Biology subjects

Ferrera, V. P.

Publications and source records attributed to Ferrera, V. P..

12 recordsLinked to original sources

bioRxiv2026

Transitive reasoning as linear classification

Transitive inference (TI) is the ability to reason about transitive relationships in an ordered set of items (e.g., if A>B and B>C, then A>C). TI is widely held to depend on a linear representation of the serial (rank) order of those items. By what computational mechanism is such an ordering constructed during learning, and how is it used to make choices that obey transitivity? Here we take a minimalist approach, applying least-squares estimation (LSE) to a serial learning task commonly used to test TI in humans and animals. In this formulation, LSE computes a linear classifier that maps task conditions onto behavioral outcomes. This algorithm makes no explicit assumptions about transitivity or serial order, yet it reproduces key empirical features of TI; namely, the ability to generalize beyond the training set, and a symbolic distance effect (SDE) in performance accuracy. Applying the classifier to individual items produces an internally ordered representation of rank from which both generalization and the SDE naturally emerge. The approach also yields a decision mechanism, in the form of a differencing operation, for selecting the correct item from any pair. These findings reframe TI as a linear classification problem, challenging conventional assumptions about the cognitive mechanisms required for transitive reasoning.

neuroscience↗

bioRxiv2025

Alteration of Water Exchange Rates Following Focused Ultrasound-Mediated BBB Opening in the Dorsal Striatum of Non-Human Primates: A Diffusion-Prepared pCASL Study

This study applied diffusion-prepared pseudo-continuous arterial spin labeling (DP-pCASL) to quantify cerebral blood flow (CBF), arterial transit time (ATT), and blood-brain barrier (BBB) water exchange rate (Kw) before and after focused ultrasound (FUS)-mediated blood-brain barrier opening (BBBO) in the dorsal striatum of four non-human primates. Six baseline and seven BBBO sessions were performed. DP-pCASL was acquired approximately 45 minutes after FUS sonication combined with intravenous microbubbles, and contrast-enhanced T1-weighted imaging was subsequently used to confirm the BBBO region. Whole-brain analyses revealed no significant changes in CBF or ATT following BBBO (permutation p > 0.05). Region-of-interest analysis within the sonicated caudate demonstrated a significant localized decrease in Kw, with median (IQR) values of 45.0 (40.6 - 55.6) min-1 at the BBBO site versus 61.6 (58.3 - 70.4) min-1 in the contralateral control region (p < 0.05), confirming spatially specific suppression of transendothelial water flux. In contrast, whole-brain Kw increased significantly following BBBO, with median (IQR) values of 49.8 (46.3 - 55.9) min-1 in non-BBBO sessions versus 59.4 (56.6 - 66.3) min-1 in BBBO sessions (p < 0.01), indicating a diffuse enhancement of water exchange across the brain. These findings establish DP-pCASL-derived Kw as a sensitive, non-contrast biomarker for both local and global BBB permeability changes induced by focused ultrasound, supporting its potential for longitudinal monitoring in preclinical and clinical neurotherapeutic applications.

neuroscience↗

bioRxiv2025

Portable transcranial therapeutic ultrasound enhances targeted gene delivery for Parkinson's disease: from rodent models to non-human primates

