Biology subjects

Fairhall, A. L.

Publications and source records attributed to Fairhall, A. L..

10 recordsLinked to original sources

bioRxiv2026

Neural representations of beliefs in a multi-dimensional inference task

Adaptive behavior requires maintaining and updating probabilistic beliefs about the world, yet how distributed brain circuits implement such computations remains unknown. We recorded from over 1,400 neurons across six brain regions in monkeys performing a multi-dimensional inference task requiring them to infer hidden rules through trial-and-error learning. Behavior was well-described by models based on Bayesian updating of beliefs over rule features. Neural representations of both observable variables (stimuli, rewards) and latent beliefs (rule preferences, confidence) were broadly distributed across hippocampus, amygdala, prefrontal cortex, anterior cingulate, striatum, and inferior temporal cortex. Belief representations were present throughout all task periods but exhibited region- and epoch-specific dynamics. Critically, trial-to-trial changes in population activity reflected Bayesian belief updating: neural responses evolved according to the integration of prior beliefs with new evidence. Additionally, we identified confidence representations that were independent of specific beliefs and showed distinct temporal profiles. These results demonstrate that probabilistic inference emerges from coordinated dynamics across distributed brain systems, with different regions contributing flexibly according to computational demands at different states of learning and decision-making.

neuroscience↗

bioRxiv2025

The Primate Hippocampus Constructs a Temporal Scaffold Anchored to Behavioral Events

The hippocampus has been attributed a range of divergent functions, including roles in memory1,2 and navigation3-5, but how its moment-to-moment neuronal activity supports cognition remains poorly understood. The activity of individual hippocampal neurons is correlated with numerous perceptual features and task variables6-14, raising the question of whether these response properties reflect distinct mechanisms or support a single generalized computation. Here, we show that these diverse response properties reflect a unified organizing principle in which the hippocampus segments experience into discrete events, with population activity transitioning between discrete neural states at behaviorally salient moments. Recording from monkeys performing a virtual spatial alternation task, we found that population activity did not evolve smoothly over time but instead shifted abruptly at each relevant event. These discontinuities segmented activity into distinct ensembles, effectively chunking separate task epochs. Notably, many neuronal responses persisted across visually distinct environments, demonstrating that these dynamics reflect abstract task structure rather than specific sensory features. These results reveal that the hippocampus constructs a temporal scaffold anchored to relevant behavioral events, with each neural state tracking a distinct task phase. This organizational principle may explain the diverse neural correlates observed across studies: rather than individually encoding perceptual or behavioral variables, hippocampal neurons collectively signal the current phase of a behavioral sequence. Our findings suggest that the hippocampus parses experience into meaningful elements and tracks "position" within a learned behavioral structure.

neuroscience↗

bioRxiv2024

Discovering plasticity rules that organize and maintain neural circuits

Intrinsic dynamics within the brain can accelerate learning by providing a prior scaffolding for dynamics aligned with task objectives. Such intrinsic dynamics should self-organize and self-sustain in the face of fluctuating inputs and biological noise, including synaptic turnover and cell death. An example of such dynamics is the formation of sequences, a ubiquitous motif in neural activity. The sequence-generating circuit in zebra finch HVC provides a reliable timing scaffold for motor output in song and demonstrates a remarkable capacity for unsupervised recovery following perturbation. Inspired by HVC, we seek a local plasticity rule capable of organizing and maintaining sequence-generating dynamics despite continual network perturbations. We adopt a meta-learning approach introduced by Confavreux et al, which parameterizes a learning rule using basis functions constructed from pre- and postsynaptic activity and synapse size, with tunable time constants. Candidate rules are simulated within initially random networks, and their fitness is evaluated according to a loss function that measures the fidelity with which the resulting dynamics encode time. We use this approach to introduce biological noise, forcing meta-learning to find robust solutions. We first show that, in the absence of perturbation, meta-learning identifies a temporally asymmetric generalization of Ojas rule that reliably organizes sparse sequential activity. When synaptic turnover is introduced, the learned rule incorporates an additional form of homeostasis, better maintaining sequential dynamics relative to other previously proposed rules. Additionally, inspired by recent findings demonstrating plasticity in synapses from inhibitory interneurons in HVC, we explore the role of inhibitory plasticity in sequence-generating circuits. We find that learned plasticity adjusts both excitation and inhibition in response to manipulations, outperforming rules applied only to excitatory connections. We demonstrate how plasticity acting on both excitatory and inhibitory synapses can better shape excitatory cell dynamics to scaffold timing representations.

