Biology subjects

Hosseini, E. A.

Publications and source records attributed to Hosseini, E. A..

5 recordsLinked to original sources

bioRxiv2024

Universality of representation in biological and artificial neural networks

Many artificial neural networks (ANNs) trained with ecologically plausible objectives on naturalistic data align with behavior and neural representations in biological systems. Here, we show that this alignment is a consequence of convergence onto the same representations by high-performing ANNs and by brains. We developed a method to identify stimuli that systematically vary the degree of inter-model representation agreement. Across language and vision, we then showed that stimuli from high-and low-agreement sets predictably modulated model-to-brain alignment. We also examined which stimulus features distinguish high-from low-agreement sentences and images. Our results establish representation universality as a core component in the model-to-brain alignment and provide a new approach for using ANNs to uncover the structure of biological representations and computations.

neuroscience↗

bioRxiv2023

Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language

Predicting upcoming events is critical to our ability to effectively interact with our environment and conspecifics. In natural language processing, transformer models, which are trained on next-word prediction, appear to construct a general-purpose representation of language that can support diverse downstream tasks. However, we still lack an understanding of how a predictive objective shapes such representations. Inspired by recent work in vision neuroscience Henaff et al. (2019), here we test a hypothesis about predictive representations of autoregressive transformer models. In particular, we test whether the neural trajectory of a sequence of words in a sentence becomes progressively more straight as it passes through the layers of the network. The key insight behind this hypothesis is that straighter trajectories should facilitate prediction via linear extrapolation. We quantify straightness using a 1-dimensional curvature metric, and present four findings in support of the trajectory straightening hypothesis: i) In trained models, the curvature progressively decreases from the first to the middle layers of the network. ii) Models that perform better on the next-word prediction objective, including larger models and models trained on larger datasets, exhibit greater decreases in curvature, suggesting that this improved ability to straighten sentence neural trajectories may be the underlying driver of better language modeling performance. iii) Given the same linguistic context, the sequences that are generated by the model have lower curvature than the ground truth (the actual continuations observed in a language corpus), suggesting that the model favors straighter trajectories for making predictions. iv) A consistent relationship holds between the average curvature and the average surprisal of sentences in the middle layers of models, such that sentences with straighter neural trajectories also have lower surprisal. Importantly, untrained models dont exhibit these behaviors. In tandem, these results support the trajectory straightening hypothesis and provide a possible mechanism for how the geometry of the internal representations of autoregressive models supports next word prediction.

neuroscience↗

bioRxiv2022

Intracranial recordings reveal three distinct neural response patterns in the language network

Despite long knowing what brain areas support language comprehension, our knowledge of the neural computations that these frontal and temporal regions implement remains limited. One important unresolved question concerns functional differences among the neural populations that comprise the language network. Leveraging the high spatiotemporal resolution of intracranial recordings, we examined responses to sentences and linguistically degraded conditions and discovered three response profiles that differ in their temporal dynamics. These profiles appear to reflect different temporal receptive windows (TRWs), with average TRWs of about 1, 4, and 6 words, as estimated with a simple one-parameter model. Neural populations exhibiting these profiles are interleaved across the language network, which suggests that all language regions have direct access to distinct, multi-scale representations of linguistic input--a property that may be critical for the efficiency and robustness of language processing.

neuroscience↗

bioRxiv2022

Artificial neural network language models align neurally and behaviorally with humans even after a developmentally realistic amount of training

Artificial neural networks have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans during language learning. Here, we use two complementary approaches to ask how the models ability to capture human fMRI responses to sentences is affected by the amount of training data. First, we evaluate GPT-2 models trained on 1 million, 10 million, 100 million, or 1 billion words against an fMRI benchmark. We consider the 100-million-word model to be developmentally plausible in terms of the amount of training data given that this amount is similar to what children are estimated to be exposed to during the first 10 years of life. Second, we test the performance of a GPT-2 model trained on a 9-billion-token dataset to reach state-of-the-art next-word prediction performance on the human benchmark at different stages during training. Across both approaches, we find that (i) the models trained on a developmentally plausible amount of data already achieve near-maximal performance in capturing fMRI responses to sentences. Further, (ii) lower perplexity--a measure of next-word prediction performance--is associated with stronger alignment with human data, suggesting that models that have received enough training to achieve sufficiently high next-word prediction performance also acquire representations of sentences that are predictive of human fMRI responses. In tandem, these findings establish that although some training is necessary for the models predictive ability, a developmentally realistic amount of training ([~]100 million words) may suffice.

neuroscience↗

bioRxiv2020

Artificial Neural Networks Accurately Predict Language Processing in the Brain

The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models. By revealing trends across models, this approach yields novel insights into cognitive and neural mechanisms in the target domain. We here present a first systematic study taking this approach to higher-level cognition: human language processing, our species signature cognitive skill. We find that the most powerful transformer models predict nearly 100% of explainable variance in neural responses to sentences and generalize across different datasets and imaging modalities (fMRI, ECoG). Models neural fits ( brain score) and fits to behavioral responses are both strongly correlated with model accuracy on the next-word prediction task (but not other language tasks). Model architecture appears to substantially contribute to neural fit. These results provide computationally explicit evidence that predictive processing fundamentally shapes the language comprehension mechanisms in the human brain. SignificanceLanguage is a quintessentially human ability. Research has long probed the functional architecture of language processing in the mind and brain using diverse brain imaging, behavioral, and computational modeling approaches. However, adequate neurally mechanistic accounts of how meaning might be extracted from language are sorely lacking. Here, we report an important first step toward addressing this gap by connecting recent artificial neural networks from machine learning to human recordings during language processing. We find that the most powerful models predict neural and behavioral responses across different datasets up to noise levels. Models that perform better at predicting the next word in a sequence also better predict brain measurements - providing computationally explicit evidence that predictive processing fundamentally shapes the language comprehension mechanisms in the human brain.

neuroscience↗