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1. Sparse Firing in a Hybrid Central Pattern Generator for Spinal Motor Circuits

   https://doi.org/10.1162/neco_a_01660  

   Strohmer, Beck; Najarro, Elias; Ausborn, Jessica; Berg, Rune W; Tolu, Silvia (April 2024, Neural Computation)

   Central pattern generators are circuits generating rhythmic movements, such as walking. The majority of existing computational models of these circuits produce antagonistic output where all neurons within a population spike with a broad burst at about the same neuronal phase with respect to network output. However, experimental recordings reveal that many neurons within these circuits fire sparsely, sometimes as rarely as once within a cycle. Here we address the sparse neuronal firing and develop a model to replicate the behavior of individual neurons within rhythm-generating populations to increase biological plausibility and facilitate new insights into the underlying mechanisms of rhythm generation. The developed network architecture is able to produce sparse firing of individual neurons, creating a novel implementation for exploring the contribution of network architecture on rhythmic output. Furthermore, the introduction of sparse firing of individual neurons within the rhythm-generating circuits is one of the factors that allows for a broad neuronal phase representation of firing at the population level. This moves the model toward recent experimental findings of evenly distributed neuronal firing across phases among individual spinal neurons. The network is tested by methodically iterating select parameters to gain an understanding of how connectivity and the interplay of excitation and inhibition influence the output. This knowledge can be applied in future studies to implement a biologically plausible rhythm-generating circuit for testing biological hypotheses. 

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2. Task-Optimized Retinal-Inspired CNN Converges to Biologically-Plausible Functionality

   Murray, K.; Wang, B.; Lynch, N. (January 2021, 8th Workshop on Biological Distributed Algorithms (BDA))

   Convolutional neural networks (CNN) are an emerging technique in modeling neural circuits and have been shown to converge to biologically plausible functionality in cortical circuits via task-optimization. This functionality has not been observed in CNN models of retinal circuits via task-optimization. We sought to observe this convergence in retinal circuits by designing a biologically inspired CNN model of a motion-detection retinal circuit and optimizing it to solve a motion-classification task. The learned weights and parameters indicated that the CNN converged to direction-sensitive ganglion and amacrine cells, cell types that have been observed in biology, and provided evidence that task-optimization is a fair method of building retinal models. The analysis used to understand the functionality of our CNN also indicates that biologically constrained deep learning models are easier to reason about their underlying mechanisms than traditional deep learning models. 

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3. Task-Optimized Retinal-Inspired CNN Converges to Biologically-Plausible Functionality. 8th Workshop on Biological Distributed Algorithms (BDA), July 2021.

   Murray, K.; Wang, B.; Lynch, N. (January 2021, 8th Workshop on Biological Distributed Algorithms (BDA))

   Convolutional neural networks (CNN) are an emerging technique in modeling neural circuits and have been shown to converge to biologically plausible functionality in cortical circuits via task-optimization. This functionality has not been observed in CNN models of retinal circuits via task-optimization. We sought to observe this convergence in retinal circuits by designing a biologically inspired CNN model of a motion-detection retinal circuit and optimizing it to solve a motion-classification task. The learned weights and parameters indicated that the CNN converged to direction-sensitive ganglion and amacrine cells, cell types that have been observed in biology, and provided evidence that task-optimization is a fair method of building retinal models. The analysis used to understand the functionality of our CNN also indicates that biologically constrained deep learning models are easier to reason about their underlying mechanisms than traditional deep learning models. 

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4. The contribution of distributional activities of dopamine in the exploration

   Wang; Mien Brabeeba; Lynch, Nancy; Halassa, Michael (January 2023, 9th workshop on Biological Distributed Algorithms (BDA))

   Decision making in natural settings requires efficient exploration to handle uncertainty. Since associations between actions and outcomes are uncertain, animals need to balance the explorations and exploitation to select the actions that lead to maximal rewards. The computa- tional principles by which animal brains explore during decision-making are poorly understood. Our challenge here was to build a biologically plausible neural network that efficiently explores an environment and understands its effectiveness mathematically. One of the most evolutionarily conserved and important systems in decision making is basal ganglia (BG)1. In particular, the dopamine activities (DA) in BG is thought to represent reward prediction error (RPE) to facilitate reinforcement learning2. Therefore, our starting point is a cortico-BG loop motif3. This network adjusts exploration based on neuronal noises and updates its value estimate through RPE. To account for the fact that animals adjust exploration based on experience, we modified the network in two ways. First, it is recently discovered that DA does not simply represent the scalar RPE value; rather it represents RPE in a distribution4. We incorporated the distributional RPE framework and further the hypothesis, allowing an RPE distribution to update the posterior of action values encoded by cortico-BG connections. Second, it is known that the firing in the layer 2/3 of cortex fires is variable and sparse5. Our network thus included a random sparsification of cortical activity as a mechanism of sampling from this posterior for experience-based exploration. Combining these two features, our network is able to take the uncertainty of our value estimates into account to accomplish efficient exploration in a variety of environments. 

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5. The contribution of distributional activities of dopamine in the exploration

   Wang, Mien Brabeeba; Lynch, Nancy; Halassa, Michael (June 2023, 9th workshop on Biological Distributed Algorithms (BDA))

   Decision making in natural settings requires efficient exploration to handle uncertainty. Since associations between actions and outcomes are uncertain, animals need to balance the explorations and exploitation to select the actions that lead to maximal rewards. The computa- tional principles by which animal brains explore during decision-making are poorly understood. Our challenge here was to build a biologically plausible neural network that efficiently explores an environment and understands its effectiveness mathematically. One of the most evolutionarily conserved and important systems in decision making is basal ganglia (BG)1. In particular, the dopamine activities (DA) in BG is thought to represent reward prediction error (RPE) to facilitate reinforcement learning2. Therefore, our starting point is a cortico-BG loop motif3. This network adjusts exploration based on neuronal noises and updates its value estimate through RPE. To account for the fact that animals adjust exploration based on experience, we modified the network in two ways. First, it is recently discovered that DA does not simply represent the scalar RPE value; rather it represents RPE in a distribution4. We incorporated the distributional RPE framework and further the hypothesis, allowing an RPE distribution to update the posterior of action values encoded by cortico-BG connections. Second, it is known that the firing in the layer 2/3 of cortex fires is variable and sparse5. Our network thus included a random sparsification of cortical activity as a mechanism of sampling from this posterior for experience-based exploration. Combining these two features, our network is able to take the uncertainty of our value estimates into account to accomplish efficient exploration in a variety of environments. 

   more » « less