Biology subjects

Johnston, W. J.

Publications and source records attributed to Johnston, W. J..

10 recordsLinked to original sources

bioRxiv2026

High-dimensional hippocampus representation facilitates learning of low-dimensional abstract representation and supports generalization

Semantic knowledge encompasses facts, concepts, and their relationships. Recent work has proposed that some forms of semantic knowledge could be encoded in low-dimensional neural representations, such as the abstract, disentangled representational geometries observed in many cognitive brain areas. Experimental work has shown that hippocampal (HPC) lesions impair the formation of flexible and generalizable semantic knowledge. However, HPC representations are often high-dimensional and sparse. How can these representations contribute to the formation of low-dimensional abstract representation across these cognitive cortical areas? Here, we reconcile these observations using multiregional neural network models. Modeling HPC as a sparse, decorrelating autoencoder, we show that its high-dimensional representations promote the robust and efficient formation of low-dimensional abstract representations in downstream regions such as prefrontal cortex. This effect is most pronounced when the geometry of sensory inputs is misaligned with the geometry of desired outputs, requiring a more complex input-output transformation. Mathematical analysis shows that hippocampal expansion recoding suppresses misleading low-dimensional input structure and reshapes the loss landscape of downstream networks, allowing abstract semantic geometry to emerge reliably. The resulting abstract representations provide an inductive bias for sample-efficient learning of novel semantic-to-action mappings. Finally, we show that consolidating hippocampus-dependent representations into a hippocampus-independent pathway improves out-of-distribution generalization based on input structure. Together, these results suggest a circuit mechanism by which high-dimensional hippocampal representations can support, rather than oppose, the emergence of low-dimensional abstract representations.

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bioRxiv2025

Impact of Task Similarity and Training Regimes on Cognitive Transfer and Interference

Learning depends not only on the content of what we learn, but also on how we learn and on how experiences are structured over time. To investigate how task similarity and training regime interact during learning, we trained participants on spatial and conceptual learning tasks that shared either similar or distinct underlying structures, using either interleaved or blocked regimes. Interleaving the two tasks hindered performance when their structures were similar, compared to when they were different. In contrast, blocked training produced the opposite effect: it improved performance and facilitated transfer across similar tasks. This effect, however, emerged only when participants first learned the conceptual task, followed by the spatial task, suggesting an asymmetric interaction between task order and structural similarity. We also replicated our results using a neural network model, providing converging evidence for the computational principles governing the interplay between training regime and structural similarity in multi-task learning.

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bioRxiv2025

The effects of task similarity during representation learning in brains and neural networks

The complexity of our environment poses significant challenges for adaptive behavior. Recognizing shared structures across tasks can theoretically improve learning through generalization. However, how such shared representations emerge and influence performance remains poorly understood. Contrary to expectations, our findings revealed that individuals trained on tasks with similar low-dimensional structures performed worse than those trained on dissimilar tasks. Magnetoencephalography revealed correlated neural representations in the samestructure group and anticorrelated ones in the different-structure group. Crucially, practice reduced this performance gap and shifted the neural representations of the tasks in the samestructure group towards anticorrelation, like those in the different-structure group. A neural network model trained on similar tasks replicated these findings: tasks with similar structures require more iterations to orthogonalize their representations. These results highlight a complex interplay between task similarity, neural dynamics, and behavior, challenging traditional assumptions about learning and generalization.

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bioRxiv2024

Modular representations emerge in neural networks trained to perform context-dependent tasks

Experimental and theoretical work has argued both for and against the existence of specialized sub-populations of neurons (modules) within single brain regions. By studying artificial neural networks, we show that this local modularity emerges to support complex (for instance, context-dependent) behavior only when the input to the network is low-dimensional. No anatomical constraints are required. We also show when modular specialization emerges implicitly at the population level, where orthogonal subspaces are viewed as distinct modules. Modularity yields abstract representations, allows for rapid learning and generalization on novel output domains as well as related tasks. Non-modular representations facilitate the rapid learning of unrelated tasks. Our findings reconcile conflicting experimental results and make predictions for future experiments.

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bioRxiv2023

The neural basis of swap errors in working memory

When making decisions in a cluttered world, humans and other animals often have to hold multiple items in memory at once - such as the different items on a shopping list. Psychophysical experiments in humans and other animals have shown remembered stimuli can sometimes become confused, with participants reporting chimeric stimuli composed of features from different stimuli. In particular, subjects will often make "swap errors" where they misattribute a feature from one object as belonging to another object. While swap errors have been described behaviorally, their neural mechanisms are unknown. Here, we elucidate these neural mechanisms through trial-by-trial analysis of neural population recordings from posterior and frontal brain regions while monkeys perform two multi-stimulus working memory tasks. In these tasks, monkeys were cued to report the color of an item that either was previously shown at a corresponding location (requiring selection from working memory) or will be shown at the corresponding location (requiring attention to a position). Animals made swap errors in both tasks. In the neural data, we find evidence that the neural correlates of swap errors emerged when correctly remembered information is selected incorrectly from working memory. This led to a representation of the distractor color as if it were the target color, underlying the eventual swap error. We did not find consistent evidence that swap errors arose from misinterpretation of the cue or errors during encoding or storage in working memory. These results suggest an alternative to established views on the neural origins of swap errors, and highlight selection from and manipulation in working memory as crucial - yet surprisingly brittle - neural processes.

