Silicon Valley Global News’ Post

A new book out today "Bridging Molecular Mechanisms and Neural Oscillatory Dynamics" It introduces "Self Aware Networks: Theory of Mind" a new framework for understanding how consciousness arises from neural activity. https://lnkd.in/gyf-Gua4

At the core of the brain's functionality are neurons, the fundamental conduits transmitting signals. Around 70 million years ago, the evolution of these neural cells marked a pivotal juncture in intelligence's development. Utilizing proteins to convert chemical information into electrical signals, neurons established the groundwork for pulse-based communication, vital for information processing. The intricate neural network, defined by varying synaptic strengths, forms the bedrock of intelligence's complexity, spanning abstract thought, language, memory, and problem-solving. https://lnkd.in/gQDrjr2j

In simple words, “neurons that fire together, wire together.” Our brain consists of 100 billion neurons. When we repeatedly think a thought, we develop neural networks that allow neurons to connect and in turn form a neural pathway. Think about the first time you learned how to ride a bike. You struggled and had to try repeatedly to coordinate with your hands and legs. But over time, with consistent practice and repetition, this became second nature to you. Now, you can ride a bike without having to think about it twice. Hebb’s law is effective in explaining how learning and memory work. Every time that you learn something new, your brain changes. Similarly, when you think the same thoughts repeatedly, you experience the same outcomes in your life. You need to take inventory of which thoughts you are consistently thinking because they have a direct impact on your actions, feelings and outcomes. You want to fire and wire the neurons that are beneficial for you rather than the ones that aren’t.

Regarding neural implants John Nosta offers this thought experiment: "...while the multi-tasking of human thought and activity is commonplace, this new capability may suggest a remarkable expansion in our cognitive capacities, potentially heralding a new forefront in human-computer symbiosis and a technological push on the complexity of human capabilities." - This novel and speculative concept describes the ability to consciously manage and operate multiple streams of thought or tasks simultaneously, facilitated by advanced neural implants. - It suggests a potential reconfiguration of cognitive architecture, where the brain, augmented by technological interfaces, can engage with and process multiple streams of information simultaneously, akin to running several complex software applications on a computer without compromising the performance of each.

Let's imagine that you are Theseus, who has made it to the center of the labyrinth, but found no minotaur. Instead, you are confronted by a familiar-looking dragon. "Greetings Hero, you have reached the wrong myth, this is not a labyrinth. I am Pytho, who guards the oracle, here at Delphi. The oracle tells me that your thread snapped along the way, so Ariadna cannot pull you out of here. You run great risk in these chambers and must leave this dream soon, before it destabilizes. We have engineered an exit path for you: follow the yellow gradient descent road and turn left at each intersection." What is not a dream is that artificial neural networks are becoming key components in MS-proteomics. Have a look at my latest blogpost.

Ever wondered what causes the brain to be fooled by the tilt illusion? Our new study gives a comprehensive answer based on an information theoretic analysis of simultaneously recorded neural (fMRI) and behavioral data of human participants while they experience the illusion. Turns out the brain dynamically increases the encoding precision for orientations that are similar to the surround such that small orientation differences between center and surround are amplified. The effect is a bit like that of a magnifying glass ...

Excited to share that our recent paper, "A Computational Framework for Time's Subjective Expansion in Temporal Oddball Paradigm Using Recurrent Neural Network," https://lnkd.in/etQfWDJK. Our research explores the phenomenon of Time's Subjective Expansion (TSE) through the lens of behavioral psychology, with a focus on the temporal oddball paradigm. We used recurrent neural network model with a winner-take-all (WTA) decision process, we demonstrated a link between neural computation and TSE. We have explored the predictions from the model using both visual and auditory modalities. #Cognition #NeuralNetworks #BehavioralPsychology #ICACCS2024 #Innovation #TimePerception #SRUniversity Ravichander Janapati

In the grand tapestry of human endeavor, the synergy between neuroscience and artificial intelligence shines brightly, offering a glimpse into the boundless potential of the mind and computation. As we navigate this intersection, we are both humbled and invigorated by the journey ahead, delving into the essence of cognition and unraveling mysteries since the dawn of consciousness. The intricate dance of biological and artificial neural networks holds the key to creating intelligent machines that rival and even surpass human capabilities. With each discovery, we edge closer to this ambitious goal, driven by curiosity and relentless determination to redefine what is achievable. The story of neuroscience and AI is a testament to human resilience, ingenuity, and the insatiable thirst for knowledge that propels us forward. Let us embrace this adventure with open hearts and minds, understanding that our innovations will shape the future of our species and the universe itself. In this symphony of science, engineering, biology, and technology, we are the conductors and composers, the dreamers and builders of a new era in intelligence. Let us rise to the challenge with courage and conviction, knowing that our efforts today will bear fruit for generations to come.

'big data is not knowledge' Yep. As I spoke recently on AI at the The Association for Business Psychology I referenced my infographic from back in 2010 where I said, "Artificial Intelligence is just that, an Intelligence, not Wisdom. Data is not Information, Information is not Knowledge, Knowledge is not Intelligence, Intelligence is not Wisdom, Wisdom without Power to apply it is just Data. Business Psychology must work across all these domains with the right mindsets and techniques, bringing in AI and other technologies to scale and refine those practices effectively and efficiently within boundaries of risk . Done wrong, psychological safety will be undermined, and that will bring the reputation of Business Psychology into disrepute. Done right whole new mindsets to empower people will arise."

Neural circuits #neuralnetworks are indeed complex entities that are difficult to predict and comprehend, even for experienced and dedicated neuroscientists. This complexity arises from the intricate interplay of numerous components and processes, including synaptic interactions, ionic conductances, and internal cellular mechanisms, all of which can exhibit highly dynamic and non-linear behavior. To understand how a neural circuit works, one must first comprehend how individual elements interact. Computer modeling of biologically inspired neurons #neurons, with random fluctuating excitatory and inhibitory synapses and internal calcium operating mechanisms, provides an excellent solution for gaining deeper insights into neural circuitry operations for both the novice and the experienced alike! This includes crucial computations of neuronal elements such as action potential firing, bursting propensity, irregularity, and depolarization block. In this context, I present an extended, unpublished model of a brainstem #dopamine neuron that are important in sleep regulation #sleep. This model builds upon my previously published work and experimental research, demonstrating the interplay of ionic conductances and synaptic activation on the computational output of dopamine neurons. The model includes parameter boxes that allow users to modify properties on the fly and rerun simulations to observe the results. Additionally, data-saving functionalities are implemented, offering flexibility for extending outputs as needed. Freely available for download from my GitHub under a GNU licence. Runs on NEURON environment using precompiled .mod files in C! Have fun learning and simulating! https://lnkd.in/ePFNBmtf