the preservation of the surrounding skull. Instead of relying on descriptive, time-consuming, or subjective methods, we develop simple quantitative analyses to map the locations of recording electrodes and to characterize the presence and extent of hippocampal brain lesions.

14Anatomic evaluation is an important aspect of many studies in neuroscience; however, it 15 often lacks information about the three-dimensional structure of the brain. With appropriate 16 contrast, micro-CT imaging provides an excellent, nondestructive, method for the evaluation 17 of brain structure, but currently available protocols are labor intensive, use hazardous 18 compounds, complicate standard histological analysis, and require the removal of the skull. 19Here we present a simple protocol that solves those problems. We use eosin in combination 20with bone decalcification to create contrast in the tissue and then employ monochromatic 21 and propagation phase-contrast micro-CT imaging to enable the imaging of brain structure 22with the preservation of the surrounding skull. Instead of relying on descriptive, time-23 consuming, or subjective methods, we develop simple quantitative analyses to map the 24 locations of recording electrodes and to characterize the presence and extent of brain lesions. 25 26