A dynamic neural field model of continuous input integration

Wojtak, Weronika; Coombes, Stephen; Avitabile, Daniele; Bicho, Estela; Erlhagen, Wolfram

doi:10.1007/s00422-021-00893-7

“…Our findings support that large-scale patterns of modular activity emerge from intracortical interactions, which arise through a dynamic network of local facilitation and lateral suppression. Notably, the mechanisms that lead to the emergence of these patterns need not be specific to the visual cortex and could serve as a more universal mechanism for cortical organization throughout the brain, including the entorhinal 68,69 and prefrontal 70,71 10 cortices (see ref 65 for review). Future work will have to assess whether the LE/LI mechanism we demonstrate in visual cortex generalizes to these other cortical areas.…”

Section: Discussionmentioning

confidence: 99%

Self-organization of modular activity in immature cortical networks

Mulholland,

Kaschube,

Smith

2024

Preprint

2

0

View full text Add to dashboard Cite

During development, cortical activity is organized into distributed modular patterns that are a precursor of the mature columnar functional architecture. Theoretically, such structured neural activity can emerge dynamically from local synaptic interactions through a recurrent network with effective local excitation with lateral inhibition (LE/LI) connectivity. Utilizing simultaneous widefield calcium imaging and optogenetics in juvenile ferret cortex prior to eye opening, we directly test several critical predictions of an LE/LI mechanism. We show that cortical networks transform uniform stimulations into diverse modular patterns exhibiting a characteristic spatial wavelength. Moreover, patterned optogenetic stimulation matching this wavelength selectively biases evoked activity patterns, while stimulation with varying wavelengths transforms activity towards this characteristic wavelength, revealing a dynamic compromise between input drive and the network’s intrinsic tendency to organize activity. Furthermore, the structure of early spontaneous cortical activity – which is reflected in the developing representations of visual orientation – strongly overlaps that of uniform opto-evoked activity, suggesting a common underlying mechanism as a basis for the formation of orderly columnar maps underlying sensory representations in the brain.

show abstract

“…Our findings support that large-scale patterns of modular activity emerge from intracortical interactions, which arise through a dynamic network of local facilitation and lateral suppression. Notably, the mechanisms that lead to the emergence of these patterns need not be specific to the visual cortex and could serve as a more universal mechanism for cortical organization throughout the brain, including the entorhinal 74 , 75 and prefrontal 76 , 77 cortices (see ref. 61 for review).…”

Section: Discussionmentioning

confidence: 99%

Self-organization of modular activity in immature cortical networks

Mulholland,

Kaschube,

Smith

2024

Nat Commun

3

0

View full text Add to dashboard Cite

During development, cortical activity is organized into distributed modular patterns that are a precursor of the mature columnar functional architecture. Theoretically, such structured neural activity can emerge dynamically from local synaptic interactions through a recurrent network with effective local excitation with lateral inhibition (LE/LI) connectivity. Utilizing simultaneous widefield calcium imaging and optogenetics in juvenile ferret cortex prior to eye opening, we directly test several critical predictions of an LE/LI mechanism. We show that cortical networks transform uniform stimulations into diverse modular patterns exhibiting a characteristic spatial wavelength. Moreover, patterned optogenetic stimulation matching this wavelength selectively biases evoked activity patterns, while stimulation with varying wavelengths transforms activity towards this characteristic wavelength, revealing a dynamic compromise between input drive and the network’s intrinsic tendency to organize activity. Furthermore, the structure of early spontaneous cortical activity – which is reflected in the developing representations of visual orientation – strongly overlaps that of uniform opto-evoked activity, suggesting a common underlying mechanism as a basis for the formation of orderly columnar maps underlying sensory representations in the brain.

show abstract

“…With Equation (7) in mind, applying the dimensionality reduction protocol [41] leads to a differential equation model for how the position representation θ is controlled by the PI and the landmarks as follows:…”

Section: Landmark Correction To Path Integration Resembles a Kalman F...mentioning

confidence: 99%

“…The brain's ability to represent and process continuous variables, such as location, time, and sensory information, is fundamental to our understanding and interaction with the external world. A compelling theoretical framework for how the brain constructs these representations is provided by continuous bump attractor networks (CBANs) in a diverse range of brain functions, such as orientation tuning in visual cortex [1], working memory [2,3], evidence accumulation and decision-making [4][5][6][7], and spatial navigation [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Continuous Bump Attractor Networks Require Explicit Error Coding for Gain Recalibration

Secer,

Knierim,

Cowan

2024

Preprint

0

View full text Add to dashboard Cite

Internal representations of continuous variables are crucial to create internal models of the external world. A prevailing model of how brain maintains these representations is given by continuous bump attractor networks (CBANs). CBANs have been hypothesized as an underlying mechanism in a broad range of brain functions across different areas, such as spatial navigation in hippocampal/entorhinal circuits and working memory in prefrontal cortex. Through recurrent connections, a CBAN maintains a persistent activity bump, whose peak location can vary along a neural space, corresponding to different values of a continuous variable. To track the value of a continuous variable changing over time, a CBAN updates the location of its activity bump based on inputs that encode the changes in the continuous variable (e.g., movement velocity in the case of spatial navigation) -- a process akin to mathematical integration. This integration process is not perfect and accumulates error over time. For error correction, CBANs can use additional inputs providing ground-truth information about the continuous variable's correct value (e.g., visual landmarks for spatial navigation). These inputs enable the network dynamics to automatically correct any representation error by shifting the activity bump toward the correct location. Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground-truth inputs (e.g., visual landmarks) also fine-tune the gain of the integration process, a crucial factor that links the change in the continuous variable (e.g., movement velocity) to the updating of the activity bump's location. However, existing CBAN models lack this plasticity, offering no insights into the neural mechanisms and representations involved in the recalibration of the integration gain. In this paper, we explore this gap by using a ring attractor network, a specific type of CBAN, to model the experimental conditions that demonstrated gain recalibration in hippocampal place cells. Our analysis reveals the neural mechanisms behind gain recalibration within a CBAN: Unlike error correction, which occurs through network dynamics based on ground-truth inputs, gain recalibration requires an additional neural signal that explicitly encodes the error in the network's representation. This error signal must be provided by some neurons whose firing rate varies monotonically with respect to one of two signals---either the instantaneous error or the time-integral of the error---for recalibration of the integration gain. Finally, we propose a modified ring attractor network as an example CBAN model that verifies our theoretical findings. Combining an error-rate code with Hebbian synaptic plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables.

show abstract

A dynamic neural field model of continuous input integration

Cited by 9 publications

References 73 publications

Self-organization of modular activity in immature cortical networks

Self-organization of modular activity in immature cortical networks

Self-organization of modular activity in immature cortical networks

Continuous Bump Attractor Networks Require Explicit Error Coding for Gain Recalibration

Contact Info

Product

Resources

About