An information-theoretic quantification of the content of communication between brain regions.

Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function. Traditional methods to analyze brain activity based on the Wiener-Granger causality principle quantify the overall information propagated by neural activity between simultaneously recorded brain regions, but do not reveal the information flow about specific features of interest (such as sensory stimuli). Here, we develop a new information theoretic measure termed Feature-specific Information Transfer (FIT), quantifying how much information about a specific feature flows between two regions.

Learning to focus on number.

With age and education, children become increasingly accurate in processing numerosity. This developmental trend is often interpreted as a progressive refinement of the mental representation of number. Here we provide empirical and theoretical support for an alternative possibility, the filtering hypothesis, which proposes that development primarily affects the ability to focus on the relevant dimension of number and to avoid interference from irrelevant but often co-varying quantitative dimensions.

State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats.

Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices.

From Shortage to Surge: A Developmental Switch in Hippocampal-Prefrontal Coupling in a Gene-Environment Model of Neuropsychiatric Disorders.

Cognitive deficits represent a major burden of neuropsychiatric disorders and result in part from abnormal communication within hippocampal-prefrontal circuits. While it has been hypothesized that this network dysfunction arises during development, long before the first clinical symptoms, experimental evidence is still missing. Here, we show that pre-juvenile mice mimicking genetic and environmental risk factors of disease (dual-hit GE mice) have poorer recognition memory that correlates with augmented coupling by synchrony and stronger directed interactions between prefrontal cortex and hippocampus.

Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons.

Thalamic and Entorhinal Network Activity Differently Modulates the Functional Development of Prefrontal-Hippocampal Interactions.

Unlabelled: Precise information flow during mnemonic and executive tasks requires the coactivation of adult prefrontal and hippocampal networks in oscillatory rhythms. This interplay emerges early in life, most likely as an anticipatory template of later cognitive performance. At neonatal age, hippocampal theta bursts drive the generation of prefrontal theta-gamma oscillations.

Neonatal hypoxia-ischemia impairs juvenile recognition memory by disrupting the maturation of prefrontal-hippocampal networks.

High-prevalence/low-severity cognitive deficits represent the life-long burden of a perinatal hypoxic–ischemic (HI) insult. They have been proposed to result from dysmaturation of prelimbic-hippocampal networks, which account for mnemonic and executive performance. Already at neonatal age the communication within these networks is largely reduced after an early HI insult with mild/moderate structural outcome.