洞察 - Computational neuroscience - # Dynamic control of sequential neural activity retrieval speed

Flexible Control of Sequential Retrieval Speed in Neural Networks with Heterogeneous Synaptic Plasticity Rules

Q: How might the proposed mechanism for flexible speed control interact with other known mechanisms for temporal control, such as short-term synaptic depression or feedforward inhibition?

The proposed mechanism for flexible speed control through heterogeneous plasticity rules could interact with other known mechanisms for temporal control in several ways. Short-term synaptic depression, for example, could complement the heterogeneous plasticity rules by providing a mechanism for regulating the strength of synaptic connections based on recent activity. This could influence the dynamics of sequence retrieval by modulating the efficacy of synapses involved in the network activity. Similarly, feedforward inhibition could play a role in shaping the dynamics of sequence retrieval by controlling the flow of information within the network. By selectively inhibiting certain pathways or populations of neurons, feedforward inhibition could influence the timing and speed of sequential activity. When combined with the heterogeneous plasticity rules, feedforward inhibition could provide an additional layer of control over the network dynamics, allowing for more precise temporal regulation. Overall, the interaction between the proposed mechanism for flexible speed control and other known mechanisms for temporal control could lead to a more robust and adaptable system for regulating sequential activity in neural networks. By integrating multiple mechanisms, the network could exhibit complex and dynamic temporal behaviors that are essential for various cognitive functions.

Q: What are the potential limitations of the heterogeneous plasticity rule approach, and how might it be extended to account for more complex temporal dynamics observed in biological neural networks?

One potential limitation of the heterogeneous plasticity rule approach is the assumption of discrete degrees of temporal symmetry across synapses. In reality, the temporal asymmetry of synaptic plasticity may vary continuously across synapses, leading to a more nuanced and complex network structure. To address this limitation, the approach could be extended to incorporate a continuous distribution of temporal asymmetry values, allowing for a more realistic representation of synaptic plasticity in biological neural networks. Furthermore, the current model focuses on the retrieval of sequential activity, but biological neural networks exhibit a wide range of temporal dynamics beyond simple sequences. To account for more complex temporal dynamics, the heterogeneous plasticity rule approach could be extended to include additional factors such as recurrent feedback loops, neuromodulatory influences, and network oscillations. By incorporating these elements, the model could capture the rich temporal dynamics observed in biological neural networks, including oscillatory patterns, phase-locking phenomena, and dynamic synchronization. Additionally, the model could be refined to incorporate more detailed biophysical properties of synapses, such as spike-timing-dependent plasticity (STDP) rules specific to different types of synapses and neurotransmitter systems. By integrating these biophysical details, the model could better capture the intricate interplay between synaptic plasticity mechanisms and network dynamics in biological neural networks.

Q: Could the principles of flexible speed control through heterogeneous plasticity rules be applied to other domains beyond sequential activity retrieval, such as decision-making or motor control?

Yes, the principles of flexible speed control through heterogeneous plasticity rules could be applied to other domains beyond sequential activity retrieval, such as decision-making or motor control. In decision-making tasks, for example, the ability to modulate the speed of information processing could be crucial for adapting to changing environmental conditions and making optimal choices. By incorporating heterogeneous plasticity rules into neural networks involved in decision-making processes, the system could dynamically adjust its processing speed based on task demands and cognitive requirements. Similarly, in motor control tasks, the flexibility of speed control could be essential for coordinating complex movements and adjusting execution speed in real-time. By implementing heterogeneous plasticity rules in motor control networks, the system could regulate the timing and speed of motor responses to achieve precise and coordinated movements. This could be particularly useful in tasks requiring fine motor skills, rapid responses, or adaptive motor behaviors. Overall, the principles of flexible speed control through heterogeneous plasticity rules have broad applicability across various cognitive and motor domains, where dynamic temporal regulation is essential for efficient and adaptive neural processing. By leveraging these principles, researchers can design more sophisticated neural network models that capture the dynamic and flexible nature of neural information processing in diverse behavioral contexts.

核心概念

Heterogeneity in the temporal symmetry of synaptic plasticity rules across neurons in a recurrent neural network enables flexible control of the speed of sequential activity retrieval by modulating external inputs.

摘要

The paper investigates a mechanism for flexible control of the speed of sequential activity retrieval in recurrent neural network models. The key idea is to introduce heterogeneity in the temporal symmetry of the synaptic plasticity rules across neurons in the network.

The network stores a sequence of activity patterns through Hebbian learning. Neurons with temporally symmetric plasticity rules act as "brakes" that stabilize the current network state, while neurons with temporally asymmetric rules act as "accelerators" that drive the transition to the next pattern in the sequence.

By modulating the external inputs to these two subpopulations of neurons, the speed of sequential activity retrieval can be flexibly controlled. The authors show that this mechanism works both in rate-based networks and spiking networks with excitatory and inhibitory neurons.

Furthermore, the authors demonstrate that this heterogeneity in plasticity rules can also enable transitions between persistent "preparatory" activity and sequential "execution" activity, by appropriately changing the external inputs. Finally, they show that the mapping between external inputs and retrieval speed can be learned through a reinforcement learning scheme.