neuroscience↗

bioRxiv2024

Olfactory bulb tracks breathing rhythms and place in freely behaving mice

Vertebrates sniff to control the odor samples that enter their nose. These samples can not only help identify odorous objects, but also locations and events. However, there is no receptor for place or time. Therefore, to take full advantage of olfactory information, an animals brain must contextualize odor-driven activity with information about when, where, and how they sniffed. To better understand contextual information in the olfactory system, we captured the breathing and movements of mice while recording from their olfactory bulb. In stimulus- and task-free experiments, mice structure their breathing into persistent rhythmic states which are synchronous with statelike structure in ongoing neuronal population activity. These population states reflect a strong dependence of individual neuron activity on variation in sniff frequency, which we display using "sniff fields" and quantify using generalized linear models. In addition, many olfactory bulb neurons have "place fields" that display significant dependence of firing on allocentric location, which were comparable with hippocampal neurons recorded under the same conditions. At the population level, a mouses location can be decoded from olfactory bulb with similar accuracy to hippocampus. Olfactory bulb place sensitivity cannot be explained by breathing rhythms or scent marks. Taken together, we show that the mouse olfactory bulb tracks breathing rhythms and self-location, which may help unite internal models of self and environment with olfactory information as soon as that information enters the brain.

neuroscience↗

bioRxiv2023

Comparing rapid rule-learning strategies in humans and monkeys

Inter-species comparisons are key to deriving an understanding of the behavioral and neural correlates of human cognition from animal models. We perform a detailed comparison of macaque monkey and human strategies on an analogue of the Wisconsin Card Sort Test, a widely studied and applied multi-attribute measure of cognitive function, wherein performance requires the inference of a changing rule given ambiguous feedback. We found that well-trained monkeys rapidly infer rules but are three times slower than humans. Model fits to their choices revealed hidden states akin to feature-based attention in both species, and decision processes that resembled a Win-stay lose-shift strategy with key differences. Monkeys and humans test multiple rule hypotheses over a series of rule-search trials and perform inference-like computations to exclude candidates. An attention-set based learning stage categorization revealed that perseveration, random exploration and poor sensitivity to negative feedback explain the under-performance in monkeys.

animal behavior and cognition↗

bioRxiv2022

Precision motor timing via scalar input fluctuations

Complex motor skills like playing piano require precise timing over long periods, without errors accumulating between subprocesses like the left and right hand movements. While biological models can produce motor-like sequences, how the brain quenches timing errors is not well understood. Motivated by songbirds, where the left and right brain nuclei governing song sequences do not connect but may receive low-dimensional thalamic input, we present a model where timing errors in an autonomous sequence generator are continually corrected by one-dimensional input fluctuations. We show in a spiking neural network model how such input can rapidly correct temporal offsets in a propagating spike pulse, recapitulating the precise timing seen in songbird brains. In a reduced, more general model, we show that such timing correction emerges when the spatial profile of the input over the sequence sufficiently reflects its temporal fluctuations, yielding time-locking attractors that slow advanced sequences and hasten lagging ones, up to the input timescale. Unlike models without fluctuating input, our model predicts anti-correlated durations of adjacent segments of the output sequence, which we verify in recorded zebra finch songs. This work provides a bioplausible picture of how temporal precision could arise in extended motor sequences and generally how low-dimensional input could continuously coordinate time-varying output signals. SignificanceComplex motor skills like playing piano require precision timing over long periods, often among multiple components like left and right muscle groups. Although brain-like network models can produce motor-like outputs, timing regulation is not well understood. We introduce a model, inspired by songbird brains, where imprecise timing in a cortical-like system is corrected by a single thalamic input regulating the sequential propagation, or tempo, of cortical activity. This model illuminates a relation between the inputs spatial structure and temporal variation that lets lagging activity hasten and advanced activity slow, which makes a prediction about output timing that we verify in real birdsong. This work reveals a simple, neuroplausible mechanism that may play a role in precision cortical or motor timing.

neuroscience↗

bioRxiv2021

Dopamine Neurons Evaluate Natural Fluctuations in Performance Quality

Many motor skills are learned by comparing ongoing behavior to internal performance benchmarks. Dopamine neurons encode performance error in behavioral paradigms where error is externally induced, but it remains unknown if dopamine also signals the quality of natural performance fluctuations. Here we recorded dopamine neurons in singing birds and examined how spontaneous dopamine spiking activity correlated with natural fluctuations in ongoing song. Antidromically identified basal ganglia-projecting dopamine neurons correlated with recent, and not future, song variations, consistent with a role in evaluation, not production. Furthermore, dopamine spiking was suppressed following the production of outlying vocal variations, consistent with a role for active song maintenance. These data show for the first time that spontaneous dopamine spiking can evaluate natural behavioral fluctuations unperturbed by experimental events such as cues or rewards.

