Biology subjects

Tian, L. Y.

Publications and source records attributed to Tian, L. Y..

5 recordsLinked to original sources

bioRxiv2026

A neural circuit algorithm for action grammar

How does the brain generate sequences of discrete elements, such as actions or words, that follow an abstract rule (a grammar)? Motivated by an experiment in which monkeys learned to draw multi-object images according to grammatical rules, we trained a recurrent neural network to generate sequences Am Bn: producing shape A m times, then B n times, or vice versa. Our findings are threefold. First, the network plans the sequence by moving to an initial state encoding m and n along orthogonal directions for upcoming execution. Second, the network repeats each shape by progressing along a shape-specific manifold. Third, transitions from Am to Bn follow parallel trajectories between manifolds across values of n, thus preserving n. Each of these is an experimentally testable prediction. The network generalizes to longer novel sequences by starting further along the same orthogonal directions. These results reveal a neural circuit implementation of a symbolic algorithm for generating rule-based action sequences.

neuroscience↗

bioRxiv2026

Neural representation of action grammar structure in primate frontal cortex

Humans and other animals can solve new problems, even on the first attempt. This capacity to generate novel problem-solving behavior has been hypothesized to depend on brain mechanisms for recombining units of knowledge using systems of procedural rules, or grammars. Yet, whether and how the brain represents and implements grammars remains unclear, in large part due to the lack of neural recordings (typically performed in animal models) during grammar-based problem solving (typically studied in humans). Here, we address that gap, and in turn identify an underlying neural basis of grammatical behavior. We designed a visual-motor construction task in which macaque monkeys trace complex, often novel, geometric figures by generating a sequence of strokes, each a distinct shape, like a circle, dash, or chevron (action symbols1). Critically, sequencing was guided by a learned hierarchical action grammar AnBmCk, meaning "repeat shape A, then repeat shape B, then repeat shape C", where the number of repetitions (n, m, and k) varied across problems. Behavior was internally generated (no cues guiding stroke order), problem-directed (to construct a specific image), and exhibited zero-shot generalization (successful on the first trial for novel problems, including harder ones)--key hallmarks of grammar use. To identify underlying neural substrates, we recorded from multiple frontal cortical areas previously implicated in rule use or action sequencing. Activity during drawing encoded key structural properties of the action grammar: (i) shape index (A, B, C), (ii) abstract role (e.g., the role A independent of the specific shape), and (iii) ordinal position within a shape repeat. Moreover, all three properties were strongest in a single region, the pre-supplementary motor area (preSMA). We suggest the possibility that this conjunction of representations is a signature of an implementation of an iterative algorithm resembling a "for-loop" program: internally tracking how many repetitions remain for each shape index and switching to the next when complete. Our study establishes a paradigm for studying the neural basis of grammar-guided novel behavior and identifies in preSMA a dynamic representation of grammatical structure supporting such behavior.

neuroscience↗

bioRxiv2025

Neural representation of action symbols in primate frontal cortex

A hallmark of intelligence is proficiency in solving new problems, including those that differ dramatically from problems seen before. Problem-solving, in turn, depends on goal-directed generation of novel ideas and behaviors1, which has been proposed to rely on internal representations of discrete units, or symbols, and processes that recombine these units into a large set of possible composite representations1-9. Although this view has been influential in formulating cognitive-level explanations of behavior, definitive evidence for a neuronal substrate of symbols has remained elusive. Here, we identify a neural population encoding action symbols--recombinable representations of discrete units of motor behavior--localized to a specific area of frontal cortex. In macaque monkeys performing a drawing-like task, we found behavioral evidence that action elements (strokes) exhibit three critical features indicating an underlying symbolic representation: (i) invariance over low-level motor parameters; (ii) categorical structure, reflecting discrete types of action; and (iii) recombination into novel sequences. In simultaneous neural recordings across motor, premotor, and prefrontal cortex, we found that planning-related population activity in ventral premotor cortex (PMv) encodes actions in a manner that, like behavior, reflects motor invariance, categorical structure, and recombination. Activity in no other recorded area exhibited these three properties of symbols. These findings reveal a neural representation of action symbols localized to PMv, and therefore identify a putative neural substrate for symbolic cognitive operations.

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bioRxiv2022

Dynamic top-down biasing implements rapid adaptive changes to individual movements

Complex behaviors depend on the coordinated activity of neural ensembles in interconnected brain areas. The behavioral function of such coordination, often measured as co-fluctuations in neural activity across areas, is poorly understood. One hypothesis is that rapidly varying co-fluctuations may be a signature of moment-by-moment task-relevant influences of one area on another. We tested this possibility for error-corrective adaptation of birdsong, a form of motor learning which has been hypothesized to depend on the top-down influence of a higher-order area, LMAN, in shaping moment-by-moment output from a primary motor area, RA. In paired recordings of LMAN and RA in singing birds, we discovered a neural signature of a top-down influence of LMAN on RA, quantified as an LMAN-leading co-fluctuation between these areas. During learning, this cofluctuation strengthened in a premotor temporal window linked to the specific movement, sequential context, and acoustic modification associated with learning. Moreover, transient perturbation of LMAN activity specifically within this premotor window caused rapid occlusion of pitch modifications, consistent with LMAN conveying a temporally localized motor-biasing signal. Combined, our results reveal a dynamic top-down influence of LMAN on RA that varies on the rapid timescale of individual movements and is flexibly linked to contexts associated with learning. This finding indicates that inter-area co-fluctuations can be a signature of dynamic top-down influences that support complex behavior and its adaptation.

neuroscience↗

bioRxiv2020

Songbirds can learn flexible contextual control over syllable sequencing

The flexible control of sequential behavior is a fundamental aspect of speech, enabling endless reordering of a limited set of learned vocal elements (syllables or words). Songbirds are phylogenetically distant from humans but share both the capacity for vocal learning and neural circuitry for vocal control that includes direct pallial-brainstem projections. Based on these similarities, we hypothesized that songbirds might likewise be able to learn flexible, moment by-moment control over vocalizations. Here, we demonstrate that Bengalese finches (Lonchura striata domestica), which sing variable syllable sequences, can learn to rapidly modify the probability of specific sequences (e.g. ab-c versus ab-d) in response to arbitrary visual cues. Moreover, once learned, this modulation of sequencing occurs immediately following changes in contextual cues and persists without external reinforcement. Our findings reveal a capacity in songbirds for learned contextual control over syllable sequencing that parallels human cognitive control over syllable sequencing in speech.

neuroscience↗