Alireza Modirshanechi

Biography

I am a computer science Ph.D. student in the Laboratory of Computational Neuroscience at EPFL where I work on computational models of learning and decision-making in the brain - under the supervision of Prof. Wulfram Gerstner. My main track of research focuses on (i) mathematical definitions of surprise and novelty, (ii) their influence on human behavior, and (iii) their manifestation in physiological measurements. I use statistical inference, information theory, and reinforcement learning to develop theoretical models which I test against behavioral and physiological data. I have worked with EEG, MEG, fMRI, and single neuron recordings. Prior to joining EPFL, I received my B.Sc. in Electrical Engineering from the Sharif University of Technology, Tehran. During the last two years of my study, I did a few research projects in the Augmented Intelligence Research Lab (AIR Lab) under the supervision of Prof. Hamid Aghajan. My projects were mainly on (i) studying biomarkers of surprise in EEG signals, (ii) decoding surprise using these biomarkers, and (iii) fMRI-based classification of visual and auditory stimuli. You can find my random scientific notes and educational articles here in my Medium account, and more information about me on my personal website.

Official source

https://people.epfl.ch/alireza.modirshanechi

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A taxonomy of surprise definitions

Johanni Michael Brea, Wulfram Gerstner, Alireza Modirshanechi

Surprising events trigger measurable brain activity and influence human behavior by affecting learning, memory, and decision-making. Currently there is, however, no consensus on the definition of surprise. Here we identify 18 mathematical definitions of surprise in a unifying framework. We first propose a technical classification of these definitions into three groups based on their dependence on an agent’s belief, show how they relate to each other, and prove under what conditions they are indistinguishable. Going beyond this technical analysis, we propose a taxonomy of surprise definitions and classify them into four conceptual categories based on the quantity they measure: (i) ‘prediction surprise’ measures a mismatch between a prediction and an observation; (ii) ‘change-point detection surprise’ measures the probability of a change in the environment; (iii) ‘confidence-corrected surprise’ explicitly accounts for the effect of confidence; and (iv) ‘information gain surprise’ measures the belief-update upon a new observation. The taxonomy poses the foundation for principled studies of the functional roles and physiological signatures of surprise in the brain.

2022

Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making

Johanni Michael Brea, Wulfram Gerstner, Marco Philipp Lehmann, Vasiliki Liakoni, Alireza Modirshanechi, Kerstin Preuschoff

Learning how to reach a reward over long series of actions is a remarkable capability of humans, and potentially guided by multiple parallel learning modules. Current brain imaging of learning modules is limited by (i) simple experimental paradigms, (ii) entanglement of brain signals of different learning modules, and (iii) a limited number of computational models considered as candidates for explaining behavior. Here, we address these three limitations and (i) introduce a complex sequential decision making task with surprising events that allows us to (ii) dissociate correlates of reward prediction errors from those of surprise in functional magnetic resonance imaging (fMRI); and (iii) we test behavior against a large repertoire of model-free, model-based, and hybrid reinforcement learning algorithms, including a novel surprise-modulated actor-critic algorithm. Surprise, derived from an approximate Bayesian approach for learning the world-model, is extracted in our algorithm from a state prediction error. Surprise is then used to modulate the learning rate of a model-free actor, which itself learns via the reward prediction error from model-free value estimation by the critic. We find that action choices are well explained by pure model-free policy gradient, but reaction times and neural data are not. We identify signatures of both model-free and surprise-based learning signals in blood oxygen level dependent (BOLD) responses, supporting the existence of multiple parallel learning modules in the brain. Our results extend previous fMRI findings to a multi-step setting and emphasize the role of policy gradient and surprise signalling in human learning.

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

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Fitting network models to neural activity is an important tool in neuroscience. A popular approach is to model a brain area with a probabilistic recurrent spiking network whose parameters maximize the likelihood of the recorded activity. Although this is widely used, we show that the resulting model does not produce realistic neural activity. To correct for this, we suggest to augment the log-likelihood with terms that measure the dissimilarity between simulated and recorded activity. This dissimilarity is defined via summary statistics commonly used in neuroscience and the optimization is efficient because it relies on back-propagation through the stochastically simulated spike trains. We analyze this method theoretically and show empirically that it generates more realistic activity statistics. We find that it improves upon other fitting algorithms for spiking network models like GLMs (Generalized Linear Models) which do not usually rely on back-propagation. This new fitting algorithm also enables the consideration of hidden neurons which is otherwise notoriously hard, and we show that it can be crucial when trying to infer the network connectivity from spike recordings.

2021