Biology subjects

Lindsay, G. W.

Publications and source records attributed to Lindsay, G. W..

8 recordsLinked to original sources

bioRxiv2026

Modulation of feature attention by reward prediction error explains value learning behavior

Adaptive behavior requires learning the value of environmental features while selectively attending to those most likely to yield reward. Reward prediction errors (RPEs) drive value learning and learned values guide attention, yet the computational function linking RPEs to attentional modulation remains unspecified. Here, we developed a reinforcement learning model with a perceptual front-end to investigate how value and RPE signals modulate attentional gain during learning. We compared five candidate RPE-attention transfer functions, each combined with either single- or multi-focus attention, against behavioral data from two adult male rhesus macaques performing a color-value learning task with shifting reward contingencies. Monkeys exhibited rapid initial learning followed by sub-optimal asymptotic accuracy. Overall, single-focus architectures consistently outperformed multi-focus counterparts on matching monkey errors, indicating that macaques collapse the value distribution into a winner-take-all attentional focus. Furthermore, the "Switch" model, in which attention targets the highest-valued feature but transiently inverts following negative RPEs, produced the fastest exploration dynamics following target switches and, together with the Absolute Value model, yielded decision confidence trajectories that positively correlated with empirical reaction times. In support of this, single-neuron correlation analyses revealed that 27 - 42% of neurons in prefrontal cortex, frontal eye fields, and lateral intraparietal area encoded previous-trial RPE at the time of next trial onset. In total, we conclude that capacity constrained attention that inverts its focus after negative RPE best explains value learning dynamics. These results provide a normative account for why biological learners sacrifice asymptotic precision for rapid adaptation in volatile environments. Significance StatementLearning which features of the environment predict reward requires both reinforcement learning and selective attention, yet how these processes interact algorithmically remains unknown. We developed a computational model that specifies how reward prediction errors dynamically adjust the gain of feature-based attention during value learning. Testing competing hypotheses against macaque behavioral data, we show that a "Switch" mechanism, in which negative prediction errors transiently invert attentional focus away from the highest-valued feature, best captures primate learning dynamics. This architecture reveals a principled trade-off: the brain sacrifices asymptotic accuracy for rapid detection of environmental change, using error-triggered attentional inversion as a directed exploration strategy. These findings bridge reinforcement learning theory and attention research by identifying the transfer function linking prediction errors to sensory gain modulation.

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bioRxiv2026

Simplified model of intrinsically bursting neurons

Rhythmic neural activity underlies essential biological functions such as locomotion, breathing, and feeding. Computational models are widely used to study how such rhythms emerge from interactions between neuron-level and circuit-level dynamics. Intrinsically bursting neurons are key components of many central pattern generators (CPGs), yet existing models span a tradeoff between biological realism and practical usability. Biophysical models involve many parameters that are difficult to tune, whereas abstract models often integrate poorly into neural circuit simulations. We propose a simplified model of intrinsically bursting neurons derived from a reduced non-spiking biophysical formulation. The model integrates readily into neural circuits while enabling direct and independent control of bursting characteristics, including duration, amplitude, and shape. We show that the model reproduces single-unit biophysical responses to diverse stimuli as well as circuit-level activity patterns from crustacean and mammalian CPGs. This model provides a practical tool for studying rhythm generation in neural circuits.

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bioRxiv2025

Neural Subspaces Encode Sequential Working Memory, but Neural Sequences Do Not

The neural mechanisms of multiple-item working memory are not well understood. In the current study, we address two competing hypotheses about the neural basis of sequential working memory: neural subspaces versus neural sequences. Using broadband MEG data from human participants, we applied dimensionality reduction and multivariate decoding techniques to test whether sequential items are maintained during the retention period through the reactivation of individual items in sequence (neural sequences), or by organizing them into distinct low-dimensional subspaces (neural subspaces). Our results revealed behaviorally relevant, low-dimensional neural subspaces that organized memory representations during the retention period but not during stimulus encoding, supporting the neural subspaces hypothesis. In contrast, we found no evidence of sequential neural replay during the delay period, contrary to predictions from the neural sequences hypothesis. Together, our findings suggest that sequential working memory is maintained through structured geometric organization in low-dimensional representational space, rather than through the sequential reactivation of individual items.

