Physiological Mechanisms Underlying Motion-Induced Blindness

Visual disappearance illusions - such as motion-induced blindness (MIB) - are commonly used to study the neural underpinnings of visual perception. In such illusions a salient visual target becomes perceptually invisible. Previous studies are inconsistent regarding the role of primary visual cortex (V1) in these illusions. Here we provide physiological and psychophysical evidence supporting a role for V1 in generating MIB

Separate and overlapping brain areas encode subjective value during delay and effort discounting

AbstractMaking decisions about rewards that involve delay or effort requires the integration of value and cost information. The brain areas recruited in this integration have been well characterized for delay discounting. However only a few studies have investigated how effort costs are integrated into value signals to eventually determine choice. In contrast to previous studies that have evaluated fMRI signals related to physical effort, we used a task that focused on cognitive effort. Participants discounted the value of delayed and effortful rewards. The value of cognitively effortful rewards was represented in the anterior portion of the inferior frontal gyrus and dorsolateral prefrontal cortex. Additionally, the value of the chosen option was encoded in the anterior cingulate cortex, caudate, and cerebellum. While most brain regions showed no significant dissociation between effort discounting and delay discounting, the ACC was significantly more activated in effort compared to delay discounting tasks. Finally, overlapping regions within the right orbitofrontal cortex and lateral temporal and parietal cortices encoded the value of the chosen option during both delay and effort discounting tasks. These results indicate that encoding of rewards discounted by cognitive effort and delay involves partially dissociable brain areas, but a common representation of chosen value is present in the orbitofrontal, temporal and parietal cortices

THE NEURAL REPLAY THOUGHT EXPERIMENT

This paper is directed at scientists interested in the relationship between the physical and the mental. My goal is to provide an accessible platform to expose and analyze the readers’ (often implicit) assumptions about the relationship between physical and mental phenomena. To this end, I developed an extension of the “neural replay thought experiment

Discovering Low-Dimensional Causal Pathways between Multiple Interacting Neuronal Populations

Understanding the nature of neural activity and computations in the brain will help us build better decision-making models to facilitate human-AI collaboration. Recording the neural activity of multiple and large neural populations in the brain is becoming widely available with modern recording techniques. It still remains a challenge, however, to understand how distinct and anatomically different neural populations interact with each other to control behaviour. We propose a new method to discover causal interactions between neural populations based on recurrent switching dynamical systems. We introduce an extended dynamics model that incorporates the current time-step when calculating the latent state variables. We also introduce an acyclicity constraint in learning the parameters of the model. These mechanisms enable rich causal interactions between neural populations to be identified from the learned model. Our model outperforms previous work on discovering interactions between neural populations in simulated datasets, without sacrificing the prediction performance of firing rates. We also apply our method on real neural recordings from two Macaque monkey brains performing a behavioral task, and show that the proposed method is able to detect causal interactions between brain regions related to the different time windows of the task

Spike-Weighted Spiking Neural Network with Spiking Long Short-Term Memory: A Biomimetic Approach to Decoding Brain Signals

Peer reviewed: TruePublication status: PublishedFunder: Institute for Infocomm Research (I2R)Funder: Agency for Science, Technology and Research (A*STAR), SingaporeFunder: Singapore International Pre-Graduate Award (SIPGA), by A*STAR Graduate Academy (A*GA)Background. Brain–machine interfaces (BMIs) offer users the ability to directly communicate with digital devices through neural signals decoded with machine learning (ML)-based algorithms. Spiking Neural Networks (SNNs) are a type of Artificial Neural Network (ANN) that operate on neural spikes instead of continuous scalar outputs. Compared to traditional ANNs, SNNs perform fewer computations, use less memory, and mimic biological neurons better. However, SNNs only retain information for short durations, limiting their ability to capture long-term dependencies in time-variant data. Here, we propose a novel spike-weighted SNN with spiking long short-term memory (swSNN-SLSTM) for a regression problem. Spike-weighting captures neuronal firing rate instead of membrane potential, and the SLSTM layer captures long-term dependencies. Methods. We compared the performance of various ML algorithms during decoding directional movements, using a dataset of microelectrode recordings from a macaque during a directional joystick task, and also an open-source dataset. We thus quantified how swSNN-SLSTM performed compared to existing ML models: an unscented Kalman filter, LSTM-based ANN, and membrane-based SNN techniques. Result. The proposed swSNN-SLSTM outperforms both the unscented Kalman filter, the LSTM-based ANN, and the membrane based SNN technique. This shows that incorporating SLSTM can better capture long-term dependencies within neural data. Also, our proposed swSNN-SLSTM algorithm shows promise in reducing power consumption and lowering heat dissipation in implanted BMIs.</jats:p