arXiv subjects

Ilenna Simone Jones

Publications and source records attributed to Ilenna Simone Jones.

6 recordsLinked to original sources

arXiv2026

Synaptic clustering emerges from learning and supports covariance discrimination

Functional synapse clusters (FSCs) are synapses with correlated presynaptic activity that are colocalized on the same neuronal dendritic branch. FSCs have been observed after learning in cortical and hippocampal pyramidal neurons. However, previous efforts to ablate FSCs by pharmacologically blocking dendritic nonlinearities to establish causal necessity may have confounded effects. Therefore, whether FSCs are causally necessary for computation is unknown. Here, we attempt to isolate FSCs from this potential confounder in silico. We train Dendrinet, an artificial neural network architecture with hierarchical dendritic segments and sparse conductance-based synapses, on a Permuted-Covariance Classification (PCC) task. This task cannot be solved by single-layer linear-nonlinear artificial neural networks. We find that neurons with dendrites can be trained to solve the task and develop excitatory and inhibitory FSCs if both dendritic nonlinearities and synaptic structural plasticity are active. Turning off dendritic nonlinearities reduces excitatory FSCs, which replicates experimental findings, and reduces performance while unexpectedly increasing inhibitory FSCs. Furthermore, shuffling learned synaptic connectivity while keeping the nonlinearities fixed reduces performance. This shows sensitivity to learned connectivity, but the shuffle does not change only FSCs. Shuffling inhibitory synapse properties reduces performance more than the corresponding excitatory shuffle, showing higher sensitivity to inhibitory organization. This work suggests that dendritic compartmentalization and learned synaptic organization can support computation of covariance structure.

q-bio.NC↗

arXiv2024

Efficient optimization of ODE neuron models using gradient descent

Neuroscientists fit morphologically and biophysically detailed neuron simulations to physiological data, often using evolutionary algorithms. However, such gradient-free approaches are computationally expensive, making convergence slow when neuron models have many parameters. Here we introduce a gradient-based algorithm using differentiable ODE solvers that scales well to high-dimensional problems. GPUs make parallel simulations fast and gradient calculations make optimization efficient. We verify the utility of our approach optimizing neuron models with active dendrites with heterogeneously distributed ion channel densities. We find that individually stimulating and recording all dendritic compartments makes such model parameters identifiable. Identification breaks down gracefully as fewer stimulation and recording sites are given. Differentiable neuron models, which should be added to popular neuron simulation packages, promise a new era of optimizable neuron models with many free parameters, a key feature of real neurons.

q-bio.NC↗

arXiv2021

Do biological constraints impair dendritic computation?

Computations on the dendritic trees of neurons have important constraints. Voltage dependent conductances in dendrites are not similar to arbitrary direct-current generation, they are the basis for dendritic nonlinearities and they do not allow converting positive currents into negative currents. While it has been speculated that the dendritic tree of a neuron can be seen as a multi-layer neural network and it has been shown that such an architecture could be computationally strong, we do not know if that computational strength is preserved under these biological constraints. Here we simulate models of dendritic computation with and without these constraints. We find that dendritic model performance on interesting machine learning tasks is not hurt by these constraints but may benefit from them. Our results suggest that single real dendritic trees may be able to learn a surprisingly broad range of tasks.

q-bio.NC↗

arXiv2020

Can Single Neurons Solve MNIST? The Computational Power of Biological Dendritic Trees

Physiological experiments have highlighted how the dendrites of biological neurons can nonlinearly process distributed synaptic inputs. This is in stark contrast to units in artificial neural networks that are generally linear apart from an output nonlinearity. If dendritic trees can be nonlinear, biological neurons may have far more computational power than their artificial counterparts. Here we use a simple model where the dendrite is implemented as a sequence of thresholded linear units. We find that such dendrites can readily solve machine learning problems, such as MNIST or CIFAR-10, and that they benefit from having the same input onto several branches of the dendritic tree. This dendrite model is a special case of sparse network. This work suggests that popular neuron models may severely underestimate the computational power enabled by the biological fact of nonlinear dendrites and multiple synapses per pair of neurons. The next generation of artificial neural networks may significantly benefit from these biologically inspired dendritic architectures.

q-bio.NC↗

arXiv2019

On functions computed on trees

Any function can be constructed using a hierarchy of simpler functions through compositions. Such a hierarchy can be characterized by a binary rooted tree. Each node of this tree is associated with a function which takes as inputs two numbers from its children and produces one output. Since thinking about functions in terms of computation graphs is getting popular we may want to know which functions can be implemented on a given tree. Here, we describe a set of necessary constraints in the form of a system of non-linear partial differential equations that must be satisfied. Moreover, we prove that these conditions are sufficient in both contexts of analytic and bit-valued functions. In the latter case, we explicitly enumerate discrete functions and observe that there are relatively few. Our point of view allows us to compare different neural network architectures in regard to their function spaces. Our work connects the structure of computation graphs with the functions they can implement and has potential applications to neuroscience and computer science.

cs.LG↗

arXiv2019

Quantifying the role of neurons for behavior is a mediation question

Many systems neuroscientists want to understand neurons in terms of mediation; we want to understand how neurons are involved in the causal chain from stimulus to behavior. Unfortunately, most tools are inappropriate for that while our language takes mediation for granted. Here we discuss the contrast between our conceptual drive towards mediation and the difficulty of obtaining meaningful evidence.

q-bio.NC↗