Biology subjects

Kiani, R.

Publications and source records attributed to Kiani, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Zero-Shot Generalization of Prospective Planning in Macaques

Achieving a goal in natural environments often requires a sequence of actions whose consequences extend beyond their immediate outcomes. Because actions within a sequence can hinder or facilitate subsequent actions, successful behavior often demands prospective planning: selecting current actions based on their consequences for future actions. Although prospective planning is well documented in humans, its generalization to unfamiliar problems in nonhuman animals remains poorly understood. Here, we developed maze-navigation tasks in which macaque monkeys moved a token toward a goal while navigating around obstacles. Monkeys selected routes based on the positions of downstream obstacles, and their choices could not be explained by simple whole-maze visual summaries. In previously unseen, complex branching mazes, monkeys avoided deceptive dead ends--routes that initially approached the goal but ultimately failed--even when those dead ends extended several maze segments downstream. Prospective route selection was evident from the first encounters with multistep mazes and generalized to previously unencountered arrangements of familiar obstacles. These findings demonstrate zero-shot generalization of prospective planning: macaques applied a prospective strategy to unfamiliar multistep problems without learning the appropriate action sequences through repeated experience.

neuroscience↗

bioRxiv2026

Control of cortical population activity with patterned microstimulation

Closed-loop control of cortical activity is a central goal in systems neuroscience and clinical neuromodulation, but most approaches either rely on detailed circuit models that are unattainable in vivo or on open-loop stimulation tuned by trial and error. Here we introduce REACHable manifold Control (REACH-Ctrl), a data-driven brain-computer interface that achieves real-time control of population spiking activity using patterned microstimulation and multi-electrode recordings. REACH-Ctrl learns a finite-horizon controllability map directly from short training epochs in which random multi-electrode pulse sequences are delivered through a subset of electrodes while recording evoked responses. From these input-output data, it identifies the "reachable manifold" of population states and computes low-current microstimulation sequences that steer activity toward designated targets, without explicit knowledge of the underlying connectivity or dynamics. We test REACH-Ctrl in macaque prefrontal cortex, demonstrating high control accuracy, robust across sessions and stimulation parameters. Geometric analyses showed that multi-pulse sequences traverse a well-defined reachable manifold with substantial, but incomplete, overlap with the intrinsic neural activity manifold, revealing both on- and off-manifold components of control. Encoding models further revealed that, in our weak-stimulation regime, population responses to multi-electrode sequences are well approximated by the linear sum of localized "stimulation fields" with modest history dependence, explaining the success of our linear control approach. These results demonstrate precise, sample-efficient control of cortical population activity with clinically relevant microstimulation hardware, and provide a general blueprint for designing perturbations for sparsely observed neural circuits.

neuroscience↗

bioRxiv2026

Hierarchical binding of cognition to action in the human basal ganglia-thalamic circuit

Flexible behavior requires binding actions to their underlying cognitive variables, yet classical basal ganglia models emphasize a serial architecture where striatal action selection precedes a pallidothalamic motor gate. We tested this framework by recording single-neuron activity across the human pallidothalamic circuit while participants reported perceptual decisions with confidence-weighted reaching movements. Neurons in globus pallidus externus and internus, subthalamic nucleus, and motor thalamus exhibited sharp activity increases at movement onset that persisted through execution and feedback. Choice and confidence were continuously encoded before, during, and after movement. Population decoding revealed a transformation along the circuit, from dynamic upstream representations to stable, low-dimensional representations downstream, particularly in motor thalamus. After feedback, outcome signals emerged selectively in subthalamic nucleus and motor thalamus. In motor thalamus, confidence and outcome converged into a representation consistent with unsigned reward prediction error. These findings redefine the human pallidothalamic circuit as an interface that binds decision, action, and evaluation during behavior.