Gene therapy for neurodegenerative diseases faces significant challenges due to the blood-brain barrier (BBB), which limits drug delivery to the central nervous system (CNS). While clinical trials for Parkinsons disease (PD) have progressed, administration of vectors expressing enzymatic or neurotrophic factor transgenes have required extensive optimization of the delivery method to achieve potentially therapeutic levels of transgene expression. Focused ultrasound (FUS) combined with microbubbles has emerged as a promising non-invasive strategy to transiently open the BBB for targeted gene delivery via viral nanocarriers including recombinant adeno-associated viruses (AAVs). However, key factors influencing FUS-mediated AAV delivery, including dose distribution and therapeutic efficacy, remain underexplored in non-human primates (NHPs). Here, we evaluated the feasibility of AAV9-CAG-GFP delivery using two portable therapeutic ultrasound modalities: ultrasound-guided, spherically-focused FUS (USgFUS) and a novel low-frequency linear array configuration for imaging and therapy called theranostic ultrasound (ThUS). In mice, FUS-sonicated regions exhibited a 25-fold increase in AAV9 biodistribution compared to systemic injection alone. Extending this approach to NHPs, we observed up to a 200-fold increase in AAV9 DNA in treated brain regions, including PD-relevant structures. In assessing the translational therapeutic potential of this technique, ThUS-mediated AAV9-hSyn-hNTRN (human neurturin) delivery in a toxin mouse model of PD facilitated the rescue of up to 80% and 75% of degenerated dopaminergic neurons in the substantia nigra and striatum, respectively. These findings demonstrate that portable ultrasound technologies can non-invasively enhance AAV9 delivery to targeted brain regions in both mice and NHPs relative to what can be achieved with intravenous (IV) delivery of the same capsid alone. With further development, these approaches may offer a clinically viable, non-invasive alternative for gene therapy in neurodegenerative diseases. One sentence summaryBBB opening with portable therapeutic ultrasound non-invasively increased viral gene delivery to the brain after systemic AAV vector administration in mice and rhesus macaques.

bioengineering↗

bioRxiv2025

Learning decouples accuracy and reaction time for rapid decisions in a transitive inference task

Transitive inference (TI) is a cognitive process in which decisions are guided by internal representations of abstract relationships. While the mechanisms underlying transitive learning have been well studied, the dynamics of the decision-making process during learning and inference remain less clearly understood. In this study, we investigated whether a modeling framework traditionally applied to perceptual decision-making--the drift diffusion model (DDM)--can account for performance in a TI transfer task involving rapid decisions that deviate from standard accuracy and response time (RT) patterns. We trained six macaque monkeys on a TI transfer task, in which they learned the implied order of a novel list of seven images in each behavioral session, indicating their decisions with saccadic eye movements or reaching movements. Consistent learning of the list structure was achieved within 200-300 trials per session. Behavioral performance exhibited a symbolic distance effect, with accuracy increasing as the ordinal distance between items grew. Notably, RTs remained relatively stable across learning, despite improvements in accuracy. We applied a generalized DDM implementation (PyDDM; Shinn et al., 2020) to jointly fit accuracy and RT data. Model fits were achieved by incorporating both an increasing evidence accumulation rate and a collapsing decision bound, successfully capturing the RT distribution shapes observed during learning. Learning and transfer were fit by varying drift rate with little change in other parameters. Eye and reaching movements showed similar dynamics, with the difference in RT accounted for mainly by non-decision time. Our results highlight a distinct dynamical regime of the DDM framework, extending its applicability to cognitive domains involving symbolic reasoning and serial relational learning.

animal behavior and cognition↗

bioRxiv2023

A mathematical theory of relational generalization in transitive inference

Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. Here we investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation (A > B and B > C) and generalize it to new combinations of items (A > C). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar "conjunctivity factor" determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the "rich regime," which enables representation learning and has been found to improve generalization, unexpectedly show poor generalization and anomalous behavior. We find that such networks implement a form of norm minimization (over hidden weights) that yields a local encoding mechanism lacking transitivity. Our findings show how minimal statistical learning principles give rise to a classical relational inductive bias (transitivity), explain empirically observed behaviors, and establish a formal approach to understanding the neural basis of relational abstraction.

neuroscience↗

bioRxiv2023

Alteration of functional connectivity in the cortex and major brain networks of non-human primates following focused ultrasound exposure

Focused ultrasound (FUS) is a non-invasive neuromodulation technology that is being investigated for potential treatment of neurological and psychiatric disorders. Focused ultrasound combined with microbubbles can temporarily open the intact blood-brain barrier (BBB) of animals and humans, and facilitate drug delivery. FUS exposure, either with or without microbubbles, has been demonstrated to alter the behavior of non-human primates, and previous work has demonstrated transient and long-term effects of FUS neuromodulation on functional connectivity using resting state functional MRI. However, it is unknown whether opening the BBB affects functional connectivity differently than FUS alone. Thus we applied FUS alone (neuromodulation) and FUS with microbubbles (BBB opening) in the dorsal striatum of lightly anesthetized non-human primates, and compared changes in functional connectivity in major brain networks. We found different alteration patterns between FUS neuromodulation and FUS-mediated BBB opening in several cortical areas, and we also found that applying FUS to a deep brain structure can alter functional connectivity in the default mode network and frontotemporal network.

