Mindstate Design Labs, backed by Silicon Valley power players, has created what its CEO calls "the least psychedelic psychedelic that's psychoactive." While there's growing evidence that psychedelic drugs can effectively treat severe mental health conditions, especially in cases where traditional treatments have failed, they still come with downsides. Their hallucinogenic effects can be scary and overwhelming, with dosing sessions lasting several hours. Good treatment is heavily reliant on the individual's mindset going into a session and the environment in which they receive it. And though it's rare, psychedelics can sometimes worsen existing mental illness.

One of the brain's most important properties is its flexibility. Our cerebral circuitry changes constantly--every day, new links are made amongst the 86 billion individual neurons in our heads, and old connections are allowed to fall away. The result is a dizzyingly complicated network that is in a constant state of flux, rewiring itself on the fly in response to its environment and the life experience of its owner. The brain's ability to do this is called neuroplasticity, and it's what gives us the capacity to learn, grow, develop new skills and ideas, and adapt to the environment in which we live. We understand some aspects of neuroplasticity fairly well but others, including the reason that certain connections get made instead of others, remain deeply mysterious.

Artificial Intelligence (AI) has achieved new levels of performance and spread in public usage with the rise of deep neural networks (DNNs). Initially inspired by human neurons and their connections, NNs have become the foundation of AI models for many advanced architectures. However, some of the most integral processes in the human brain, particularly neurogenesis and neuroplasticity in addition to the more spread neuroapoptosis have largely been ignored in DNN architecture design. Instead, contemporary AI development predominantly focuses on constructing advanced frameworks, such as large language models, which retain a static structure of neural connections during training and inference. In this light, we explore how neurogenesis, neuroapoptosis, and neuroplasticity can inspire future AI advances. Specifically, we examine analogous activities in artificial NNs, introducing the concepts of ``dropin'' for neurogenesis and revisiting ``dropout'' and structural pruning for neuroapoptosis. We additionally suggest neuroplasticity combining the two for future large NNs in ``life-long learning'' settings following the biological inspiration. We conclude by advocating for greater research efforts in this interdisciplinary domain and identifying promising directions for future exploration.

Advances in model editing through neuron pruning hold promise for removing undesirable concepts from large language models. However, it remains unclear whether models have the capacity to reacquire pruned concepts after editing. To investigate this, we evaluate concept relearning in models by tracking concept saliency and similarity in pruned neurons during retraining. Our findings reveal that models can quickly regain performance post-pruning by relocating advanced concepts to earlier layers and reallocating pruned concepts to primed neurons with similar semantics. This demonstrates that models exhibit polysemantic capacities and can blend old and new concepts in individual neurons. While neuron pruning provides interpretability into model concepts, our results highlight the challenges of permanent concept removal for improved model \textit{safety}. Monitoring concept reemergence and developing techniques to mitigate relearning of unsafe concepts will be important directions for more robust model editing. Overall, our work strongly demonstrates the resilience and fluidity of concept representations in LLMs post concept removal.

Brain training is essential if you want do you live up to your full potential as a human being. Your brain is your most important organ, therefore it is essential that you train it for peak performance. Like it or not, every single day your brain is being trained. Unfortunately, it's being trained to be reactive, to shorten his attention span, and to give you hits of dopamine when new Facebook likes come in and text messages appear on your phone. Your brain is being shaped and conditioned by every single thing you read, watch, view, listen to and experience.

The human brain is capable of integrating intricate and diverse inputs from multiple sensory systems simultaneously in order to rapidly comprehend and assess the information for planning and execution of complex actions. These and other cognitive functions are performed by the vastly interconnected neural networks formed by the roughly 100 billion neurons of the brain. The precise patterns of connectivity among neurons within these networks determines their function, and through experience, these connectivity patterns change over time to enable acquisition of new skills. Indeed, one of the most impressive aspects of brain function is the ability to learn new cognitive skills, such as the ability to understand and speak a foreign language. During learning, connections between neurons change through a process known as synaptic plasticity, which plays a pivotal role in learning, While traditional learning brings about changes in neural networks through experience, synaptic plasticity can also be enhanced by activating neuromodulatory regions in the brain via peripheral neurostimulation.

Segments has become a guide to a weekly practice of engaging with social mediums while learning & sharing to nourish neuroplasticity. In efforts to keep my socials intentional, I've decided to add more structure and enjoyment to my corner. Four days of the week are dedicated to sharing in the context of the four segments introduced in my Segments: Grow Learn Build Play article. Grow Learn Build Play Build is a framework comprised of metrics to guide multi-applicable 4 week builds. It's a systematic way to integrate and implement while firing those neural networks.

Conspiracy theories, or in general seriously distorted beliefs, are widespread. How and why are they formed in the brain is still more a matter of speculation rather than science. In this paper one plausible mechanisms is investigated: rapid freezing of high neuroplasticity (RFHN). Emotional arousal increases neuroplasticity and leads to creation of new pathways spreading neural activation. Using the language of neurodynamics a meme is defined as quasi-stable associative memory attractor state. Depending on the temporal characteristics of the incoming information and the plasticity of the network, memory may self-organize creating memes with large attractor basins, linking many unrelated input patterns. Memes with fake rich associations distort relations between memory states. Simulations of various neural network models trained with competitive Hebbian learning (CHL) on stationary and non-stationary data lead to the same conclusion: short learning with high plasticity followed by rapid decrease of plasticity leads to memes with large attraction basins, distorting input pattern representations in associative memory. Such system-level models may be used to understand creation of distorted beliefs and formation of conspiracy memes, understood as strong attractor states of the neurodynamics.

We present a deep neural-network model for lifelong learning inspired by several forms of neuroplasticity. The neural network develops continuously in response to signals from the environment. In the beginning, the network is a blank slate with no nodes at all. It develops according to four rules: (i) expansion, which adds new nodes to memorize new input combinations; (ii) generalization, which adds new nodes that generalize from existing ones; (iii) forgetting, which removes nodes that are of relatively little use; and (iv) backpropagation, which fine-tunes the network parameters. We analyze the model from the perspective of accuracy, energy efficiency, and versatility and compare it to other network models, finding better performance in several cases.

The representation of memory in the brain is one of the unresolved questions in neuroscience. A key feature of learning and memory is the process of neuroplasticity--the ability of the brain to remodel structurally and functionally as a result of cognitive experience. Although the neurobiological basis of this process (that is, synaptic plasticity) is well established, the system level dynamics of neuroplasticity are still unclear. Recently, diffusion-weighted magnetic resonance imaging (DW-MRI), which can be carried out noninvasively in humans, provided a new approach to explore neuroplasticity. One of the parameters extracted from DW-MRI is mean diffusivity (MD) of water molecules, which is a biomarker of tissue microstructure (1).