インサイト - Computational neuroscience - # Dendritic Computations in Randomly Connected Feedforward Networks

Likelihood of Dendritic Computations in Randomly Connected Feedforward Neural Networks

Q: What are the potential functional implications of the observed likelihood of dendritic computations in feedforward networks for neural information processing and representation?

The findings from the study highlight the significant role of dendritic computations in enhancing neural information processing and representation within feedforward networks. The likelihood of clustered convergence of inputs from co-active neural ensembles onto dendritic segments suggests that single neurons can effectively decode complex patterns of activity. This capability is crucial for mixed selectivity, where neurons respond to combinations of stimulus features rather than isolated inputs. The presence of dendritic nonlinearities, such as dendritic spikes and calcium-induced calcium release (CICR), allows for the amplification of synaptic inputs, leading to stronger postsynaptic responses when inputs are clustered. This mechanism supports the representation of arbitrary input combinations, enabling neurons to discriminate between different sensory, motor, and cognitive events. The ability to perform such computations at the dendritic level can enhance the overall efficiency of neural coding, allowing for more sophisticated processing of information. Furthermore, the study suggests that the interplay between network connectivity, background activity, and dendritic properties is essential for achieving somatic selectivity, which is vital for accurate information representation in neural circuits.

Q: How might the findings be affected by the inclusion of feedback connections or recurrent network dynamics, which are prevalent in many brain regions?

The inclusion of feedback connections and recurrent dynamics could significantly alter the functional implications of dendritic computations observed in feedforward networks. In recurrent networks, the presence of feedback loops can lead to more complex interactions between neurons, potentially enhancing the robustness of pattern recognition and sequence processing. Feedback connections may facilitate the integration of past inputs with current activity, allowing for temporal context to influence neuronal responses. This could enhance the ability of neurons to maintain representations over time, which is particularly important for tasks such as memory and learning. Moreover, recurrent dynamics could introduce additional sources of variability and noise, which may affect the reliability of dendritic computations. While the study emphasizes the importance of low background activity for effective dendritic processing, recurrent networks often operate under conditions of higher noise due to continuous feedback. This could lead to challenges in distinguishing between true signals and noise, potentially complicating the decoding of input patterns. However, the presence of feedback could also provide mechanisms for error correction and stabilization of representations, thereby enhancing the overall computational capacity of neural circuits.

Q: Could the principles of dendritic computations identified in this study be leveraged in the design of artificial neural networks to improve their pattern recognition and sequence processing capabilities?

Yes, the principles of dendritic computations identified in this study can be effectively leveraged in the design of artificial neural networks (ANNs) to enhance their pattern recognition and sequence processing capabilities. By incorporating mechanisms that mimic dendritic nonlinearities, such as activation functions that exhibit saturation and amplification similar to dendritic spikes, ANNs can improve their ability to process complex input patterns. Additionally, the concept of clustered inputs can be integrated into ANN architectures by designing layers that allow for the convergence of multiple inputs onto specific nodes, thereby facilitating mixed selectivity. This could lead to more efficient representations of data, enabling the network to learn and recognize combinations of features rather than relying solely on individual inputs. Furthermore, the study's insights into the importance of network connectivity and background activity can inform the design of more robust training algorithms that account for noise and variability in data. By simulating the conditions under which dendritic computations thrive—such as low noise and high co-activity—ANNs can be trained to achieve better performance in tasks involving pattern recognition and sequence processing. Overall, the principles derived from biological dendritic computations offer valuable strategies for enhancing the functionality and efficiency of artificial neural networks.

核心概念

Random feedforward connectivity, along with large enough neural ensembles (∼100 neurons for groups and ∼1000 neurons for sequences), is sufficient to lead to the convergence of groups or sequences consisting of 3-5 inputs onto small dendritic zones. This provides a substrate for downstream networks to decode arbitrary input patterns.

要約

The content explores the likelihood of dendritic computations, such as grouped and sequential inputs, in randomly connected feedforward neural networks. Key insights:

Grouped convergence of 3-4 inputs from different neural ensembles is likely in most network configurations, providing a substrate for downstream neurons to decode arbitrary input patterns.
Sequential convergence of 3-5 inputs requires larger ensembles (∼1000 neurons) and is more sensitive to background noise. Low noise conditions are better suited for sequence discrimination.
Ectopic inputs can degrade sequence selectivity, with electrical sequences being more sensitive to distal ectopic inputs compared to chemical sequences.
Even with strong dendritic selectivity, somatic responses may show weak selectivity due to the overwhelming influence of background activity. Balancing excitation and inhibition can help improve sequence selectivity at the soma.

The analysis combines theory, simulations, and biologically-inspired models to assess the statistical likelihood and computational implications of dendritic computations in randomly connected feedforward networks.

