The Unexplored Territory of Neural Models: Potential Guides for Exploring the Function of Metabotropic Neuromodulation

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

Gated transformations from egocentric to allocentric reference frames involving retrosplenial cortex, entorhinal cortex, and hippocampus

This paper reviews the recent experimental finding that neurons in behaving rodents show egocentric coding of the environment in a number of structures associated with the hippocampus. Many animals generating behavior on the basis of sensory input must deal with the transformation of coordinates from the egocentric position of sensory input relative to the animal, into an allocentric framework concerning the position of multiple goals and objects relative to each other in the environment. Neurons in retrosplenial cortex show egocentric coding of the position of boundaries in relation to an animal. These neuronal responses are discussed in relation to existing models of the transformation from egocentric to allocentric coordinates using gain fields and a new model proposing transformations of phase coding that differ from current models. The same type of transformations could allow hierarchical representations of complex scenes. The responses in rodents are also discussed in comparison to work on coordinate transformations in humans and non-human primates.

Glioma‑neuronal interactions in tumor progression: Mechanism, therapeutic strategies and perspectives (Review)

An increasing body of evidence has become available to reveal the synaptic and functional integration of glioma into the brain network, facilitating tumor progression. The novel discovery of glioma-neuronal interactions has fundamentally challenged our understanding of this refractory disease. The present review aimed to provide an overview of how the neuronal activities function through synapses, neurotransmitters, ion channels, gap junctions, tumor microtubes and neuronal molecules to establish communications with glioma, as well as a simplified explanation of the reciprocal effects of crosstalk on neuronal pathophysiology. In addition, the current state of therapeutic avenues targeting critical factors involved in glioma-euronal interactions is discussed and an overview of clinical trial data for further investigation is provided. Finally, newly emerging technologies, including immunomodulation, a neural stem cell-based delivery system, optogenetics techniques and co-culture of neuron organoids and glioma, are proposed, which may pave a way towards gaining deeper insight into both the mechanisms associated with neuron- and glioma-communicating networks and the development of therapeutic strategies to target this currently lethal brain tumor.

Learning an Efficient Hippocampal Place Map from Entorhinal Inputs Using Non-Negative Sparse Coding

Cells in the entorhinal cortex (EC) contain rich spatial information and project strongly to the hippocampus where a cognitive map is supposedly created. These cells range from cells with structured spatial selectivity, such as grid cells in the medial EC (MEC) that are selective to an array of spatial locations that form a hexagonal grid, to weakly spatial cells, such as non-grid cells in the MEC and lateral EC (LEC) that contain spatial information but have no structured spatial selectivity. However, in a small environment, place cells in the hippocampus are generally selective to a single location of the environment, while granule cells in the dentate gyrus of the hippocampus have multiple discrete firing locations but lack spatial periodicity. Given the anatomic connection from the EC to the hippocampus, how the hippocampus retrieves information from upstream EC remains unclear. Here, we propose a unified learning model that can describe the spatial tuning properties of both hippocampal place cells and dentate gyrus granule cells based on non-negative sparse coding from EC inputs. Sparse coding plays an important role in many cortical areas and is proposed here to have a key role in the hippocampus. Our results show that the hexagonal patterns of MEC grid cells with various orientations, grid spacings and phases are necessary for the model to learn different place cells that efficiently tile the entire spatial environment. However, if there is a lack of diversity in any grid parameters or a lack of hippocampal cells in the network, this will lead to the emergence of hippocampal cells that have multiple firing locations. More surprisingly, the model can also learn hippocampal place cells even when weakly spatial cells, instead of grid cells, are used as the input to the hippocampus. This work suggests that sparse coding may be one of the underlying organizing principles for the navigational system of the brain.

Distinct burst properties contribute to the functional diversity of thalamic nuclei

Thalamic neurons fire spikes in two modes, burst and tonic. The function of burst firing is unclear, but the evidence suggests that bursts are more effective at activating cortical cells, and that postinhibition rebound bursting contributes to thalamocortical oscillations during sleep. Bursts are considered stereotyped signals; however, there is limited evidence regarding how burst properties compare across thalamic nuclei of different functional or anatomical organization. Here, we used whole-cell patch clamp recordings and compartmental modeling to investigate the properties of bursts in six sensory thalamic nuclei, to study the mechanisms that can lead to different burst properties, and to assess the implications of different burst properties for thalamocortical transmission and oscillatory functions. We found that bursts in higher-order cells on average had higher number of spikes and longer latency to the first spike. Additionally, burst features in first-order neurons were determined by sensory modality. Shifting the voltage-dependence and density of the T-channel conductance in a compartmental model replicates the burst properties from the intracellular recordings, pointing to molecular mechanisms that can generate burst diversity. Furthermore, the model predicts that bursts with higher number of spikes will drastically reduce the effectiveness of thalamocortical transmission. In addition, the latency to burst limited the rebound oscillatory frequency in modeled cells. These results demonstrate that burst properties vary according to the thalamocortical hierarchy and with sensory modality. The findings imply that, while in burst mode, thalamocortical transmission and firing frequency will be determined by the number of spikes and latency to burst.