neuroscience↗

bioRxiv2026

The geometry of context-dependent biased decisions during learning

Adaptive behavior requires inferring latent context and rapidly adjusting decisions in response to changing environmental contingencies. We investigated how reward context is learned, represented, and updated during decision making. We recorded large populations of neurons in lateral prefrontal cortex while macaque monkeys learned a direction-discrimination task in which reward contingencies alternated unpredictably between favoring leftward and rightward choices. Once trained, monkeys inferred context switches from a single unexpected outcome, immediately adjusting both choice bias and reaction times--hallmarks of model-based inference. Early in learning, however, adaptation unfolded gradually across multiple trials. Neural population analyses revealed that reward context was encoded through systematic shifts in the geometry of neural representations. Accumulated sensory evidence (decision variable) and choice were organized along curvilinear decision manifolds, which were displaced across contexts primarily along the decision-variable axis. This geometry naturally implemented context-dependent biases: a fixed linear readout generated different choice tendencies across contexts without remapping. Longitudinal recordings further showed that, with learning, these representational transitions between manifolds became faster, mirroring the emergence of one-trial behavioral generalization. Recurrent neural networks trained on the same task reproduced both the behavioral signatures and the context-dependent geometric shifts. Together, these findings identify a mechanism by which prefrontal circuits support hierarchical inference: reward context is encoded as structured shifts in representational geometry, enabling rapid generalization and flexible control of decision policies.

neuroscience↗

bioRxiv2025

Inferotemporal cortex encodes formation and termination of perceptual decisions

Visually-guided decisions are often modeled as bounded accumulation of sensory evidence, with "ramping" activity in frontoparietal cortex taken as a canonical signature of integration. Here we show that anterior inferotemporal cortex (TEa) implements an alternative integration code during object-based decisions. In macaques performing stochastic face categorization, TEa neurons tracked momentary feature fluctuations while concurrently encoding an accumulated decision variable as a running average of task-relevant evidence. This running-mean code increased category signal-to-noise without ramps, supported flexible amplification of task-relevant features, and showed a sharp collapse in sensitivity to new evidence after commitment. These signatures emerged concurrently with, or slightly earlier than, those in simultaneously recorded LIP neurons, arguing against inheritance from feedback. A simple subunit model combining momentary evidence with a running-average decision variable accounts for TEa dynamics. Our results identify a previously unrecognized format for evidence integration and reposition TEa as a decision-aware hub within the ventral visual pathway.

neuroscience↗

bioRxiv2025

Towards model-based design of causal manipulations of brain circuits with high spatiotemporal precision

Recent advancements in neurotechnology enable precise spatiotemporal patterns of micros- timulations with single-cell resolution. The choice of perturbation sites must satisfy two key criteria: efficacy in evoking significant responses and selectivity for the desired target effects. This choice is currently based on laborious trial-and-error procedures, unfeasible for sequences of multi-site stimulations. Efficient methods to design complex perturbation patterns are ur- gently needed. Can we design a spatiotemporal pattern of stimulation to steer neural activity and behavior towards a desired target? We outline a method for achieving this goal in two steps. First, we identify the most effective perturbation sites, or hubs, only based on short observations of spontaneous neural activity. Second, we provide an efficient method to design multi-site stimulation patterns by combining approaches from nonlinear dynamical systems, control theory and data-driven methods. We demonstrate the feasibility of our approach using multi-site stimulation patterns in recurrent network models.

neuroscience↗

bioRxiv2021

Neuron particles capture network topology and behavior from single units

While networks of neurons, glia and vascular systems enable and support brain functions, to date, mathematical tools to decode network dynamics and structure from very scarce and partially observed neuronal spiking behavior remain underdeveloped. Large neuronal networks contribute to the intrinsic neuron transfer function and observed neuronal spike trains encoding complex causal information processing, yet how this emerging causal fractal memory in the spike trains relates to the network topology is not fully understood. Towards this end, we propose a novel statistical physics inspired neuron particle model that captures the causal information flow and processing features of neuronal spiking activity. Relying on synthetic comprehensive simulations and real-world neuronal spiking activity analysis, the proposed fractional order operators governing the neuronal spiking dynamics provide insights into the memory and scale of the spike trains as well as information about the topological properties of the underlying neuronal networks. Lastly, the proposed model exhibits superior predictions of animal behavior during multiple cognitive tasks.

