ICTS

The song of the adult male zebra finch is a well-studied example of an ethologically relevant, learned, motor sequence and how such motor sequences are initiated remains poorly understood. Zebra finch song bouts typically begin with a variable number of introductory notes (INs). We have shown that the timing and acoustic properties of INs reach a consistent state before each song suggesting that INs "prepare" the brain for song initiation. Here, we examine the nature of this preparation. First, we show that the speeding up of gaps between INs is unique to INs and is correlated with time to song initiation. Second, by recording neural activity in the singing male zebra finch, we find a gradual change in the active population of neurons as INs repeat. Third, we find changes in respiratory pressure as INs repeat with a consistent increase in the coordination between respiration and vocalizations. Finally, in a small subset of zebra finches that begin their songs without INs (~1-2% of birds in our colony) we find IN-like, silent, expiratory pulses and IN-like premotor activity before song initiation. Overall, these results suggest that INs reflect neural processes important for specifying the time of song initiation, possibly by establishing proper coordination between respiratory and vocal neural circuits.

ynchronization of rhythmic units is essential for various biological functions. The synchronization mechanism is often governed by neural networks. For example, the circadian rhythm in mammals functions by synchronizing the gene expression rhythms of individual neurons within a neural tissue called the suprachiasmatic nucleus. Various movement patterns observed in animal locomotion, such as walking and swimming, are generated by neural networks known as central pattern generators. The synchronization dynamics of oscillator groups strongly depend on the heterogeneity of intrinsic frequencies, noise intensity, and the interaction network. If these factors can be estimated from observations, it can aid in understanding, predicting, and controlling the system, and contribute to elucidating the design principles of robust systems. However, estimation involves many challenges. Among them, estimation becomes exponentially difficult as the model's dimensions and the number of parameters increase. Therefore, it is desirable to assume a model with as low dimensions and as few parameters as possible. In the synchronization phenomena of oscillator groups, the phase oscillator model is expected to be useful. This presentation introduces research on estimation using the phase oscillator model [1,2]. [1]A Matsuki, H Kori, R Kobayashi: Network inference from oscillatory signals based on circle map, arXiv:2407.07445 (2024) [2]F Mori, H Kori: Noninvasive inference methods for interaction and noise intensities of coupled oscillators using only spike time data, Proceedings of the National Academy of Sciences 119, e2113620119 (2022)

Grid cells in the medial entorhinal cortex (MEC) fire when an animal is located at the vertices of a hexagonal grid that extends across the environment. The population activity of grid cells serves as an allocentric representation of the current location of the animal. Recent studies have identified a class of grid cells that represent locations ahead of the animal. How do these predictive representations emerge from the wetware of the MEC? To address this question, we developed a detailed conductance-based model of the MEC network, constrained by existing data on the biophysical properties of stellate cells and the topology of the MEC network. Our model revealed two mechanisms underlying the emergence of a predictive code in the MEC. The first relied on a time scale associated with the HCN conductance. The other depended on the degree of asymmetry in the topology of the MEC network. The former mechanism was sufficient to explain predictive coding in layer II grid cells that represented locations shifted ahead of the current location. The shift was equivalent to ~5% of the diameter of a grid field. The latter mechanism was required to model predictive representations in layer III grid cells that were shifted forward by a distance of ~25% of the diameter of a grid field. A corollary of our model, that the extent of the predictive code changes monotonically along the dorsoventral axis of the MEC following observed changes in the properties of the HCN conductance, is borne out by recent experiments.

Nonlinear dynamics aims to elucidate the basic mechanisms necessary to reflect the temporal behavior of a natural system. The data analysis and modeling techniques proposed by artificial intelligence (deep networks, computational reservoirs, recurrent networks, as examples), on the other hand, ostensibly resign the mechanistic vision for a data-oriented modeling paradigm.  In these lectures, these apparently antagonistic approaches will be analyzed in parallel, using as examples my work on the physics of birdsong production and vocal learning.

The hippocampus supports two important functions: spatial navigation and the storage of episodic memories. Yet, how these two seemingly distinct roles converge in a single circuit remains an open question. In this talk, I will present a neural model that leverages the low-dimensional attractor dynamics of spatial "grid cells" to implement associative, spatial, and episodic memory, thus unifying these distinct functions. Our model, Vector-HaSH (Vector Hippocampal Scaffolded Heteroassociative Memory) operates through a factorization of the content of memories from dynamics that generate error-correcting stable states. This leads to a graceful trade-off between number of stored items and recall detail, unlike the abrupt capacity limits found in classical Hopfield-like memory models. We find that the usage of pre-structured low-dimensional representations also enables high-capacity sequence memorization by recasting the chaining problem of high-dimensional states into one of learning low-dimensional transitions. Further, our presented approach reproduces several hippocampal experiments on spatial mapping and context-dependent representations, and provides a circuit model of the 'memory palaces' used by memory athletes. Thus, this work provides a unified framework for understanding how the hippocampus simultaneously supports spatial mapping, associative memory, and episodic memory.

The basal ganglia are known to influence action selection and modulation of movement vigor, but whether and how they contribute to specifying the kinematics of learned motor skills is not understood. Here, we probe this question by recording and manipulating basal ganglia activity in rats trained to generate complex task-specific movement patterns with rich kinematic structure. We find that the sensorimotor arm of the basal ganglia circuit is crucial for generating the detailed movement patterns underlying the acquired motor skills. Furthermore, the neural representations in the striatum, and the control function they subserve, do not depend on inputs from the motor cortex. Taken together, these results extend our understanding of the basal ganglia by showing that they can specify and control the fine-grained details of learned motor skills through their interactions with lower-level motor circuits.