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bioRxiv2023

Primate superior colliculus is engaged in abstract higher-order cognition

Categorization is a fundamental cognitive process by which the brain assigns stimuli to behaviorally meaningful groups. Investigations of visual categorization in primates have identified a hierarchy of cortical areas that are involved in the transformation of sensory information into abstract category representations. However, categorization behaviors are ubiquitous across diverse animal species, even those without a neocortex, motivating the possibility that subcortical regions may contribute to abstract cognition in primates. One candidate structure is the superior colliculus (SC), an evolutionarily conserved midbrain region that, although traditionally thought to mediate only reflexive spatial orienting, is involved in cognitive tasks that require spatial orienting. Here, we reveal a novel role of the primate SC in abstract, higher-order visual cognition. We compared neural activity in the SC and the posterior parietal cortex (PPC), a region previously shown to causally contribute to category decisions, while monkeys performed a visual categorization task in which they report their decisions with a hand movement. The SC exhibits stronger and shorter-latency category encoding than the PPC, and inactivation of the SC markedly impairs monkeys category decisions. These results extend SCs established role in spatial orienting to abstract, non-spatial cognition.

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bioRxiv2022

Redundant representations are required to disambiguate simultaneously presented complex stimuli

An individual observing a barking dog and purring cat together in a field has distinct pairs of representations of the two animals in their visual and auditory systems. Without prior knowledge, how does the observer infer that the dog barks and the cat purrs? This integration of distributed representations is called the assignment problem, and it must be solved to integrate distinct representations across but also within sensory modalities. Here, we identify and analyze a solution to the assignment problem: the representation of one or more common stimulus features in pairs of relevant brain regions - for example, estimates of the spatial position of both the cat and the dog represented in both the visual and auditory systems. We characterize how the reliability of this solution depends on different features of the stimulus set (e.g., the size of the set and the complexity of the stimuli) and the details of the split representations (e.g., the precision of each stimulus representation and the amount of overlapping information). Next, we implement this solution in a biologically plausible receptive field code and show how constraints on the number of neurons and spikes used by the code force the brain to navigate a tradeoff between local and catastrophic errors. We show that, when many spikes and neurons are available, representing stimuli from a single sensory modality can be done more reliably across multiple brain regions, despite the risk of assignment errors. Finally, we show that a feedforward neural network can learn the optimal solution to the assignment problem. We also discuss relevant results on assignment errors from the human working memory literature and show that several key predictions of our theory already have support.

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bioRxiv2022

The lateral intraparietal area preferentially supports stimulus selection in directed tasks compared to undirected free behavior

Our reactions to the sensory world depend on context. For instance, explicit directions to search for a particular object or feature will induce a different treatment of the sensory world than exploration without a fixed goal. To understand how we navigate the sensory world, it is necessary to understand how both directed search and undirected exploration are produced by the brain. The lateral intraparietal area (LIP) in the posterior parietal cortex has an established role in visual stimulus selection. However, most studies of LIPs role in stimulus selection focus primarily on highly trained, directed tasks, in which animals are given explicit cues as to which stimulus they should select. Here, we compare neural activity in LIP across two tasks. In one task, the animal is given an explicit direction to select one of two natural images from an array; in the other, the animal is allowed to choose an image freely based on their innate preferences. We find that LIP reliably encodes the eye movement prior to its execution only in the directed task, while the eye movement encoding in the undirected task emerges significantly later. Further, LIPs encoding of the images behavioral relevance emerges after the decision in both tasks. These results indicate that LIP preferentially supports stimulus selection in highly trained and directed behaviors, as opposed to free behavior.

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bioRxiv2021

Abstract representations emerge naturally in neural networks trained to perform multiple tasks

Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability to generalize arises from a specific representational geometry, that we call abstract and that is referred to as disentangled in machine learning. These abstract representations have been observed in recent neurophysiological studies. However, it is unknown how they emerge. Here, using feedforward neural networks, we demonstrate that the learning of multiple tasks causes abstract representations to emerge, using both supervised and reinforcement learning. We show that these abstract representations enable few-sample learning and reliable generalization on novel tasks. We conclude that abstract representations of sensory and cognitive variables may emerge from the multiple behaviors that animals exhibit in the natural world, and, as a consequence, could be pervasive in high-level brain regions. We also make several specific predictions about which variables will be represented abstractly.

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bioRxiv2019

Nonlinear mixed selectivity supports reliable neural computation

Neuronal activity in the brain is variable, yet both perception and behavior are generally reliable. How does the brain achieve this? Here, we show that the conjunctive coding of multiple stimulus features, commonly known as nonlinear mixed selectivity, may be used by the brain to support reliable information transmission using unreliable neurons. Nonlinear mixed selectivity (NMS) has been observed widely across the brain, from primary sensory to decision-making to motor areas. Representations of stimulus features are nearly always mixed together, rather than represented separately or with only additive (linear) mixing, as in pure selectivity. NMS has been previously shown to support flexible linear decoding for complex behavioral tasks. Here, we show that NMS has another important benefit: it requires as little as half the metabolic energy required by pure selectivity to achieve the same level of transmission reliability. This benefit holds for sensory, motor, and more abstract, cognitive representations. Further, we show experimental evidence that NMS exists in the brain even when it does not enable behaviorally useful linear decoding. This suggests that NMS may be a general coding scheme exploited by the brain for reliable and efficient neural computation.

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