neuroscience↗

bioRxiv2021

Recurrent neural circuits overcome partial inactivation by compensation and re-learning

1Technical advances in artificial manipulation of neural activity have precipitated a surge in studying the causal contribution of brain circuits to cognition and behavior. However, complexities of neural circuits challenge interpretation of experimental results, necessitating theoretical frameworks for system-atic explorations. Here, we take a step in this direction, using, as a testbed, recurrent neural networks trained to perform a perceptual decision. We show that understanding the computations implemented by network dynamics enables predicting the magnitude of perturbation effects based on changes in the networks phase plane. Inactivation effects are weaker for distributed network architectures, are more easily discovered with non-discrete behavioral readouts (e.g., reaction times), and vary considerably across multiple tasks implemented by the same circuit. Finally, networks that can "learn" during inactivation recover function quickly, often much faster than the original training time. Our framework explains past empirical observations by clarifying how complex circuits compensate and adapt to perturbations.

neuroscience↗

bioRxiv2021

The geometry of the representation of decision variable and stimulus difficulty in the parietal cortex

Lateral intraparietal (LIP) neurons represent formation of perceptual decisions involving eye movements. In circuit models for these decisions, neural ensembles that encode actions compete to form decisions. Consequently, decision variables (DVs) are represented as partially potentiated action plans, where ensembles increase their average responses for stronger evidence supporting their preferred actions. As another consequence, DV representation and readout are implemented similarly for decisions with identical competing actions, irrespective of input and task context differences. Here, we challenge those core principles using a novel face-discrimination task, where LIP firing rates decrease with supporting evidence, contrary to conventional motion-discrimination tasks. These opposite response patterns arise from similar mechanisms in which decisions form along curved population-response manifolds misaligned with action representations. These manifolds rotate in state space based on task context, necessitating distinct readouts. We show similar manifolds in lateral and medial prefrontal cortices, suggesting a ubiquitous representational geometry across decision-making circuits.

neuroscience↗

bioRxiv2020

Linear integration of sensory evidence over space and time underlies face categorization

Visual object recognition relies on elaborate sensory processes that transform retinal inputs to object representations, but it also requires decision-making processes that read out object representations and function over prolonged time scales. The computational properties of these decision-making processes remain underexplored for object recognition. Here, we study these computations by developing a stochastic multi-feature face categorization task. Using quantitative models and tight control of spatiotemporal visual information, we demonstrate that humans categorize faces through an integration process that first linearly adds the evidence conferred by task-relevant features over space to create aggregated momentary evidence, and then linearly integrates it over time with minimum information loss. Discrimination of stimuli along different category boundaries (e.g., identity or expression of a face) is implemented by adjusting feature weights of spatial integration. This linear but flexible integration process over space and time bridges past studies on simple perceptual decisions to complex object recognition behavior.

neuroscience↗

bioRxiv2020

Predicting perturbation effects from resting state activity using functional causal flow

A crucial challenge in targeted manipulation of neural activity is to identify perturbation sites whose stimulation exerts significant effects downstream (high efficacy), a procedure currently achieved by labor-intensive trial-and-error. Targeted perturbations will be greatly facilitated by understanding causal interactions within neural ensembles and predicting the efficacy of perturbation sites before intervention. Here, we address this issue by developing a computational framework to predict how single-site micorstimulation alters the ensemble spiking activity in an alert monkeys prefrontal cortex. Our framework uses delay embedding techniques to infer the ensembles functional causal flow (FCF) based on the functional interactions inferred at rest. We validate FCF using ground truth data from models of cortical circuits, showing that FCF is robust to noise and can be inferred from brief recordings of even a small fraction of neurons in the circuit. A detailed comparison of FCF with several alternative methods, including Granger causality and transfer entropy, highlighted the advantages of FCF in predicting perturbation effects on empirical data. Our results provide the foundation for using targeted circuit manipulations to develop targeted interventions suitable for brain-machine interfaces and ameliorating cognitive dysfunctions in the human brain.

