A taxonomy of surprise definitions

Modirshanechi, Alireza; Brea, Johanni; Gerstner, Wulfram

doi:10.1016/j.jmp.2022.102712

Cited by 36 publications

(32 citation statements)

References 88 publications

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“…An event is surprising if it does not match our expectations 1,2,3,4 . The unexpected punchline of a joke 3 , the unexpected continuation of a sequence of tones 5 , harmonies 6,7 or images 8,9,10 , as well as rule switching such as shift of escape platform in the Morris watermaze 11 or meaning of cues 12,13,14,15 induce measurable physiological and behavioral reactions in humans and animals.…”

Section: Introductionmentioning

confidence: 60%

“…Definitions of surprise in a probabilistic framework 4 have previously been used to explain adaptation to rule switching 42,43,44,45 . However, these definitions cannot be directly applied to spiking neural networks since a correct normalization of probability distributions is difficult to maintain within spiking networks 131,132 and the calculation of a distance, or Kullback-Leiber divergence, between two probability distributions 4,133 is even harder. Surprise-driven neural networks for adaptative decision making 134 or neural particle filters for adaptive perception 135 are not easily extendable to networks of spiking neurons.…”

Section: Discussionmentioning

confidence: 99%

“…Surprise-driven neural networks for adaptative decision making 134 or neural particle filters for adaptive perception 135 are not easily extendable to networks of spiking neurons. Our approach extracts from the activity of spiking neurons a qualitative surprise signal that can be interpreted as a measure of observation-mismatch surprise 4 without a direct link to probability distributions.…”

Section: Discussionmentioning

confidence: 99%

“…In summary, surprise, i.e., a response of the brain to a stimulus that occurs against expectations 1,2,3,4 , is a phenomenon of a relevance that is comparable to that of reward. Similar to reward and reward expectations 88 , surprise must be detected by neuronal networks in the brain and transformed into modulatory signals that influence synaptic plasticity.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast to mathematical studies that start from a normative framework of surprise 36,37,38,39,40,41,42,43,44,45,46,4 , we take a constructive approach based on a network of spiking model neurons with plastic connections. We consider two aspects of spiking neural networks as crucial requirements for biological plausibility.…”

Section: Introductionmentioning

confidence: 99%

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Fast Adaptation to Rule Switching using Neuronal Surprise

Barry

Gerstner

2022

Preprint

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In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signal is extracted from an increase in neural activity after an imbalance of excitation and inhibition. The surprise signal modulates synaptic plasticity via a three-factor learning rule which increases plasticity at moments of surprise. The surprise signal remains small when transitions between sensory events follow a previously learned rule but increases immediately after rule switching. In our model, previously learned rules are protected against overwriting when learning a new rule. Our surprise-modulated spiking network model makes a step towards solving the stability-plasticity dilemma in neuroscience and the problem of continual learning in machine learning.

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Section: Introductionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast Adaptation to Rule Switching using Neuronal Surprise

Barry

Gerstner

2022

Preprint

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EEG mismatch responses in a multimodal roving stimulus paradigm provide evidence for probabilistic inference across audition, somatosensation, and vision

Grundei

Schröder

Gijsen

et al. 2023

Human Brain Mapping

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The human brain is constantly subjected to a multimodal stream of probabilistic sensory inputs. Electroencephalography (EEG) signatures, such as the mismatch negativity (MMN) and the P3, can give valuable insight into neuronal probabilistic inference. Although reported for different modalities, mismatch responses have largely been studied in isolation, with a strong focus on the auditory MMN. To investigate the extent to which early and late mismatch responses across modalities represent comparable signatures of uni‐ and cross‐modal probabilistic inference in the hierarchically structured cortex, we recorded EEG from 32 participants undergoing a novel tri‐modal roving stimulus paradigm. The employed sequences consisted of high and low intensity stimuli in the auditory, somatosensory and visual modalities and were governed by unimodal transition probabilities and cross‐modal conditional dependencies. We found modality specific signatures of MMN (~100–200 ms) in all three modalities, which were source localized to the respective sensory cortices and shared right lateralized prefrontal sources. Additionally, we identified a cross‐modal signature of mismatch processing in the P3a time range (~300–350 ms), for which a common network with frontal dominance was found. Across modalities, the mismatch responses showed highly comparable parametric effects of stimulus train length, which were driven by standard and deviant response modulations in opposite directions. Strikingly, P3a responses across modalities were increased for mispredicted stimuli with low cross‐modal conditional probability, suggesting sensitivity to multimodal (global) predictive sequence properties. Finally, model comparisons indicated that the observed single trial dynamics were best captured by Bayesian learning models tracking unimodal stimulus transitions as well as cross‐modal conditional dependencies.

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Information and Entropy

Souza de Cursi

2024

International Series in Operations Research &Amp; Management Science

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A taxonomy of surprise definitions

Cited by 36 publications

References 88 publications

Fast Adaptation to Rule Switching using Neuronal Surprise

Fast Adaptation to Rule Switching using Neuronal Surprise

EEG mismatch responses in a multimodal roving stimulus paradigm provide evidence for probabilistic inference across audition, somatosensation, and vision

Information and Entropy

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