A unifying perspective on neural manifolds and circuits for cognition

Nature reviews. Neuroscience, Год журнала: 2023, Номер 24(6), С. 363 - 377

Опубликована: Апрель 13, 2023

Язык: Английский

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An approximate line attractor in the hypothalamus encodes an aggressive state

Cell, Год журнала: 2023, Номер 186(1), С. 178 - 193.e15

Опубликована: Янв. 1, 2023

The hypothalamus regulates innate social behaviors, including mating and aggression. These behaviors can be evoked by optogenetic stimulation of specific neuronal subpopulations within MPOA VMHvl, respectively. Here, we perform dynamical systems modeling population activity in these nuclei during behaviors. In unsupervised analysis identified a dominant dimension neural with large time constant (>50 s), generating an approximate line attractor state space. Progression the trajectory along this was correlated escalation agonistic behavior, suggesting that it may encode scalable aggressiveness. Consistent this, individual differences magnitude integration were strongly contrast, attractors not observed mating; instead, neurons fast dynamics tuned to actions. Thus, different hypothalamic employ distinct codes represent similar

Язык: Английский

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Neural heterogeneity controls computations in spiking neural networks

Proceedings of the National Academy of Sciences, Год журнала: 2024, Номер 121(3)

Опубликована: Янв. 10, 2024

The brain is composed of complex networks interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does this neural influence macroscopic dynamics, how might it contribute to computation? In work, we use a mean-field model investigate computation heterogeneous networks, by studying the cell thresholds affects three key computational functions population: gating, encoding, decoding signals. Our results suggest serves different types. inhibitory interneurons, varying degree spike threshold allows them gate propagation signals reciprocally coupled excitatory population. Whereas homogeneous interneurons impose synchronized dynamics narrow dynamic repertoire neurons, act as an offset while preserving neuron function. Spike also controls entrainment properties periodic input, thus affecting temporal gating synaptic inputs. Among increases dimensionality improving network’s capacity perform tasks. Conversely, suffer for function generation, but excel at encoding via multistable regimes. Drawing from these findings, propose intra-cell-type mechanism sculpting local circuits permitting same canonical microcircuit be tuned diverse

Язык: Английский

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The computational foundations of dynamic coding in working memory

Trends in Cognitive Sciences, Год журнала: 2024, Номер 28(7), С. 614 - 627

Опубликована: Апрель 4, 2024

Working memory (WM) is a fundamental aspect of cognition. WM maintenance classically thought to rely on stable patterns neural activities. However, recent evidence shows that population activities during undergo dynamic variations before settling into pattern. Although this has been difficult explain theoretically, network models optimized for typically also exhibit such dynamics. Here, we examine versus coding in data, classical models, and task-optimized networks. We review principled mathematical reasons why do not, while naturally coding. suggest an update our understanding maintenance, which computational feature rather than epiphenomenon.

Язык: Английский

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Functional connectomics reveals general wiring rule in mouse visual cortex

Nature, Год журнала: 2025, Номер 640(8058), С. 459 - 469

Опубликована: Апрель 9, 2025

Abstract Understanding the relationship between circuit connectivity and function is crucial for uncovering how brain computes. In mouse primary visual cortex, excitatory neurons with similar response properties are more likely to be synaptically connected 1–8 ; however, broader rules remain unknown. Here we leverage millimetre-scale MICrONS dataset analyse synaptic functional of across cortical layers areas. Our results reveal that preferentially within areas—including feedback connections—supporting universality ‘like-to-like’ hierarchy. Using a validated digital twin model, separated neuronal tuning into feature (what respond to) spatial (receptive field location) components. We found only component predicts fine-scale connections beyond what could explained by proximity axons dendrites. also discovered higher-order rule whereby postsynaptic neuron cohorts downstream presynaptic cells show greater similarity than predicted pairwise like-to-like rule. Recurrent neural networks trained on simple classification task develop patterns mirror both rules, magnitudes those in data. Ablation studies these recurrent disrupting impairs performance random connections. These findings suggest principles may have role sensory processing learning, highlighting shared biological artificial systems.