neuroscience↗

bioRxiv2020

Distinct population code for movement kinematics and changes of ongoing movements in human subthalamic nucleus

The subthalamic nucleus (STN) is theorized to globally suppress movement through connections with downstream basal ganglia structures. Current theories are supported by increased STN activity when subjects withhold an uninitiated action plan, but a critical test of these theories requires studying STN responses when an ongoing action is replaced with an alternative. Here, we perform this test using an extended reaching task with instructions to switch movement trajectory mid-action. We show that STN activity decreases during action switches, contrary to prevalent theories. Further, beta oscillations in the local field potential in STN, which are associated with movement inhibition do not show increased power or entraining of neuronal firing during switches. We report an inhomogeneous population neural code in STN, with one sub-population encoding movement kinematics and direction and another encoding unexpected action switches. We suggest an elaborate neural code in STN that contributes to planning actions and changing the plans.

neuroscience↗

bioRxiv2020

Bayesian Inference with Incomplete Knowledge Explains Perceptual Confidence and its Deviations from Accuracy

In perceptual decisions, subjects infer hidden states of the environment based on noisy sensory information. Here we show that both choice and its associated confidence are explained by a Bayesian framework based on partially observable Markov decision processes (POMDPs). We test our model on monkeys performing a direction-discrimination task with post-decision wagering, demonstrating that the model explains objective accuracy and predicts subjective confidence. Further, we show that the model replicates well-known discrepancies of confidence and accuracy, including the hard-easy effect, opposing effects of stimulus volatility on confidence and accuracy, dependence of confidence ratings on simultaneous or sequential reports of choice and confidence, apparent difference between choice and confidence sensitivity, and seemingly disproportionate influence of choice-congruent evidence on confidence. These effects may not be signatures of sub-optimal inference or discrepant computational processes for choice and confidence. Rather, they arise in Bayesian inference with incomplete knowledge of the environment.

neuroscience↗

bioRxiv2020

Morpho-physiological and gene expression responses of wheat by Aegilops cylindrica amphidiploids to salt stress

Aegilops cylindrica Host is one of the most salt-tolerant species in the Triticeae tribe. Amphidiploid plants derived from hybridization of Roshan x Aegilops cylindrica and Chinese Spring x Ae. cylindrica genotypes contrasting in salt tolerance were assessed for their morpho-physiological responses and the expression patterns of three genes related to ion homeostasis under 250 mM NaCl. Results showed that salt stress caused significant declines in both their morphological and phenological traits. Moreover, salt stress reduced not only their chlorophyll content but also their root and shoot K contents and K/Na ratios, while it led to significant enhancements in the remaining traits. Similar to Ae. cylindrica, the amphidiploids subjected to salt stress exhibited significantly higher H2O2 levels, root and shoot K contents, and root and shoot K/Na ratios accompanied by lower root and shoot Na contents and MDA concentrations when compared with the same traits in the wheat parents. Quantitative Real-Time PCR showed significant differential expression patterns of the HKT1;5, NHX1, and SOS1 genes between the amphidiploids and their parents. The transcript level of HKT1;5 was found to be higher in the roots than in the shoots of both the amphidiploids and Ae. cylindrica while NHX1 exhibited a higher expression in the shoot tissues. The consistency of these data provides compelling support for the hypothesis that active exclusion of Na from the roots and elevated vacuolar sequestration of Na in the leaves might explain the declining Na levels in the shoots and roots of both the amphidiploids and Ae. cylindrica relative to those measured in wheat parents. It is concluded that the hybridized amphiploids are potentially valuable resources for salt improvement in bread wheat through the backcrossing approach.

plant biology↗

bioRxiv2020

Prefrontal cortex represents heuristics that shape choice bias and its integration into future behavior.

