Brain-machine convergent evolution: Why finding parallels between brain and artificial systems is informative.

Central nervous system neurons manifest a rich diversity of selectivity profiles-whose precise role is still poorly understood. Following the striking success of artificial networks, a major debate has emerged concerning their usefulness in explaining neuronal properties. Here we propose that finding parallels between artificial and neuronal networks is informative precisely because these systems are so different from each other.

Face-Selective Units in Human Ventral Temporal Cortex Reactivate during Free Recall.

Research in functional neuroimaging has suggested that category-selective regions of visual cortex, including the ventral temporal cortex (VTC), can be reactivated endogenously through imagery and recall. Face representation in the monkey face-patch system has been well studied and is an attractive domain in which to explore these processes in humans. The VTCs of 8 human subjects (4 female) undergoing invasive monitoring for epilepsy surgery were implanted with microelectrodes.

The Noisy Brain: Power of Resting-State Fluctuations Predicts Individual Recognition Performance.

The unique profile of strong and weak cognitive traits characterizing each individual is of a fundamental significance, yet their neurophysiological underpinnings remain elusive. Here, we present intracranial electroencephalogram (iEEG) measurements in humans pointing to resting-state cortical "noise" as a possible neurophysiological trait that limits visual recognition capacity. We show that amplitudes of slow (<1 Hz) spontaneous fluctuations in high-frequency power measured during rest were predictive of the patients' performance in a visual recognition 1-back task (26 patients, total of 1,389 bipolar contacts pairs).

Convergent evolution of face spaces across human face-selective neuronal groups and deep convolutional networks.

The discovery that deep convolutional neural networks (DCNNs) achieve human performance in realistic tasks offers fresh opportunities for linking neuronal tuning properties to such tasks. Here we show that the face-space geometry, revealed through pair-wise activation similarities of face-selective neuronal groups recorded intracranially in 33 patients, significantly matches that of a DCNN having human-level face recognition capabilities. This convergent evolution of pattern similarities across biological and artificial networks highlights the significance of face-space geometry in face perception.

Where Does Time Go When You Blink?

Retinal input is frequently lost because of eye blinks, yet humans rarely notice these gaps in visual input. Although previous studies focused on the perceptual and neural correlates of diminished awareness to blinks, the impact of these correlates on the perceived time of concurrent events is unknown. Here, we investigated whether the subjective sense of time is altered by spontaneous blinks.

Quenching of spontaneous fluctuations by attention in human visual cortex.

In the absence of a task, the human brain enters a mode of slow spontaneous fluctuations. A fundamental, unresolved question is whether these fluctuations are ongoing and thus persist during task engagement, or alternatively, are quenched and replaced by task-related activations. Here, we examined this issue in the human visual cortex, using fMRI.

Covert neurofeedback without awareness shapes cortical network spontaneous connectivity.

Recent advances in blood oxygen level-dependent-functional MRI (BOLD-fMRI)-based neurofeedback reveal that participants can modulate neuronal properties. However, it is unknown whether such training effects can be introduced in the absence of participants' awareness that they are being trained. Here, we show unconscious neurofeedback training, which consequently produced changes in functional connectivity, introduced in participants who received positive and negative rewards that were covertly coupled to activity in two category-selective visual cortex regions.