A primary challenge in utilizing in-vitro biological neural networks for computations is finding good encoding and decoding schemes for inputting and decoding data to and from the networks. Furthermore, identifying the optimal parameter settings for a given combination of encoding and decoding schemes adds additional complexity to this challenge. In this study we explore stimulation timing as an encoding method, i.e. we encode information as the delay between stimulation pulses and identify the bounds and acuity of stimulation timings which produce linearly separable spike responses. We also examine the optimal readout parameters for a linear decoder in the form of epoch length, time bin size and epoch offset. Our results suggest that stimulation timings between 36 and 436ms may be optimal for encoding and that different combinations of readout parameters may be optimal at different parts of the evoked spike response.

Deep learning techniques can be used to first training unsupervised models on large amounts of unlabelled data, before fine-tuning the models on specific tasks. This approach has seen massive success for various kinds of data, e.g. images, language, audio, and holds the promise of improving performance in various downstream tasks (e.g. encoding or decoding brain data). However, there has been limited progress taking this approach for modelling brain signals, such as Magneto-/electroencephalography (M/EEG). Here we propose two classes of deep learning foundational models that can be trained using forecasting of unlabelled MEG. First, we consider a modified Wavenet; and second, we consider a modified Transformer-based (GPT2) model. The modified GPT2 includes a novel application of tokenisation and embedding methods, allowing a model developed initially for the discrete domain of language to be applied to continuous multichannel time series data. We also extend the forecasting framework to include condition labels as inputs, enabling better modelling (encoding) of task data. We compare the performance of these deep learning models with standard linear autoregressive (AR) modelling on MEG data. This shows that GPT2-based models provide better modelling capabilities than Wavenet and linear AR models, by better reproducing the temporal, spatial and spectral characteristics of real data and evoked activity in task data. We show how the GPT2 model scales well to multiple subjects, while adapting its model to each subject through subject embedding. Finally, we show how such a model can be useful in downstream decoding tasks through data simulation. All code is available on GitHub (https://github.com/ricsinaruto/MEG-transfer-decoding).

A major goal of cognitive neuroscience consists of identifying how the brain responds to distinct experimental conditions. While descriptive statistics and statistical tests are classically used to analyze neural data [1], this approach is not suited to predict how the brain should react to new conditions. The resulting models of the brain can thus be particularly challenging to compare. By contrast, predictive encoding models [2, 3] can be directly trained to predict brain responses to various experimental conditions, and compared on their ability to accurately predict novel conditions. For example, encoding models allow the estimation of integration constants in the brain [4, 5], the hierarchical organization of visual [6] and speech processing [7, 8]. Beyond MEG, predictive models have enabled automatic segmentation [9] and dynamical system identification [10, 11]. In functional Magnetic Resonance Imaging, predictive encoding models are starting to emulate complex neural processing [12] and are a step towards discovering new phenomena [13, 14]. Yet, this general objective of developing encoding models faces three major challenges when working with non-invasive and time-resolved signals collected by magneto-and electro-encephalography (M/EEG).

Both scenarios have some advantages which are, unfortunately, overweighed with severe limitations that hinder implementations of BCI technology in real-world tasks. Thus, in synchronous BCI paradigms, a wide variety of stimuli, including visual categories, can be utilized to explore and measure the evoked responses of a particular subject [1]. However, the whole set of stimuli has to be successively presented to the subject each time to determine his intention, which makes such approach inconvenient for the applications requiring fast, real-time control of an external device. Motor-imagery or other asynchronous BCIs do not require any external stimuli presentation, which allows a subject to produce voluntary mental commands at his own wish. At the same time, the ability of different subjects to perform various mental tasks is variable and depends on their personal physiological parameters and experience [2].

Both scenarios have some advantages which are, unfortunately, overweighed with severe limitations that hinder implementations of BCI technology in real-world tasks. Thus, in synchronous BCI paradigms, a wide variety of stimuli, including visual categories, can be utilized to explore and measure the evoked responses of a particular subject [1]. However, the whole set of stimuli has to be successively presented to the subject each time to determine his intention, which makes such approach inconvenient for the applications requiring fast, real-time control of an external device. Motor-imagery or other asynchronous BCIs do not require any external stimuli presentation, which allows a subject to produce voluntary mental commands at his own wish. At the same time, the ability of different subjects to perform various mental tasks is variable and depends on their personal physiological parameters and experience [2].

This paper presents a novel technique for analyzing electromagnetic imaging data obtained using the stimulus evoked experimental paradigm. The technique is based on a probabilistic graphical model, which describes the data in terms of underlying evoked and interference sources, and explicitly models the stimulus evoked paradigm.