neuroscience↗

bioRxiv2022

Neural dynamics and geometry for transitive inference

Relational cognition -- the ability to infer relationships that generalize to novel combinations of objects -- is fundamental to human and animal intelligence. Despite this importance, it remains unclear how relational cognition is implemented in the brain due in part to a lack of hypotheses and predictions at the levels of collective neural activity and behavior. Here we discovered, analyzed, and experimentally tested neural networks (NNs) that perform transitive inference (TI), a classic relational task (if A > B and B > C, then A > C). We found NNs that (i) generalized perfectly, despite lacking overt transitive structure prior to training, (ii) generalized when the task required working memory (WM), a capacity thought essential to inference in the brain, (iii) emergently expressed behaviors long observed in living subjects, in addition to a novel order-dependent behavior, and (iv) adopted different task solutions yielding alternative behavioral and neural predictions. Further, in a large-scale experiment, we found that human subjects performing WM-based TI showed behavior inconsistent with a class of NNs expressing an intuitive task solution. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition.

neuroscience↗

bioRxiv2022

Superstitious learning of abstract order from random reinforcement

Survival depends on identifying learnable features of the environment that predict reward, and avoiding others that are random and unlearnable. However, humans and other animals often infer spurious associations among unrelated events, raising the question of how well they can distinguish learnable patterns from unlearnable events. Here, we tasked monkeys with discovering the serial order of two pictorial sets: a "learnable" set in which the stimuli were implicitly ordered and monkeys were rewarded for choosing the higher-rank stimulus and an "unlearnable" set in which stimuli were unordered and feedback was random regardless of the choice. We replicated prior results that monkeys reliably learned the implicit order of the learnable set. Surprisingly, the monkeys behaved as though some ordering also existed in the unlearnable set, showing consistent choice preference that transferred to novel untrained pairs in this set, even under a preference-discouraging reward schedule that gave rewards more frequently to the stimulus that was selected less often. In simulations, a model-free RL algorithm (Q-learning) displayed a degree of consistent ordering among the unlearnable set but, unlike the monkeys, failed to do so under the preference, discouraging reward schedule. Our results suggest that monkeys infer abstract structures from objectively random events using heuristics that extend beyond stimulus-outcome conditional learning to more cognitive model-based learning mechanisms.

animal behavior and cognition↗

bioRxiv2021

Influence of Rule and Reward-based Strategies on Inferences of Serial Order by Monkeys

Knowledge of transitive relationships between items can contribute to learning the order of a set of stimuli from pairwise comparisons. However, cognitive mechanisms of transitive inferences based on rank order remain unclear, as are contributions of reward magnitude and rule-based inference. To explore these issues, we created a conflict between rule- and reward-based learning during a serial ordering task. Rhesus macaques learned two lists, each containing five stimuli, that were trained exclusively with adjacent pairs. Selection of the higher-ranked item resulted in rewards. "Small reward" lists yielded 2 drops of fluid reward, while "large reward" lists yielded 5 drops. Following training of adjacent pairs, monkeys were tested on novels pairs. One item was selected from each list, such that a ranking rule could conflict with preferences for large rewards. Differences in associated reward magnitude had a strong influence on accuracy, but we also observed a symbolic distance effect. That provided evidence of a rule-based influence on decisions. Reaction time comparisons suggested a conflict between rule and reward-based processes. We conclude that performance reflects the contributions of two strategies, and that a model-based strategy is employed in the face of a strong countervailing reward incentive.

animal behavior and cognition↗

bioRxiv2021

Long Term Study of Motivational and Cognitive Effects of Low-intensity Focused Ultrasound Neuromodulation in the Dorsal Striatum of Nonhuman Primates

