Computational neuroscience

Computational neuroscience is an interdisciplinary field that uses mathematical models, simulations, and theoretical approaches to understand the brain's structure and function. It combines principles from neuroscience, computer science, mathematics, physics, and engineering to analyze neural systems and processes. Key aspects of computational neuroscience include: 1. **Modeling Neural Activity**: Researchers create models to replicate the electrical activity of neurons, including how they generate action potentials, communicate with each other, and process information.

Hebbian theory, often summarized by the phrase "cells that fire together, wire together," is a principle of synaptic plasticity in neuroscience that describes how the connections between neurons, or synapses, change over time based on their activity patterns. It was proposed by the psychologist Donald Hebb in his 1949 book "The Organization of Behavior.

Neurotechnology refers to an interdisciplinary field that combines neuroscience, engineering, and technology to develop devices and systems designed to interface with the nervous system. This can involve a range of applications, including the study and manipulation of neural activity, the enhancement of cognitive functions, and the treatment of neurological disorders.

"A.I. Rising" is a science fiction film released in 2018, directed by Lazar Bodrozic. The movie is set in a future where humanity has developed advanced artificial intelligence and explores the complexities of human-A.I. relationships. The story revolves around a space mission where a human astronaut forms a bond with a humanoid A.I. named KIKI, who is designed to serve and assist the crew.

AI alignment refers to the challenge of ensuring that artificial intelligence systems' goals, values, and behaviors align with those of humans. This is particularly important as we develop more powerful AI systems that may operate autonomously and make decisions that can significantly impact individuals and society at large. The primary aim of AI alignment is to ensure that the actions taken by AI systems are beneficial to humanity and do not lead to unintended harmful consequences.

An action potential is a rapid, significant change in the electrical membrane potential of a neuron or muscle cell, which occurs when the cell is activated by a stimulus. It is a fundamental mechanism for transmitting signals in the nervous system and is crucial for muscle contraction.

An action potential is a rapid, temporary change in the electrical membrane potential of a cell, particularly in excitable cells like neurons and muscle cells. This change allows for the transmission of electrical signals along the length of the cell and between cells.

An activating function, or activation function, is a mathematical function used in artificial neural networks to introduce non-linearity into the model. This is crucial because it allows the network to learn complex patterns in data. Without non-linear activation functions, a neural network would effectively behave like a linear model, regardless of how many layers it had.

An Artificial Intelligence (AI) system is a computer program or a set of algorithms designed to perform tasks that typically require human intelligence. These tasks can include understanding natural language, recognizing patterns, learning from data, making decisions, solving problems, and even exhibiting creativity. AI systems can range from simple rule-based programs to complex machine learning models that can adapt and improve over time based on experience.

An "artificial brain" generally refers to advanced computational systems designed to simulate the functions of the human brain. This concept encompasses a range of technologies and disciplines, including artificial intelligence (AI), neural networks, and brain-computer interfaces. Here are some key aspects: 1. **Artificial Intelligence**: AI systems aim to replicate cognitive functions like learning, reasoning, and problem-solving, although they are not modeled on neural structures in a direct way.

Artificial consciousness, often referred to as synthetic consciousness or machine consciousness, is the hypothetical concept of a machine or software system having conscious experiences similar to those of humans or other sentient beings. It involves the development of artificial systems that possess qualities associated with consciousness, such as self-awareness, the ability to perceive and respond to the environment, subjective experiences, and potentially even emotions.

Artificial empathy refers to the ability of a machine or algorithm to recognize, respond to, and simulate human emotions in a way that appears empathetic. This concept is gaining interest in fields such as artificial intelligence (AI), robotics, and human-computer interaction. Unlike genuine human empathy, which arises from emotional experience and understanding, artificial empathy relies on programmed responses, data analysis, and patterns in human behavior.

Artificial General Intelligence (AGI) refers to a type of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a level that is comparable to or indistinguishable from human intelligence. Unlike narrow AI, which is designed to perform specific tasks (such as image recognition or language translation), AGI would be able to reason, solve problems, and adapt to new situations in a general and flexible manner.

Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (the acquisition of information and rules for using it), reasoning (using rules to reach approximate or definite conclusions), and self-correction. AI can be categorized into different types and subfields: 1. **Narrow AI (Weak AI)**: This form of AI is designed and trained for a specific task, such as facial recognition, language translation, or playing chess.

The term "artificial intelligence arms race" refers to a competitive situation among nations, corporations, or groups to develop and deploy advanced artificial intelligence technologies at the fastest pace, often with military or strategic applications in mind. This race can involve a variety of AI technologies, including machine learning, autonomous systems, natural language processing, and others that have potential applications in defense, security, and international power dynamics.