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biorxiv.org

統計

Probability of occurrence of fully-mixed groups of 3-4 inputs from different neural ensembles is likely in 4 out of 6 network configurations tested.
Probability of occurrence of stimulus-driven groups (receiving inputs from any ensemble) of 3-5 inputs is up to two orders of magnitude higher than fully-mixed groups.
Probability of occurrence of perfectly-ordered sequences of 3-5 inputs requires larger ensembles (∼1000 neurons) in a population of ∼100,000 neurons.
Ectopic inputs can degrade sequence selectivity by 27-53% for mid-length sequences (5-7 inputs), but have a smaller effect on longer sequences (16-18% drop).
Somatic sequence selectivity is weak (0.05-0.08) even with strong dendritic selectivity (∼0.8), but can be improved to 0.13-0.22 by accounting for excitation-inhibition balance.

引用

"Random feedforward connectivity, along with large enough neural ensembles (∼100 neurons for groups and ∼1000 neurons for sequences), is sufficient to lead to the convergence of groups or sequences consisting of 3-5 inputs onto small dendritic zones."
"Ectopic inputs can degrade sequence selectivity, with electrical sequences being more sensitive to distal ectopic inputs compared to chemical sequences."
"Even with strong dendritic selectivity, somatic responses may show weak selectivity due to the overwhelming influence of background activity. Balancing excitation and inhibition can help improve sequence selectivity at the soma."

抽出されたキーインサイト

Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks

by Somashekar,B... 場所 www.biorxiv.org 05-21-2024

https://www.biorxiv.org/content/10.1101/2024.05.20.595075v3

深掘り質問

What are the potential functional implications of the observed likelihood of dendritic computations in feedforward networks for neural information processing and representation?

The findings from the study highlight the significant role of dendritic computations in enhancing neural information processing and representation within feedforward networks. The likelihood of clustered convergence of inputs from co-active neural ensembles onto dendritic segments suggests that single neurons can effectively decode complex patterns of activity. This capability is crucial for mixed selectivity, where neurons respond to combinations of stimulus features rather than isolated inputs. The presence of dendritic nonlinearities, such as dendritic spikes and calcium-induced calcium release (CICR), allows for the amplification of synaptic inputs, leading to stronger postsynaptic responses when inputs are clustered.
This mechanism supports the representation of arbitrary input combinations, enabling neurons to discriminate between different sensory, motor, and cognitive events. The ability to perform such computations at the dendritic level can enhance the overall efficiency of neural coding, allowing for more sophisticated processing of information. Furthermore, the study suggests that the interplay between network connectivity, background activity, and dendritic properties is essential for achieving somatic selectivity, which is vital for accurate information representation in neural circuits.

How might the findings be affected by the inclusion of feedback connections or recurrent network dynamics, which are prevalent in many brain regions?

The inclusion of feedback connections and recurrent dynamics could significantly alter the functional implications of dendritic computations observed in feedforward networks. In recurrent networks, the presence of feedback loops can lead to more complex interactions between neurons, potentially enhancing the robustness of pattern recognition and sequence processing. Feedback connections may facilitate the integration of past inputs with current activity, allowing for temporal context to influence neuronal responses. This could enhance the ability of neurons to maintain representations over time, which is particularly important for tasks such as memory and learning.
Moreover, recurrent dynamics could introduce additional sources of variability and noise, which may affect the reliability of dendritic computations. While the study emphasizes the importance of low background activity for effective dendritic processing, recurrent networks often operate under conditions of higher noise due to continuous feedback. This could lead to challenges in distinguishing between true signals and noise, potentially complicating the decoding of input patterns. However, the presence of feedback could also provide mechanisms for error correction and stabilization of representations, thereby enhancing the overall computational capacity of neural circuits.

Could the principles of dendritic computations identified in this study be leveraged in the design of artificial neural networks to improve their pattern recognition and sequence processing capabilities?

Yes, the principles of dendritic computations identified in this study can be effectively leveraged in the design of artificial neural networks (ANNs) to enhance their pattern recognition and sequence processing capabilities. By incorporating mechanisms that mimic dendritic nonlinearities, such as activation functions that exhibit saturation and amplification similar to dendritic spikes, ANNs can improve their ability to process complex input patterns.
Additionally, the concept of clustered inputs can be integrated into ANN architectures by designing layers that allow for the convergence of multiple inputs onto specific nodes, thereby facilitating mixed selectivity. This could lead to more efficient representations of data, enabling the network to learn and recognize combinations of features rather than relying solely on individual inputs.
Furthermore, the study's insights into the importance of network connectivity and background activity can inform the design of more robust training algorithms that account for noise and variability in data. By simulating the conditions under which dendritic computations thrive—such as low noise and high co-activity—ANNs can be trained to achieve better performance in tasks involving pattern recognition and sequence processing. Overall, the principles derived from biological dendritic computations offer valuable strategies for enhancing the functionality and efficiency of artificial neural networks.