Язык: Английский

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Functional connectomics reveals general wiring rule in mouse visual cortex

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Март 14, 2023

Understanding the relationship between circuit connectivity and function is crucial for uncovering how brain implements computation. In mouse primary visual cortex (V1), excitatory neurons with similar response properties are more likely to be synaptically connected, but previous studies have been limited within V1, leaving much unknown about broader rules. this study, we leverage millimeter-scale MICrONS dataset analyze synaptic functional of individual across cortical layers areas. Our results reveal that responses preferentially connected both areas — including feedback connections suggesting universality ‘like-to-like’ hierarchy. Using a validated digital twin model, separated neuronal tuning into feature (what respond to) spatial (receptive field location) components. We found only component predicts fine-scale connections, beyond what could explained by physical proximity axons dendrites. also higher-order rule where postsynaptic neuron cohorts downstream presynaptic cells show greater similarity than predicted pairwise like-to-like rule. Notably, recurrent neural networks (RNNs) trained on simple classification task develop patterns mirroring rules, magnitude those in data. Lesion these RNNs disrupting has significantly impact performance compared lesions random connections. These findings suggest principles may play role sensory processing learning, highlighting shared biological artificial systems.

Язык: Английский

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Neuromodulatory control of complex adaptive dynamics in the brain

Interface Focus, Год журнала: 2023, Номер 13(3)

Опубликована: Апрель 14, 2023

How is the massive dimensionality and complexity of microscopic constituents nervous system brought under sufficiently tight control so as to coordinate adaptive behaviour? A powerful means for striking this balance poise neurons close critical point a phase transition, at which small change in neuronal excitability can manifest nonlinear augmentation activity. brain could mediate transition key open question neuroscience. Here, I propose that different arms ascending arousal provide with diverse set heterogeneous parameters be used modulate receptivity target neurons-in other words, act mediating order. Through series worked examples, demonstrate how neuromodulatory interact inherent topological subsystems complex behaviour.

Язык: Английский

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Online dynamical learning and sequence memory with neuromorphic nanowire networks

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Ноя. 1, 2023

Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties nanostructured materials. In addition their neural network-like structure, NWNs also exhibit resistive memory switching in response electrical inputs due synapse-like changes conductance at nanowire-nanowire cross-point junctions. Previous studies have demonstrated how dynamics generated by can be harnessed for temporal learning tasks. This study extends these findings further demonstrating online from spatiotemporal dynamical features using image classification and sequence recall tasks implemented on NWN device. Applied MNIST handwritten digit task, with device achieves overall accuracy 93.4%. Additionally, we find a correlation between individual classes mutual information. The task reveals patterns embedded enable pattern. Overall, results provide proof-of-concept elucidate enhance learning.

Язык: Английский

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Sequential predictive learning is a unifying theory for hippocampal representation and replay

Опубликована: Апрель 29, 2024

Abstract The mammalian hippocampus contains a cognitive map that represents an animal’s position in the environment 1 and generates offline “replay” 2,3 for purposes of recall 4 , planning 5,6 forming long term memories 7 . Recently, it’s been found artificial neural networks trained to predict sensory inputs develop spatially tuned cells 8 aligning with predictive theories hippocampal function 9–11 However, whether learning can also account ability produce replay is unknown. Here, we find spatially-tuned cells, which robustly emerge from all forms learning, do not guarantee presence generate replay. Offline simulations only emerged used recurrent connections head-direction information multi-step observation sequences, promoted formation continuous attractor reflecting geometry environment. These trajectories were able show wake-like statistics, autonomously recently experienced locations, could be directed by virtual head direction signal. Further, make cyclical predictions future sequences rapidly learn produced sweeping representations positions reminiscent theta sweeps 12 results demonstrate how hippocampal-like representation engaged suggest reflect circuit implements data-efficient algorithm sequential learning. Together, this framework provides unifying theory functions hippocampal-inspired approaches intelligence.

Язык: Английский

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A line attractor encoding a persistent internal state requires neuropeptide signaling

Cell, Год журнала: 2024, Номер 187(21), С. 5998 - 6015.e18

Опубликована: Авг. 26, 2024

Язык: Английский

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