Artificial intelligence detection software refers to tools and systems designed to identify, analyze, and evaluate the presence or influence of artificial intelligence (AI) in various contexts. This can include: 1. **AI-generated Content Detection**: Software that detects texts, images, videos, or any other content generated by AI models, such as GPT-3, DALL-E, or other generative algorithms.

"Artificial wisdom" is a concept that refers to the application of advanced artificial intelligence (AI) systems to interpret, understand, and provide insights that go beyond mere data analysis. While traditional AI focuses on processing information, recognizing patterns, and making predictions based on quantitative data, artificial wisdom aims to incorporate deeper knowledge, contextual awareness, ethical considerations, and emotional intelligence into the decision-making process.

As of my last knowledge update in October 2023, "Autapse" does not refer to a widely recognized term in scientific literature or popular culture. However, it is possible that it could refer to a specific concept, product, technology, or niche subject in fields like neuroscience, artificial intelligence, or perhaps even a brand or software that has emerged after my last update.

BCM theory, or Bardeen-Cooper-Schrieffer theory, is a theoretical framework that describes superconductivity in many materials. Developed in 1957 by John Bardeen, Leon Cooper, and Robert Schrieffer, this theory explains how certain materials can conduct electricity without resistance when cooled to very low temperatures. Key concepts of BCM theory include: 1. **Cooper Pairs**: At low temperatures, electrons in a superconductor can form pairs known as Cooper pairs.

Bayesian approaches to brain function refer to the application of Bayesian statistical principles to understand how the brain processes information, makes decisions, and learns from experience. These approaches posit that the brain operates in a way that is fundamentally probabilistic, where it constantly updates its beliefs about the world based on prior knowledge and new sensory information. ### Key Concepts: 1. **Bayesian Inference**: This is a statistical method that updates the probability for a hypothesis as more evidence or information becomes available.

"BigBrain" can refer to several things depending on the context, but it is often associated with projects or initiatives in neuroscience and technology. One prominent example is the "BigBrain Project," which involves creating a detailed, 3D digital map of the human brain. This project aims to enhance our understanding of brain structure and function using advanced imaging techniques, particularly magnetic resonance imaging (MRI). It provides a valuable resource for researchers studying the brain and neurological diseases.

The term "binding neuron" is not widely recognized in mainstream neuroscience terminology, but it can refer to concepts in cognitive neuroscience or computational models related to how the brain integrates and binds information from different sensory modalities or cognitive processes. In a general context, "binding" refers to the process by which the brain combines disparate pieces of information (such as visual, auditory, and tactile inputs) to form a coherent perception or understanding of an object or event.

A biological neuron model is a representation of the structure and function of neurons, which are the fundamental units of the brain and nervous system. Neurons transmit information throughout the body via electrical and chemical signals. While there are various ways to model neurons, the most common approaches include simplified models that emphasize their essential characteristics and more detailed biophysical models that capture the complexity of neuronal behavior.

The Blue Brain Project is a scientific research initiative aimed at creating a detailed, biologically accurate digital reconstruction of the brain. Launched in 2005 by the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, the project seeks to understand the intricate workings of the brain by simulating its components, particularly at the cellular and molecular levels.

Brain-reading refers to the process of interpreting or decoding brain activity to infer thoughts, intentions, or mental states. This can be achieved through various techniques, most notably neuroimaging methods such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG). Researchers use these technologies to analyze patterns of brain activity and correlate them with specific cognitive functions or responses.

Brain simulation refers to computational and experimental techniques used to create models of the brain's structure and functionality. These simulations aim to replicate the processes of the brain, facilitating a deeper understanding of its operations, including neuronal activity, neural networks, and behavioral responses. There are several approaches and applications in brain simulation: 1. **Computational Models**: These models use mathematical and computational frameworks to simulate the behavior of neurons and networks of neurons.

Brain-body interaction refers to the intricate and dynamic communication between the brain and various bodily systems. This interplay is crucial for regulating numerous physiological processes, behaviors, and responses to the environment. The interaction can be understood through multiple dimensions: 1. **Neurophysiological Communication**: The brain communicates with the body through the nervous system.

Brian is a simulator for spiking neural networks (SNNs). It is written in Python and is designed to facilitate the study of spiking neurons and the dynamics of networks of such neurons. Brian allows researchers and developers to easily implement and simulate complex neural models without needing a deep understanding of the underlying numerical methods.

The Budapest Reference Connectome is a comprehensive brain connectivity map that was created to serve as a reference model for understanding how different regions of the brain are interconnected. This project is part of a broader effort in neuroscience to map the human brain's structure and function, known as the connectome. The connectome represents the complex network of neural connections in the brain, including both the anatomical pathways (how neurons are physically connected) and functional connections (how different brain regions communicate with each other).

