CA3 Circuit Model Compressing Sequential Information in Theta Oscillation and Replay.

The hippocampus plays a critical role in the compression and retrieval of sequential information. During wakefulness, it achieves this through theta phase precession and theta sequences. Subsequently, during periods of sleep or rest, the compressed information reactivates through sharp-wave ripple events, manifesting as memory replay. However, how these sequential neuronal activities are generated and how they store information about the external environment remain unknown. We developed a hippocampal cornu ammonis 3 (CA3) computational model based on anatomical and electrophysiological evidence from the biological CA3 circuit to address these questions. The model comprises theta rhythm inhibition, place input, and CA3-CA3 plastic recurrent connection. The model can compress the sequence of the external inputs, reproduce theta phase precession and replay, learn additional sequences, and reorganize previously learned sequences. A gradual increase in synaptic inputs, controlled by interactions between theta-paced inhibition and place inputs, explained the mechanism of sequence acquisition. This model highlights the crucial role of plasticity in the CA3 recurrent connection and theta oscillational dynamics and hypothesizes how the CA3 circuit acquires, compresses, and replays sequential information.

Distributed sensory coding by cerebellar complex spikes in units of cortical segments.

Sensory processing is essential for motor control. Climbing fibers from the inferior olive transmit sensory signals to Purkinje cells, but how the signals are represented in the cerebellar cortex remains elusive. To examine the olivocerebellar organization of the mouse brain, we perform quantitative Ca2+ imaging to measure complex spikes (CSs) evoked by climbing fiber inputs over the entire dorsal surface of the cerebellum simultaneously. The surface is divided into approximately 200 segments, each composed of ∼100 Purkinje cells that fire CSs synchronously. Our in vivo imaging reveals that, although stimulation of four limb muscles individually elicits similar global CS responses across nearly all segments, the timing and location of a stimulus are derived by Bayesian inference from coordinated activation and inactivation of multiple segments on a single trial basis. We propose that the cerebellum performs segment-based, distributed-population coding that represents the conditional probability of sensory events.

Task-Related Synaptic Changes Localized to Small Neuronal Population in Recurrent Neural Network Cortical Models.

Humans have flexible control over cognitive functions depending on the context. Several studies suggest that the prefrontal cortex (PFC) controls this cognitive flexibility, but the detailed underlying mechanisms remain unclear. Recent developments in machine learning techniques allow simple PFC models written as a recurrent neural network to perform various behavioral tasks like humans and animals. Computational modeling allows the estimation of neuronal parameters that are crucial for performing the tasks, which cannot be observed by biologic experiments. To identify salient neural-network features for flexible cognition tasks, we compared four PFC models using a context-dependent integration task. After training the neural networks with the task, we observed highly plastic synapses localized to a small neuronal population in all models. In three of the models, the neuronal units containing these highly plastic synapses contributed most to the performance. No common tendencies were observed in the distribution of synaptic strengths among the four models. These results suggest that task-dependent plastic synaptic changes are more important for accomplishing flexible cognitive tasks than the structures of the constructed synaptic networks.

Excitatory Neuronal Hubs Configure Multisensory Integration of Slow Waves in Association Cortex.

Multisensory integration (MSI) is a fundamental emergent property of the mammalian brain. During MSI, perceptual information encoded in patterned activity is processed in multimodal association cortex. The systems-level neuronal dynamics that coordinate MSI, however, are unknown. Here, we demonstrate intrinsic hub-like network activity in the association cortex that regulates MSI. We engineered calcium reporter mouse lines based on the fluorescence resonance energy transfer sensor yellow cameleon (YC2.60) expressed in excitatory or inhibitory neurons. In medial and parietal association cortex, we observed spontaneous slow waves that self-organized into hubs defined by long-range excitatory and local inhibitory circuits. Unlike directional source/sink-like flows in sensory areas, medial/parietal excitatory and inhibitory hubs had net-zero balanced inputs. Remarkably, multisensory stimulation triggered rapid phase-locking mainly of excitatory hub activity persisting for seconds after the stimulus offset. Therefore, association cortex tends to form balanced excitatory networks that configure slow-wave phase-locking for MSI. VIDEO ABSTRACT.

Identification of transcriptional regulatory elements for Ntng1 and Ntng2 genes in mice.

BACKGROUND: Higher brain function is supported by the precise temporal and spatial regulation of thousands of genes. The mechanisms that underlie transcriptional regulation in the brain, however, remain unclear. The Ntng1 and Ntng2 genes, encoding axonal membrane adhesion proteins netrin-G1 and netrin-G2, respectively, are paralogs that have evolved in vertebrates and are expressed in distinct neuronal subsets in a complementary manner. The characteristic expression patterns of these genes provide a part of the foundation of the cortical layer structure in mammals. RESULTS: We used gene-targeting techniques, bacterial artificial chromosome (BAC)-aided transgenesis techniques, and in vivo enhancer assays to examine transcriptional mechanisms in vivo to gain insight into how the characteristic expression patterns of these genes are acquired. Analysis of the gene expression patterns in the presence or absence of netrin-G1 and netrin-G2 functional proteins allowed us to exclude the possibility that a feedback or feedforward mechanism mediates their characteristic expression patterns. Findings from the BAC deletion series revealed that widely distributed combinations of cis-regulatory elements determine the differential gene expression patterns of these genes and that major cis-regulatory elements are located in the 85-45 kb upstream region of Ntng2 and in the 75-60 kb upstream region and intronic region of Ntng1. In vivo enhancer assays using 2-kb evolutionarily conserved regions detected enhancer activity in the distal upstream regions of both genes. CONCLUSIONS: The complementary expression patterns of Ntng1 and Ntng2 are determined by transcriptional cis-regulatory elements widely scattered in these loci. The cis-regulatory elements characterized in this study will facilitate the development of novel genetic tools for functionally dissecting neural circuits to better understand vertebrate brain function.