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Category: neuroscience

Neuron ‘ground plans’ could simplify brain and behavior research

While E. Josie Clowney would never suggest that neuroscience is simple, a new study by her team at the University of Michigan could drastically reduce complexity in future studies. Their work focused on instinctual behaviors in fruit flies, but it has the potential to accelerate work to better understand the neurobiology that underlies behavior and decision-making in mammals, including humans.

The research establishes a new way to understand neurons, their connectivity and the behaviors they control. Within this new framework, the researchers can circumvent the conventional approach of considering each type of neuron individually and instead focus on groupings defined by shared structure and by two sets of regulatory genes. The work is published in the journal Nature.

While there are more than 8,000 kinds of neurons in the fruit fly cerebrum —the part of its brain where instinctual behaviors are hardwired—there are less than 200 major structural groups, or ground plans. Led by Najia Elkahlah, who recently defended her doctoral thesis in the Clowney lab, the team’s discoveries revealed how these ground plans get set up. There is a sort of order or hierarchy, where one set of genes coordinates the formation of the ground plan, and the other set produces small differences in shape and connectivity among neurons within each ground plan.

Scientists identify a cell type in the brain that was previously ignored and it may explain why human memory has no known upper limit

The human brain contains roughly 86 billion neurons. That number appears in almost every popular account of memory and intelligence, and it tends to carry an implicit argument: that the scale of human cognition follows from the scale of this cell count. What is less often mentioned is that the brain contains a roughly comparable number of a different cell type entirely, one that researchers have treated, for most of the history of neuroscience, as little more than biological scaffolding.

A paper published on 23 May in the Proceedings of the National Academy of Sciences puts forward a new hypothesis about what those cells, called astrocytes, might actually be doing. The work comes from a team at MIT: lead author Leo Kozachkov, Jean-Jacques Slotine, a professor of mechanical engineering and brain and cognitive sciences, and Dmitry Krotov of the MIT-IBM Watson AI Lab, who is the paper’s senior author. Their claim is not that astrocytes have been misunderstood in any dramatic sense; it is the more careful suggestion that they may be doing computational work that neurons, on their own, cannot account for.

This is a hypothesis supported by a mathematical model. The experimental work needed to test it has not yet been done.

Beyond the brain: Organs help shape the nervous systems that control them

A new Yale study reveals that major organ systems in the body aren’t just passive structures operating on directions from command central—the brain—but instead are active participants in controlling their own functions.

Writing in the journal Nature, a team of researchers led by Yale’s Rui Chang demonstrates how organs develop and maintain their own neural circuitry, which in turn communicates with the brain in a sort of two-way conversation.

The findings provide a new understanding of how the body and brain communicate via networks of neurons embedded inside organs that constitute a mini-nervous system, called “organ intrinsic nervous systems,” which help control critical functions such as digestion, heart rhythm, breathing, insulin secretion, and immune responses, the researchers say.

AI-Discovered Cognitive Models Reveal Novel Insights into Human and Animal Learning

The problem? Human brains (and animal brains, too) are incredibly complex. While these handcrafted models are great starting points, they often oversimplify things and miss the messy, rich reality of actual behavior. On the flip side, using powerful, flexible AI to analyze data can capture that richness, but AI usually gives us a “black box”—it finds patterns but can’t explain *why* or *how* it found them, leaving scientists to do the heavy lifting of figuring out the rules.

Scientific models are widely used across the natural sciences as an interface between scientific theories and empirical data [1]. Such models play a key role, for example, in the study of human and animal learning, where they express algorithmic hypotheses and relate them to psychology and neuroscience data [2, 3]. These models are traditionally handcrafted by expert researchers based on existing theory or new insights. Such handcrafted models, however, are now known to fall short of capturing the full richness of behavior, even in their narrow domains [4– 7]. An alternative data-driven approach has emerged, seeking to discover new insights by fitting and interpreting flexible models [8– 11]. However, these tools require substantial human effort to derive insight from data, and it has been unclear how to discover new ideas from data efficiently. Here, we present DataDIVER, a general approach for automatically discovering computational models from data, and demonstrate that these models surface novel mechanistic insights into human and animal learning. Our approach delivers models that take the form of short computer programs, which are optimized both to fit data well and to be simple. These programs explicitly connect with existing theoretical frameworks and are readily understandable by human scientists. They can also be used to make novel predictions, some of which we show are borne out in re-analysis of existing data. General-purpose tools for surfacing new ideas from data, especially in combination with the large datasets that are increasingly available in many fields, stand to dramatically accelerate scientific discovery.

The authors have declared no competing interest.

Precognition, Retrocausation, and the Unconscious with Eric Wargo

Eric Wargo, PhD, is author of Time Loops: Precognition, Retrocognition, and the Unconscious. He is an anthropologist and science writer. His blog is http://thenightshirt.com/.

