Topic: information transmission

Computation in neural circuits relies on a common set of motifs, including divergence of common inputs to parallel pathways, convergence of multiple inputs to a single neuron, and nonlinearities that select some signals over others. Convergence and circuit nonlinearities, considered individually, can lead to a loss of information about the inputs. Past work has detailed how to optimize nonlinearities and circuit weights to maximize information, but we show that selective nonlinearities, acting together with divergent and convergent circuit structure, can improve information transmission over

In vivo, single neurons have high trial-to-trial variability in their information transmission, which presents a challenge for networks whose outputs must be consistent for high task performance. Reliability becomes increasingly difficult to attain for working memory tasks where information must be preserved for extended periods of time. This implies that relative to systems without noise, the brain must have mechanisms dedicated to compensating for unreliability in neuron activity. How trained networks mitigate the effects of noise while completing working memory tasks remains largely unexplo

Recent research in awake, behaving organisms revealed the strong modulatory effects of movement on sensory coding. Surprisingly, these effects are not consistent across species. For example, in rodents and insects locomotion increases the magnitude of visual responses [1-3], while in primates, locomotion has a weak suppressive influence [4]. These differences raise intriguing questions about the computational purpose of such modulations and the generality of the underlying principles of sensory processing. Here, we address these questions from a theoretical perspective. Our approach is groun

Brains are energy-hungry and evolution has driven them to work in an energy-efficient manner. In food restriction experiments, Padamsey et al. [1] have recently observed that individual neurons trade off energy consumption and information transmission. Specifically, neurons appear to reduce energy consumption by changing their integration properties through the adaptation of membrane resistance, resting potential, and synaptic efficacies, which comes at the cost of reduced information transmission. Furthermore, they found that individual neurons assume a broad range of values of these paramete

While sensory coding is traditionally thought of as a process independent of behavioral state, modern studies showcase potent modulatory effects of movement on sensory neurons. Intriguingly, these effects are not uniform and can differ between species. In insects and rodents, visual responses increase dramatically when the animal is moving, whereas in primates locomotion has a weak impact on responses in the primary visual cortex (V1). These differences raise questions about the existence and universality of computational principles underlying sensory coding during behavior. We take a normativ

The functional transmission of information between different synapses is crucial, and the potential for such transmission in a mixed-cultured neural network of chick and rat has been demonstrated. We analyzed the correlation between neural electrical activity bursts using clustering and neural Ca2+ signal peaks obtained from Ca2+ imaging method. This analysis showed that information transmission occurs between chick neurons and rat neurons in a mixed-cultured neuronal network of chick and rat.We also cultured glial cells from both chickens and rats, combined with a glial cell-derived condition