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bioRxiv2025

DynVision: A Toolbox for Biologically Plausible Recurrent Convolutional Networks

AO_SCPLOWBSTRACTC_SCPLOWConvolutional Neural Networks (CNNs) trained for image recognition have demonstrated remarkable conceptual similarities to the primate ventral visual pathway, but their standard feedforward architectures lack the recurrent connections that are ubiquitous in visual cortex. Such recurrence is thought to underlie spatiotemporal phenomena including adaptation, delayed normalization, and robustness to noisy input. However, incorporating functionally beneficial recurrence into CNNs that captures spatiotemporal phenomena of biological vision remains challenging. Although recent advances have incorporated neurobiological constraints, the field lacks accessible tools for systematically comparing how different architectural choices, such as recurrence type, temporal delays, and connectivity patterns, shape neural dynamics and behavior. Here, we introduce DynVision, a modular open-source toolbox for constructing and evaluating biologically plausible recurrent convolutional neural networks (RCNNs). DynVision implements numerical ODE solvers with heterogeneous delays, supports five types of lateral recurrence ranging from simple self-connections to cortically-organized local recurrence, and separates scientific modeling decisions from implementation details through a configuration-driven design. Training is computationally efficient, achieving a 52% speedup over reference implementations. We demonstrate the framework through systematic exploration of the parameter space, revealing that qualitative differences in temporal dynamics are highly sensitive to often-implicit modeling choices such as the target location of recurrent integration and the temporal window used for loss computation. Critically, we find that continuous-time recurrent dynamics can naturally give rise to cortical temporal phenomena without requiring explicit divisive normalization, while a different recurrent configuration produces noise robustness approaching human-level performance. These findings suggest functionally distinct configurations of recurrence and highlight the challenge of creating fully realistic models, thus emphasizing the need for a comprehensive and cohesive modeling framework to aid exploration. Code and documentation are available at https://github.com/Lindsay-Lab/DynVision/.

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bioRxiv2022

Bio-inspired neural networks implement different recurrent visual processing strategies than task-trained ones do

Behavioral studies suggest that recurrence in the visual system is important for processing degraded stimuli. There are two broad anatomical forms this recurrence can take, lateral or feedback, each with different assumed functions. Here we add four different kinds of recurrence--two of each anatomical form--to a feedforward convolutional neural network and find all forms capable of increasing the ability of the network to classify noisy digit images. Specifically, we take inspiration from findings in biology by adding predictive feedback and lateral surround suppression. To compare these forms of recurrence to anatomically-matched counterparts we also train feedback and lateral connections directly to classify degraded images. Counter-intuitively, we find that the anatomy of the recurrence is not related to its function: both forms of task-trained recurrence change neural activity and behavior similarly to each other and differently from their bio-inspired anatomical counterparts. By using several analysis tools frequently applied to neural data, we identified the distinct strategies used by the predictive versus task-trained networks. Specifically, predictive feedback de-noises the representation of noisy images at the first layer of the network and decreases its dimensionality, leading to an expected increase in classification performance. Surprisingly, in the task-trained networks, representations are not de-noised over time at the first layer (in fact, they become noiser and dimensionality increases) yet these dynamics do lead to de-noising at later layers. The analyses used here can be applied to real neural recordings to identify the strategies at play in the brain. Our analysis of an fMRI dataset weakly supports the predictive feedback model but points to a need for higher-resolution cross-regional data to understand recurrent visual processing..

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bioRxiv2019

A simple circuit model of visual cortex explains neural and behavioral aspects of attention

Selective visual attention modulates neural activity in the visual system in complex ways and leads to enhanced performance on difficult visual tasks. Here, we show that a simple circuit model, the stabilized supralinear network, gives a unified account of a wide variety of effects of attention on neural responses. We replicate results from studies of both feature and spatial attention, addressing findings in a variety of experimental paradigms on changes both in firing rates and in correlated neural variability. Finally, we expand this circuit model into an architecture that can perform visual tasks--a convolutional neural network--in order to show that these neural effects can enhance detection performance. This work provides the first unified mechanistic account of the effects of attention on neural and behavioral responses.

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bioRxiv2017

Understanding Biological Visual Attention Using Convolutional Neural Networks

How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visual tasks, we show that neural modulations of the kind and magnitude observed experimentally lead to performance changes of the kind and magnitude observed experimentally. We find that, at earlier layers, attention applied according to tuning does not successfully propagate through the network, and has a weaker impact on performance than attention applied according to values computed for optimally modulating higher areas. This raises the question of whether biological attention might be applied at least in part to optimize function rather than strictly according to tuning. We suggest a simple experiment to distinguish these alternatives.

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bioRxiv2017

Hebbian Learning in a Random Network Captures Selectivity Properties of Prefrontal Cortex

Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by prefrontal cortex (PFC). Neural activity in PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear mixed selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which PFC exhibits computationally relevant properties such as mixed selectivity and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data shows significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and allows the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results give intuition about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training.\n\nSignificance StatementPrefrontal cortex (PFC) is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli--and in particular, to combinations of stimuli (\"mixed selectivity\")--is a topic of interest. Despite the fact that models with random feedforward connectivity are capable of creating computationally-relevant mixed selectivity, such a model does not match the levels of mixed selectivity seen in the data analyzed in this study. Adding simple Hebbian learning to the model increases mixed selectivity to the correct level and makes the model match the data on several other relevant measures. This study thus offers predictions on how mixed selectivity and other properties evolve with training.

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