The findings suggest a potential functional role for the experimentally observed diversity in synaptic plasticity rules across different brain regions and even within local networks. This heterogeneity may enable flexible control of the temporal dynamics of neural activity, which is crucial for the proper planning and execution of temporally extended behaviors.

自定义摘要

使用 AI 改写

生成参考文献

翻译原文

翻译成其他语言

生成思维导图

从原文生成

访问来源

biorxiv.org

统计

"Retrieval speed can now be arbitrarily slowed down, and even completely stopped when the input to the asymmetric population is sufficiently negative."
"Retrieval speed can be modulated by varying external inputs to the network. Neurons with temporally symmetric plasticity rules act as brakes and tend to slow down the dynamics, while neurons with temporally asymmetric rules act as accelerators of the dynamics."
"Small external input biases (∼ 1mV) relative to the difference in spiking threshold and resting potential (20mV) are sufficient to generate a temporal rescaling of as large as ∼ 2."

引用

"Neurons with temporally symmetric plasticity rules act as brakes and tend to slow down the dynamics, while neurons with temporally asymmetric rules act as accelerators of the dynamics."
"Transitions between persistent 'preparatory' activity and sequential 'execution' activity can be realized in this model by rescaling the magnitude of external input."
"Once the mapping between external inputs and retrieval speed is learned, it can be used to control the speed of other stored sequences without having to relearn this mapping."

从中提取的关键见解

Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules

by Gillett,M., ... 在 www.biorxiv.org 03-24-2023

https://www.biorxiv.org/content/10.1101/2023.03.22.533836v2

更深入的查询

How might the proposed mechanism for flexible speed control interact with other known mechanisms for temporal control, such as short-term synaptic depression or feedforward inhibition?

The proposed mechanism for flexible speed control through heterogeneous plasticity rules could interact with other known mechanisms for temporal control in several ways. Short-term synaptic depression, for example, could complement the heterogeneous plasticity rules by providing a mechanism for regulating the strength of synaptic connections based on recent activity. This could influence the dynamics of sequence retrieval by modulating the efficacy of synapses involved in the network activity.
Similarly, feedforward inhibition could play a role in shaping the dynamics of sequence retrieval by controlling the flow of information within the network. By selectively inhibiting certain pathways or populations of neurons, feedforward inhibition could influence the timing and speed of sequential activity. When combined with the heterogeneous plasticity rules, feedforward inhibition could provide an additional layer of control over the network dynamics, allowing for more precise temporal regulation.
Overall, the interaction between the proposed mechanism for flexible speed control and other known mechanisms for temporal control could lead to a more robust and adaptable system for regulating sequential activity in neural networks. By integrating multiple mechanisms, the network could exhibit complex and dynamic temporal behaviors that are essential for various cognitive functions.

What are the potential limitations of the heterogeneous plasticity rule approach, and how might it be extended to account for more complex temporal dynamics observed in biological neural networks?

One potential limitation of the heterogeneous plasticity rule approach is the assumption of discrete degrees of temporal symmetry across synapses. In reality, the temporal asymmetry of synaptic plasticity may vary continuously across synapses, leading to a more nuanced and complex network structure. To address this limitation, the approach could be extended to incorporate a continuous distribution of temporal asymmetry values, allowing for a more realistic representation of synaptic plasticity in biological neural networks.
Furthermore, the current model focuses on the retrieval of sequential activity, but biological neural networks exhibit a wide range of temporal dynamics beyond simple sequences. To account for more complex temporal dynamics, the heterogeneous plasticity rule approach could be extended to include additional factors such as recurrent feedback loops, neuromodulatory influences, and network oscillations. By incorporating these elements, the model could capture the rich temporal dynamics observed in biological neural networks, including oscillatory patterns, phase-locking phenomena, and dynamic synchronization.
Additionally, the model could be refined to incorporate more detailed biophysical properties of synapses, such as spike-timing-dependent plasticity (STDP) rules specific to different types of synapses and neurotransmitter systems. By integrating these biophysical details, the model could better capture the intricate interplay between synaptic plasticity mechanisms and network dynamics in biological neural networks.

Could the principles of flexible speed control through heterogeneous plasticity rules be applied to other domains beyond sequential activity retrieval, such as decision-making or motor control?

Yes, the principles of flexible speed control through heterogeneous plasticity rules could be applied to other domains beyond sequential activity retrieval, such as decision-making or motor control. In decision-making tasks, for example, the ability to modulate the speed of information processing could be crucial for adapting to changing environmental conditions and making optimal choices. By incorporating heterogeneous plasticity rules into neural networks involved in decision-making processes, the system could dynamically adjust its processing speed based on task demands and cognitive requirements.
Similarly, in motor control tasks, the flexibility of speed control could be essential for coordinating complex movements and adjusting execution speed in real-time. By implementing heterogeneous plasticity rules in motor control networks, the system could regulate the timing and speed of motor responses to achieve precise and coordinated movements. This could be particularly useful in tasks requiring fine motor skills, rapid responses, or adaptive motor behaviors.
Overall, the principles of flexible speed control through heterogeneous plasticity rules have broad applicability across various cognitive and motor domains, where dynamic temporal regulation is essential for efficient and adaptive neural processing. By leveraging these principles, researchers can design more sophisticated neural network models that capture the dynamic and flexible nature of neural information processing in diverse behavioral contexts.