13 papers · ranked by Valyu relevance
Benjamin J. Arthur, Christopher M. Kim, Susu Chen, Stephan Preibisch + 1 more
Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a prominent tool to study computations in the brain. With an increasing size and complexity of neural recordings, there is a need for fast algorithms that can scale to large datasets. We present optimized CPU and GPU…
Sahar Jahani, Seyed Kamaledin Setarehdan
Near infrared spectroscopy allows monitoring of oxy and deoxyhemoglobin concentration changes associated with hemodynamic response function (HRF). HRF is mainly affected by physiological interferences which occur in the superficial layers of the head. This makes HRF extracting a very challenging task. Recent studies…
Neythen J. Treloar, Nathan Braniff, Brian Ingalls, Chris P. Barnes
The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence—reinforcement learning—to the optimal experimental design task of maximizing confidence in estimates of…
Robert Kim, Yinghao Li, Terrence J. Sejnowski
Cortical microcircuits exhibit complex recurrent architectures that possess dynamically rich properties. The neurons that make up these microcircuits communicate mainly via discrete spikes, and it is not clear how spikes give rise to dynamics that can be used to perform computationally challenging tasks. In contrast…
Kenway Louie
Learning is widely modeled in psychology, neuroscience, and computer science by prediction error-guided reinforcement learning (RL) algorithms. While standard RL assumes linear reward functions, reward-related neural activity is a saturating, nonlinear function of reward; however, the computational and behavioral…
Beren Millidge, Mark Walton, Rafal Bogacz
An influential theory posits that dopaminergic neurons in the mid-brain implement a model-free reinforcement learning algorithm based on temporal difference (TD) learning. A fundamental assumption of this model is that the reward function being optimized is fixed. However, for biological creatures the ‘reward function’…
Huzi Cheng, Joshua W. Brown
Goal-directed planning presents a challenge for classical Reinforcement Learning (RL) algorithms due to the vastness of combinatorial state and goal spaces. Humans and animals adapt to complex environments especially with diverse, non-stationary objectives, often employing intermediate goals for long-horizon tasks.…
Chenguang Li, Gabriel Kreiman, Sharad Ramanathan
Artificial neural networks have performed remarkable feats in a wide variety of domains. However, artificial intelligence algorithms lack the flexibility, robustness, and generalization power of biological neural networks. Given the different capabilities of artificial and biological neural networks, it would be…
Li Ji-An, Marcus K. Benna, Marcelo G. Mattar
Normative modeling frameworks such as Bayesian inference and reinforcement learning provide valuable insights into the fundamental principles governing adaptive behavior. While these frameworks are valued for their simplicity and interpretability, their reliance on few parameters often limits their ability to capture…
Prasad U. Bandodkar, Razeen R. Shaikh, Gregory T. Reeves
Model development is essential to gain a mathematical understanding of the underlying phenomena in systems biology. In most models, it is typically hard to estimate the values of the biophysical/phenomenological parameters that characterize the model. The parameters are estimated by minimizing a function that reduces a…
Ian Cone, Claudia Clopath, Harel Z. Shouval
Dopamine (DA) releasing neurons in the midbrain learn response patterns that represent reward prediction error (RPE). Typically, models proposing a mechanistic explanation for how dopamine neurons learn to exhibit RPE are based on temporal difference (TD) learning, a machine learning algorithm. However, mechanistic…
Tamara Müller, Pietro Lio’
Neurodegenerative diseases such as Alzheimer’s and Parkinson’s impact millions of people worldwide. Early diagnosis has proven to greatly increase the chances of slowing down the diseases’ progression. Correct diagnosis often relies on the analysis of large amounts of patient data, and thus lends itself well to support…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…