Cable theory is a mathematical model used to describe the electrical properties of neuronal cells, specifically the way that electrical signals propagate along the length of an axon or dendrite. It provides a framework for understanding how neurons transmit electrical signals through their membranes, considering their cylindrical geometry and the physical properties of cellular components like membranes, cytoplasm, and the extracellular medium.

Caret is an open-source software tool designed primarily for visualizing and manipulating spatial transcriptomics data. It is particularly useful for researchers in the fields of genomics and bioinformatics, allowing them to explore and analyze complex datasets that involve gene expression information in a spatial context. Caret provides various functionalities, including: 1. **Data Visualization**: It helps in creating plots and visualizations that depict gene expression levels across different spatial locations in tissues or organisms.

Carina Curto is a prominent neuroscientist known for her research in the field of neuroscience, particularly relating to the mechanisms of the brain and how they influence behavior. She has made significant contributions to understanding sensory processing, neural circuits, and related topics within both developmental and adult neuroscience. Curto's work often employs advanced imaging techniques and quantitative analyses to explore the underlying principles of neural function and connectivity. Additionally, she may be involved in teaching and mentoring students in the field of neuroscience.

The Cerebellar Model Articulation Controller (CMAC) is a type of neural network model inspired by the structure and function of the cerebellum in the human brain. It was developed for control and learning tasks, particularly in robotics and complex system simulations. ### Key Features of CMAC: 1. **Architecture**: - CMAC consists of a combination of memory storage and function approximation.

The Conference on Neural Information Processing Systems (NeurIPS) is one of the premier conferences in the field of machine learning and artificial intelligence. It focuses on advances in neural computation and related areas, including but not limited to machine learning, statistics, optimization, and cognitive science. NeurIPS serves as a platform for researchers, practitioners, and experts from diverse fields to present their latest findings, share ideas, and discuss challenges in artificial intelligence and machine learning.

Connectionism is a theoretical framework in cognitive science and artificial intelligence that models mental processes using networks of simple units, often inspired by the way biological neural networks operate in the brain. It emphasizes the connections between these units, which can represent neurons, and how they work together to process information. Key characteristics of connectionism include: 1. **Neural Networks**: Connectionist models are typically built using artificial neural networks (ANNs) that consist of layers of interconnected nodes or "neurons.

The term "connectome" refers to a comprehensive map of the neural connections in the brain. It is analogous to a genome, which represents the complete set of genetic material in an organism. The connectome aims to detail the complex network of neurons and their synaptic connections, providing insight into how different brain regions communicate with one another.

"Connectome" is a book written by Sebastian Seung, a neuroscientist and professor of computational neuroscience. Published in 2012, the book explores the concept of the connectome, which refers to the comprehensive map of neural connections in the brain. Seung discusses how these connections, made up of neurons and their synapses, play a fundamental role in shaping our thoughts, memories, and behaviors.

A Convolutional Neural Network (CNN) is a class of deep learning algorithms that is particularly effective for processing data with a grid-like topology, such as images. CNNs are widely used in computer vision tasks, including image classification, object detection, and segmentation, among others. ### Key Components of CNNs: 1. **Convolutional Layers**: - The core building block of a CNN.

A cultured neuronal network refers to a network of neurons that have been derived from living cells and maintained in vitro (in a laboratory environment) for study. These neuronal cultures can be established from various sources, including embryonic or postnatal brain tissue, stem cells, or genetically modified cells. Key features of cultured neuronal networks include: 1. **Cellular Composition**: Cultured neuronal networks typically consist of neurons and may also include glial cells, which support and protect neurons.

Dendritic spines are small, protruding structures found on the dendrites of neurons. They serve as the primary sites for synaptic transmission and are critical for neural communication and plasticity. Each spine forms a synapse with an axon terminal from another neuron, allowing for the transfer of signals across the synapse. Dendritic spines vary in shape and size, and their morphology can change in response to neural activity, a phenomenon known as synaptic plasticity.

The Exponential Integrate-and-Fire (EIF) model is a mathematical representation often used in computational neuroscience to simulate the behavior of spiking neurons. It is an extension of the simple Integrate-and-Fire (IF) model and incorporates more biologically realistic dynamics, particularly in the way neuronal depolarization occurs.

Fast Analog Computing with Emergent Transient States is a concept in the field of computing and neuromorphic engineering that explores the utilization of analog hardware to perform computations quickly and efficiently. This approach often draws inspiration from the way biological systems, particularly the brain, process information.

The FitzHugh-Nagumo model is a mathematical model used to describe the electrical activity of excitable cells, such as neurons and cardiac cells. It's a simplification of the more complex Hodgkin-Huxley model, which describes action potentials in neurons. The FitzHugh-Nagumo model captures the essential features of excitability and is often used in theoretical biology, neuroscience, and studying various types of wave phenomena in excitable media.

