Metabolic cost

A large part of mammalian brains is dedicated to sensory processing. Sensory receptors extract distinct but partly correlated features of the environment that vary at different timescales, and then transmit them through subcortical structures and a succession of recurrently connected cortical areas. A general theory on how sensory pathways encode, propagate, and transform sensory features is still missing. To understand how biophysically realistic neural networks support these fundamental brain functions, we developed an analytical framework based on the principle of efficient coding (Barlow,

Sensory systems continuously adapt their responses based on the frequency of stimuli they encounter. Our goal is to develop a clear understanding of how populations of cortical neurons adapt to varying sensory environments. Recent experiments in the mouse primary visual cortex (V1) have shown a remarkable finding: the magnitude of population responses follows a power law with respect to stimulus frequency. This power law is universal, with the same exponent observed across different statistical environments, enabling predictions of population responses to new environments and allowing the brai

Hippocampal place cell populations putatively encode allocentric spatial position [1-4]. In bats and rodents, place cells exhibit heterogeneously sized and overlapping receptive (or place) fields [5-9]. These heterogeneous place fields instantiate a multiscale spatial tuning landscape in which the larger place fields of some place cells encompass the relatively more numerous narrowly tuned place fields of other cells (Fig 1a). In this work, we investigate the effectiveness of such multiscale codes for encoding allocentric spatial position. We do so by deriving closed-form expressions for how w

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing

Neurons are typically sensitive to a small fraction of possible stimuli in sensory space. If the environment changes, making certain stimuli more prevalent, neurons sensitive to those stimuli respond more often and therefore have a higher average firing rate. Prolonged exposure to such an environment often causes such neurons to adapt by responding less vigorously. If adaptation consistently returns the average firing rate to its value prior to environmental shift, it is termed firing-rate homeostasis. We present a normative explanation of firing-rate homeostasis grounded in the Infomax princi