Noninvasive brain stimulation using focused ultrasound (FUS) has many potential applications as a research and clinical tool, including incorporation into neural prosthetics for cognitive rehabilitation. To develop this technology, it is necessary to evaluate the safety and efficacy of FUS neuromodulation for specific brain targets and cognitive functions. It is also important to test whether repeated long-term application of FUS to deep brain targets improves or degrades behavioral and cognitive function. To this end, we investigated the effects of FUS in the dorsal striatum of nonhuman primates (NHP) performing a visual-motor decision-making task for small or large rewards. Over the course of 2 years, we performed 129 and 147 FUS applications, respectively, in two NHP. FUS (0.5 MHz @ 0.2 - 0.8 MPa) was applied to the putamen and caudate in both hemispheres to evaluate the effects on movement accuracy, motivation, decision accuracy, and response time. Sonicating the caudate or the putamen unilaterally resulted in modest but statistically significant improvements in motivation and decision accuracy, but at the cost of slower reaction times. The effects were dose (i.e., FUS pressure) and reward dependent. There was no effect on reaching accuracy, nor was there long-term behavioral impairment or neurological trauma evident on T1-weighted, T2-weighted, or susceptibility-weighted MRI scans. Sonication also resulted in significant changes in resting state functional connectivity between the caudate and multiple cortical regions. The results indicate that applying FUS to the dorsal striatum can positively impact the motivational and cognitive aspects of decision making. The capability of FUS to improve motivation and cognition in NHPs points to its therapeutic potential in treating a wide variety of human neural diseases, and warrants further development as a novel technique for non-invasive deep brain stimulation.

neuroscience↗

bioRxiv2019

Neuronal responses in posterior parietal cortex during learning of implied serial order

Monkeys are able to learn the implied ordering of pairs of images drawn from an ordered set, without ever seeing all of the images simultaneously and without explicit spatial or temporal cues. The learning of implied order differs from learning of explicit visual or motor sequences. We recorded the activity of parietal neurons in rhesus macaques while they learned 7-item TI lists when only 2 items were presented on each trial. Behavior and ensemble neuronal activity were significantly influenced by the ordinal relationship of the stimulus pairs, specifically symbolic distance (the difference in rank) and joint ranks (the sum of the ranks). Symbolic distance strongly predicted decision accuracy, and learning was consistently faster as symbolic distance increased. An effect of joint rank on performance was also found nested within the symbolic distance effect. Across the population of neurons, there was significant modulation of firing correlated with the relative ranks of the two stimuli presented on each trial. Neurons exhibited selectivity for stimulus rank during learning, but not before or after. The observed behavior during learning is best explained by a virtual workspace model, not by associative or reward mechanisms. The neural data support a role for posterior parietal cortex in representing several variables that contribute to serial learning, particularly information about the ordinal ranks of the stimuli presented during a given trial. Thus, parietal cortex appears to belong to a neural substrate for learning and representing abstract relationships in a virtual workspace.

neuroscience↗

bioRxiv2017

Inferential Learning of Serial Order of Perceptual Categories by Rhesus Monkeys (Macaca mulatta)

Category learning in animals is typically trained explicitly, in most instances by varying the exemplars of a single category in a matching-to-sample task. Here, we show that rhesus macaques can learn categories by a transitive inference paradigm in which novel exemplars of five categories were presented throughout each training session. Instead of requiring decisions about a constant set of repetitively presented stimuli, we studied the macaques ability to determine the relative order of multiple exemplars of particular stimuli that were rarely repeated. Ordinal decisions generalized both to novel stimuli and, as a consequence, to novel pairings. Thus, we showed that rhesus monkeys could learn to categorize on the basis of implied ordinal position, and that they could then make inferences about category order. Our results challenge the plausibility of association models of category learning and broaden the scope of the transitive inference paradigm.\n\nSignificance StatementThe cognitive abilities of non-human animals are of enduring interest to scientists and the general public because they blur the dividing line between human and non-human intelligence. Categorization and sequence learning are highly abstract cognitive abilities each in their own right. This study is the first to provide evidence that visual categories can be ordered serially by macaque monkeys using a behavioral paradigm that provides no explicit feedback about category or serial order. These results strongly challenge accounts of learning based on stimulus-outcome associations.

animal behavior and cognition↗