Here he defines time loops as akin to self-fulfilling prophecies. He asserts that they could be the very basis of the creative process. He explains that retrocausation is to physics what precognition is to parapsychology. He explores the social and psychological dynamics associated with the notion of premonitions. He reviews the experiment in time of J. W. Dunne suggesting that dreams contain much information seemingly derived from the future. He applies Dunne’s methology to dreams of Sigmund Freud.

New Thinking Allowed host, Jeffrey Mishlove, PhD, is author of The Roots of Consciousness, Psi Development Systems, and The PK Man. Between 1986 and 2002 he hosted and co-produced the original Thinking Allowed public television series. He is the recipient of the only doctoral diploma in \.

Critical Thalamocortical Coordination Dynamics Track Conscious State Transitions

Abstract Despite substantial progress in identifying neural correlates of consciousness, no unified quantitative framework currently derives a formally specified order parameter for conscious-state organisation from established neurophysiological principles, or links thalamocortical coordination dynamics to measurable state transitions across pharmacological, pathological, and perturbational conditions through a single computational formalism. We propose a neurocomputational theoretical framework in which conscious states are associated with metastable regimes of large-scale thalamocortical coordination operating near critical dynamical boundaries. The framework is formalised through a dynamic coordination functional Φ(t), defined as a surface integral over the thalamocortical interface and directly operationalisable from high-density EEG as a weighted combination of gamma-band power spectral density, thalamocortical coherence, and theta-gamma phase-amplitude coupling. The thalamic reticular nucleus (TRN) is identified as the anatomical implementation of the control parameter governing proximity to the critical point, grounded in a Wilson-Cowan model of TRN inhibitory gating whose bifurcation structure is characterised computationally. Numerical simulation of the linearised field equation on the thalamocortical boundary demonstrates internal consistency: the simulated system produces power-law recovery dynamics tau_rec proportional to | θ — θ _c|^v with nu consistent with model A universality class [0.5, 1.5], and a Kuramoto mean-field derivation establishes that Φ(t) emerges as the natural order parameter of coupled thalamocortical oscillators rather than being postulated. The joint (|Φ(t)|, Var[|Φ(t)|]) phase space correctly separates simulated waking, anaesthetic, ictal, and minimally conscious regimes without parameter fitting to empirical data. All simulation code is publicly available. Six quantitatively specific, independently falsifiable predictions are derived across five experimental domains: power-law Gamma Dip scaling in near-threshold EEG with a specific exponent range; causal disruption of thalamocortical coherence by selective TRN silencing; opposite EEG scaling exponent deviations in ASD versus schizophrenia; systematic Φ_est collapse under propofol anaesthesia correlated with PCI; Φ_est as a real-time consciousness biomarker in disorders of consciousness; and clinical validity of Φ_est in disorders of consciousness and ictal state discrimination by the metastability index. Each prediction is stated with quantitative thresholds and a pre-specified falsification criterion. The framework provides: the first anatomically specified and formally derived order parameter for conscious-state organisation directly operationalisable from passive EEG; a mechanistically grounded identification of the TRN as the dynamical control parameter, testable by a single optogenetic experiment; and a computationally validated, pre-registerable programme of six falsifiable predictions defining a tractable empirical agenda. Φ_est would constitute a candidate real-time consciousness biomarker if the framework’s predictions are confirmed in purpose-designed experiments.

Reading and writing neural activity with Neuropixels Opto probes

High-density electrophysiology devices allow neuroscientists to observe spikes from large populations of neurons, and optogenetics allows them to drive or suppress those spikes. We show that a single device can combine these two capabilities, providing a high-resolution means to both read and write neural activity in the living brain.

Neuroproteasomes regulate endogenous tau paired helical filament formation in an APOE genotype- and age-dependent manner

A cellular explanation for how tau aggregates into fibrils in Alzheimer’s disease has been elusive. This paper identifies the failure of ‘neuroproteasomes’ as sufficient to convert tau into paired helical filaments, a process regulated by ApoE and aging.

NIH-funded study suggests that testosterone suppresses brain tumor growth in males

Findings may warrant exploration of the hormones as glioblastoma treatment.

In a new National Institutes of Health (NIH)-funded study, scientists at Cleveland Clinic discovered that hormones associated with male development may play a key role in limiting the growth of brain tumors in men. The research team found that the loss of androgen hormones, such as testosterone, in a preclinical model of glioblastoma drove tumor growth by inducing local inflammation and triggering the production of stress hormones. In an analysis of data from more than 1,300 men with glioblastoma, the authors found that supplemental testosterone was significantly associated with improved survival, which was consistent with their preclinical experiments.

“This outcome is a welcome surprise and may potentially offer a lead for new treatments for a kind of cancer that is deadlier in men,” said Anthony Letai, M.D., Ph.D., director of NIH’s National Cancer Institute (NCI).

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