neuroscience↗

bioRxiv2020

From neuron to muscle to movement: a complete biomechanical model of Hydra contractile behaviors

How does neural activity drive muscles to produce behavior? The recent development of genetic lines in Hydra that allow complete calcium imaging of both neuronal and muscle activity, as well as systematic machine learning quantification of behaviors, makes this small Cnidarian an ideal model system to understand and model the complete transformation from neural firing to body movements. As a first step to achieve this, we have built a biomechanical model of Hydra, incorporating its neuronal activity, muscle activity and body column biomechanics, incorporating its fluid-filled hydrostatic skeleton. Our model is based on experimental measurements of neuronal and muscle activity, and assumes gap junctional coupling among muscle cells and calcium-dependent force generation y muscles. With these assumptions, we can robustly reproduce a basic set of Hydras behaviors. We can further explain puzzling experimental observations, including the dual kinetics observed in muscle activation and the different engagement of ecto- and endodermal muscle in different behaviors. This work delineates the spatiotemporal control space of Hydra movement and can serve as a template for future efforts to systematically decipher the transformations in the neural basis of behavior.

biophysics↗

bioRxiv2019

Capturing multiple timescales of adaptation to second-order statistics with generalized linear models: gain scaling and fractional differentiation

Single neurons can dynamically change the gain of their spiking responses to account for shifts in stimulus variance. Moreover, gain adaptation can occur across multiple timescales. Here, we examine the ability of a simple statistical model of spike trains, the generalized linear model (GLM), to account for these adaptive effects. The GLM describes spiking as a Poisson process whose rate depends on a linear combination of the stimulus and recent spike history. The GLM successfully replicates gain scaling observed in Hodgkin-Huxley simulations of cortical neurons that occurs when the ratio of spike-generating potassium and sodium conductances approaches one. Gain scaling in the GLM depends on the length and shape of the spike history filter. Additionally, the GLM captures adaptation that occurs over multiple timescales as a fractional derivative of the stimulus variance, which has been observed in neurons that include long timescale after hyperpolarization conductances. Fractional differentiation in GLMs requires long spike history that span several seconds. Together, these results demonstrate that the GLM provides a tractable statistical approach for examining single-neuron adaptive computations in response to changes in stimulus variance.

neuroscience↗

bioRxiv2018

Fast and Flexible Sequence Induction In Spiking Neural Networks Via Rapid Excitability Changes

Cognitive flexibility, the adaptation of mental processing to changes in task demands, is thought to depend on biological neural networks ability to rapidly modulate the dynamics governing how they process information. While extensive work has elucidated how network dynamics can be reshaped by slowly occurring structural changes, e.g. the gradual modification of recurrent synaptic patterns, much less is known about how dynamics might be reconfigured over faster timescales of seconds. One compelling example of rapid and selective modulation of network dynamics potentially involved in cognitive flexibility is observed in rodent hippocampus, where short bouts of exploratory behavior cause new activity sequences to preferentially \"replay\" during subsequent awake rest periods without continued sensory input. Fast mechanisms for selectively biasing sequential activity through networks, however, remain unknown. Using a spiking neural network model, we asked whether a simplified version of sequence replay could arise from three biophysically plausible components: recurrent, spatially organized connectivity; homogeneous, stochastic \"gating\" inputs; and rapid, activity-dependent scaling of gating input strengths, based on a phenomenon known as long-term potentiation of intrinsic excitability (LTP-IE). Indeed, these enabled both forward and reverse replay of flexible sequences reflecting recent behavior, despite unchanged recurrent weights. Specifically, activation-triggered LTP-IE \"tags\" neurons in the recurrent network by increasing their spiking probability when gating input is applied, and the sequential ordering of spikes is reconstructed by the existing recurrent connectivity. In a proof-of-concept demonstration, we also show how LTP-IE-based sequences can implement temporary stimulus-response mappings in a straightforward manner. These results elucidate a simple yet previously unexplored combination of biological mechanisms that converge in hippocampus and suffice for fast and flexible reconfiguration of sequential network dynamics, suggesting their potential role in cognitive flexibility over rapid timescales.

neuroscience↗