Information processing

Research in Taro Toyoizumi’s laboratory connects synaptic plasticity, information processing and adaptive neural circuits. This role leads computational work on sleep within a multiscale CREST BioDX collaboration with Kaoru Inokuchi and Tatsuya Haga. Analytical approaches draw on statistical physics and information theory, alongside mathematical models of learning and memory. The postdoctoral category is for researchers within five years of their doctorate; the research-scientist category is for more experienced doctorate holders. The appointment is at RIKEN’s Wako campus, with annual renewal

Sean Carroll talks with Caltech researcher Erik Winfree about programming DNA to process information and build microscopic machines. They discuss molecular computation, self-assembly, biological information processing and the prospects for DNA-based robots in biology and medicine.

…coupled ant–leaf dynamics can support information processing beyond individual cognition, providing an experimental model for the capabilities and limits of collective problem solving.

…difference by mimicking the brain’s information coding via discrete voltage spikes. However, it remains unclear how both artificial and natural networks of spiking neurons can learn energy-efficient information processing strategies. Here we propose Predictive Coding Light (PCL), a recurrent hierarchical spiking neural network for unsupervised representation learning. In contrast to previous predictive coding approaches, PCL does not transmit prediction errors to higher

…He discusses information processing, dynamic range, measurement methods, experimental evidence, criticisms and the question of whether criticality is a distinctive computational property of brains or part of a broader natural phenomenon.

…its influence on neural dynamics and information processing remains incomplete. To address this gap, we introduced a model of randomly connected neural populations (modules) and studied its dynamics by means of the mean-field theory and simulations. Our analysis uncovered a rich phase diagram: High-dimensional chaotic activity (microscopic chaos), common in homogeneous networks, is separated from the low-dimensional chaotic activity of strongly coherent modules (macroscop

Over the past thirty years or so, cognitive neuroscience has made spectacular progress understanding the biological mechanisms of consciousness. Consciousness science, as this field is now sometimes called, was not only inexistent thirty years ago, but its very name seemed like an oxymoron: how can there be a science of consciousness? And yet, despite this scepticism, we are now equipped with a rich set of sophisticated behavioural paradigms, with an impressive array of techniques making it possible to see the brain in action, and with an ever-growing collection of theories and speculations ab

Behavioural unresponsiveness during sleep does not imply that information processing stops. This seminar examines which responses to outside stimuli and internal memories persist, which are lost, and how the remaining cognitive abilities affect sleep quality and its functions.

…results in impairments of hippocampal information processing and storage (doi: 10.3389/fnbeh.2015.00103).To what extent this also affec

Investigating the contribution of various neuronal circuits to our information processing, learning, and actions is often limited by the difficulty of providing precise and scalable complex experiences with detailed behavior measurements.Despite recent advances, existing behavior platforms are typically optimized for few, often simple tasks with constrained extendability, and priced prohibitively for scale, while behavior tracking requires work intensive labeling to generate situation-specific training data.Here, we present work on two novel open and flexible tools to enable easier pursuit of

The cerebral cortex is known to follow a hierarchical organization, which supports information processing and ultimately determines behavior. The hierarchy has been observed in brain connectivity and cytoarchitecture, as well as the timescale of spiking activity. However, whether further dynamical features of cortical activity follow a hierarchical organization remains an open question.To address this question, we measured single-neuron statistics from the resting-state activity of macaque neocortex (N=6) from several cortical areas (V1, V4, DP, 7A, M1, PMd, vlPFC). We found some non-systemati

Theoretical studies show that, operating at criticality maximises both the information processing and storage capabilities of neural systems. Experimental indications that the brain might operate in this way come in the form of: (1) power law distributions of avalanches and (2) non-Gaussian fixed points that appear in phenomenological renormalization group analysis. Despite theoretical benefits and experimental findings, it remains highly debated whether neural criticality is functionally relevant. Firstly, it is hard to confirm if avalanches follow true power laws. Secondly, recent studies in

In theoretical neuroscience, the brain is usually described as an information processing system that encodes and manipulates representations of knowledge to produce plans of action. This view leads to a decomposition of brain functions into putative processes such as object recognition, working memory, decision-making, action planning, etc., inspiring the search for the neural correlates of these processes. However, neurophysiological data do not support many of the predictions of these classic subdivisions. Instead, there is divergence and broad distribution of functions that should be unifie

We are now clearly entering a new age in our relationship with machines. The power of AI natural language processors and image generators has rapidly exceeded the expectations of even those who developed them. Serious questions are now being asked about the extent to which machines could become — or perhaps already are — sentient or conscious. Do AI machines understand the instructions they are given and the answers they provide? In this talk I will consider the prospects for conscious machines, by which I mean machines that have feelings, know about their own existence, and about ours. I will

…is required to ensure proper information processing in the brain. Many brain disorders, especially neurodevelopental disorders, are rooted in a specific disturbance of this coordination. In my research group we use a combination of two-photon microscopy and electrophisiology to examine how inhibitory synapses are fromed and how this formation is coordinated with nearby excitatroy synapses.

…to operate near a critical point where information processing is optimal. At this critical point, incoming activity is neither amplified (supercritical) nor damped (subcritical), but approximately preserved as it passes through neural networks. Departures from the critical point have been associated with conditions of poor neurologica

…implementing its required information processing. To observe the effect of these processes, we introduce the spatially-embedded recurrent neural network (seRNN). seRNNs learn basic task-related inferences while existing within a 3D Euclidean space, where the communication of constituent neurons is constrained by a sparse connectome. We find that seRNNs, similar to primate cerebral corti

…contribute to modulate hippocampal information processing and cognition. In this talk, I will present some recent findings revealing how enhanced forms of plasticity in adult-born neurons underlie the way they become incorporated into pre-existing networks in response to experience.

Neuronal network computation and computation by avalanche supporting networks are of interest to the fields of physics, computer science (computation theory as well as statistical or machine learning) and neuroscience. Here we show that computation of complex Boolean functions arises spontaneously in threshold networks as a function of connectivity and antagonism (inhibition), computed by logic automata (motifs) in the form of computational cascades. We explain the emergent inverse relationship between the computational complexity of the motifs and their rank-ordering by function probabilities

…circuitry’s interaction with cortical information streams and the circuitry’s inner communication. With ultra-high field functional imaging we investigated the functional architecture of the human entorhinal-hippocampal circuitry. We identified an organization that is consistent with convergence of information in anterior and lateral entorhinal subregions and the subiculum/CA1 border while keeping a second route specific for scene processing in a p

…fundamental units of communication and information processing in the brain. The accurate wiring of neurons through synapses into neural networks or circuits is essential for brain organization. Neuronal networks are sculpted and refined throughout life by constant adjustment of the strength of synaptic communication by neuronal activity, a process known as synaptic plasticity. Deficits in the development or plasticity of synapses underlie various neuropsychiatric disorders, including autism, schizophrenia and intellectual disability. The

…would confer optimal capabilities for information processing and computation. However, how this c

…to uncover basic principles of information processing in the cerebral cortex, with a focus on how sensory learning can selectively alter synaptic strength. I will discuss recent findings that differentiate reinforcement learning from sensory experience, showing rapid and selective plasticity of thalamic and inhibitory synapses within primary sensory cortex.

…But what is its role in neural information processing? We showed analytically how homeostasis changes collective dynamics and consequently information flow - depending on the input to the network. We then studied how input and homeostasis on a recurrent network of LIF neurons impacts information flow and task performance. We showed how we can tune the working point of the network, and found that, contrary to previous assumptions, there is not one optimal working point for a family of tasks, but each task may requir