Goal-directed behavior requires integrating sensory information with prior knowledge about the environment. Behavioral biases that arise from these priors could increase positive outcomes when the priors match the true structure of the environment, but mismatches also happen frequently and could cause unfavorable outcomes. Biases that reduce gains and fail to vanish with training indicate fundamental suboptimalities arising from ingrained heuristics of the brain. Here, we report systematic, gain-reducing choice biases in highly-trained monkeys performing a motion direction discrimination task where only the current stimulus is behaviorally relevant. The monkeys bias fluctuated at two distinct time scales: slow, spanning tens to hundreds of trials, and fast, arising from choices and outcomes of the most recent trials. Our finding enabled single trial prediction of biases, which influenced the choice especially on trials with weak stimuli. The pre-stimulus activity of neuronal ensembles in the monkey prearcuate gyrus represented these biases as an offset along the decision axis in the state space. This offset persisted throughout the stimulus viewing period, when sensory information was integrated, leading to a biased choice. The pre-stimulus representation of history-dependent bias was functionally indistinguishable from the neural representation of upcoming choice before stimulus onset, validating our model of single-trial biases and suggesting that pre-stimulus representation of choice could be fully defined by biases inferred from behavioral history. Our results indicate that the prearcuate gyrus reflects intrinsic heuristics that compute bias signals, as well as the mechanisms that integrate them into the oculomotor decision-making process.

animal behavior and cognition↗

bioRxiv2020

Differences in memory for what, where, and when components of recently-formed episodes

An integral feature of human memory is the ability to recall past events. What distinguishes such episodic memory from associative and semantic memories is the joint encoding and retrieval of "what," "where," and "when" (WWW) of events. Here, we investigated whether the WWW components of episodes are retrieved with equal fidelity. Using a novel task where human participants were probed on the WWW components of a recently-viewed synthetic movie, we found fundamental differences in mnemonic accuracy between these components. The memory of "when" had the lowest accuracy and was most severely influenced by primacy and recency. Further, the memory of "when" and "where" were most susceptible to interference due to changes in memory load. These findings suggest that episodes are not stored and retrieved as a coherent whole. Rather, memory components preserve a degree of independence, suggesting that remembering coherent episodes is an active reconstruction process.

neuroscience↗

bioRxiv2019

Strongly correlated spatiotemporal encoding and simple decoding in the prefrontal cortex

We studied the fine temporal structure of spiking patterns of groups of up to 100 simultaneously recorded units in the prefrontal cortex of monkeys performing a visual discrimination task. We characterized the vocabulary of population activity patterns using 10 ms time bins and found that different sets of population activity patterns (codebooks) are used in different task epochs and that spiking correlations between units play a large role in defining those codebooks. Models that ignore those correlations fail to capture the population codebooks in all task epochs. Further, we show that temporal sequences of population activity patterns have strong history-dependence and are governed by different transition probabilities between patterns and different correlation time scales, in the different task epochs, suggesting different computational dynamics governing each epoch. Together, the large impact of spatial and temporal correlations on the dynamics of the population code makes the observed sequences of activity patterns many orders of magnitude more likely to appear than predicted by models that ignore these correlations and rely only on the population rates. Surprisingly, however, models that ignore these correlations perform quite well for decoding behavior from population responses. The difference of encoding and decoding complexity of the neural codebook suggests that one of the goals of the complex encoding scheme in the prefrontal cortex is to accommodate simple decoders that do not have to learn correlations.

neuroscience↗

bioRxiv2019

Decoding and perturbing decision states in real time

In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment. The process of deliberation on the evidence can be described by a time-varying decision variable (DV), decoded from neural activity, that predicts a subjects decision at the end of a trial. However, within trials, large moment-to-moment fluctuations of the DV are observed. The behavioral significance of these fluctuations and their role in the decision process remain unclear. Here we show that within-trial DV fluctuations decoded in real time from motor cortex are tightly linked to choice behavior, and that robust changes in DV sign have the statistical regularities expected from behavioral studies of changes-of-mind. Furthermore, we find single-trial evidence for absorbing decision bounds. As the DV builds up, heavily favoring one or the other choice, moment-to-moment variability in the DV is reduced, and both neural DV and behavioral decisions become resistant to additional pulses of sensory evidence as predicted by diffusion-to-bound and attractor models of the decision process.

neuroscience↗