The Galves–Löcherbach model is a mathematical model used in the field of statistical mechanics and spin glasses. It is a type of interacting particle system that features a discrete collection of spins (or binary variables) which can represent different states (e.g., up or down). The model is constructed to study the behavior of these spins under a stochastic (random) dynamics influenced by both local interactions between neighboring spins and a global external field.

Gašper Tkačik does not appear to be widely recognized in public databases, notable figures, or historical texts up to October 2023. It is possible that he may be a private individual or a professional in a specific field that has not gained significant public attention. If you have more context or specific details about who Gašper Tkačik is or the relevant domain (such as science, art, sports, etc.

As of my last knowledge update in October 2021, there is no widely recognized figure or concept specifically known as "Gregor Schöner." It's possible that it may refer to a person who has gained prominence after that date, or it could be a name relevant in a specific field or context not widely known.

In the context of artificial intelligence, particularly in natural language processing and machine learning, "hallucination" refers to the phenomenon where a model generates information that is plausible-sounding but factually incorrect, nonsensical, or entirely fabricated. This can occur in models like chatbots, text generators, or any AI system that creates content based on learned patterns from data.

High-frequency oscillations (HFOs) refer to transient brain wave patterns that occur at frequencies greater than 80 Hz and can be observed in various types of neurophysiological recordings, such as electroencephalograms (EEGs) and intracranial electroencephalograms (iEEGs). HFOs are often classified into two main categories based on their frequency range: 1. **Fast ripples**: Typically defined as oscillations between 250 to 500 Hz.

The Hindmarsh–Rose model is a mathematical model used to describe the dynamics of spiking neurons. Developed by Brian Hindmarsh and Gerhard Rose in the late 1980s, it is a type of neuron model that captures key features of the behavior of real biological neurons, including the spiking and bursting phenomena. The model is based on a set of ordinary differential equations that represent the membrane potential of a neuron and the dynamics of ion currents across the neuronal membrane.

The Hodgkin–Huxley model is a mathematical description of the electrical characteristics of excitable cells, particularly neurons. Developed in 1952 by Alan Hodgkin and Andrew Huxley, this model provides a detailed mechanism for understanding how action potentials (the rapid depolarization and repolarization of the neuronal membrane) are generated and propagated. ### Key Components of the Hodgkin–Huxley Model 1.

The Human Brain Project (HBP) is a major scientific initiative that aims to advance our understanding of the human brain and develop new computing technologies inspired by brain function. Launched in 2013 as part of the European Union's Future and Emerging Technologies (FET) program, the project is one of the largest neuroscience research initiatives in the world.

The Human Connectome Project (HCP) is a multidisciplinary research initiative aimed at mapping the neural connections within the human brain, often referred to as the "connectome." Launched in 2009, the project seeks to understand how these connections relate to brain function, structure, and behavior.

The International Neuroinformatics Coordinating Facility (INCF) is an international organization that aims to promote collaboration and data sharing in the field of neuroinformatics, which is the discipline that combines neuroscience and informatics to facilitate the collection, sharing, and analysis of data related to the brain and nervous system. Established in 2005, the INCF works to enhance the ability of researchers worldwide to leverage computational tools and data resources to better understand neural systems.

Julijana Gjorgjieva is a prominent figure, often recognized for her contributions in a specific field, but without additional context, it's challenging to provide precise information about her. As of my last update in October 2023, there may have been developments or changes related to her career or activities.

Laurent Itti is a prominent figure in the fields of neuroscience and artificial intelligence, particularly known for his research on visual attention and the mechanisms of perception. He has contributed significantly to our understanding of how the brain processes visual information and how attention influences perception and behavior. Itti's work often combines computational models with experimental neuroscience, aiming to simulate and understand how visual attention operates in humans and how these principles can be applied to artificial systems.

Liam Paninski is an American neuroscientist known for his work on statistical methods in neuroscience, particularly in the areas of computational neuroscience, neuronal modeling, and the analysis of large-scale neural data. His research often focuses on understanding the dynamics of neural networks and how neurons encode information. Paninski has contributed to developing statistical techniques that help interpret complex neural data, such as spike train analysis and dimensionality reduction.

The Linear-Nonlinear-Poisson (LNP) cascade model is a framework used in computational neuroscience to describe how sensory neurons process information. It captures the relationship between the stimuli (inputs) that a neuron receives and its firing rate (output), providing insights into the underlying mechanisms of neural coding. Here's a breakdown of the components of the LNP model: 1. **Linear Component**: The first stage of the model involves a linear transformation of the input stimulus.

Maximally Informative Dimensions (MID) refers to a concept in the fields of data science and machine learning, particularly in the context of dimensionality reduction and feature selection. It focuses on identifying the dimensions (or features) of a dataset that provide the most useful information for a particular task, such as classification, regression, or clustering. The underlying idea of maximally informative dimensions is that not all dimensions in a dataset contribute equally to the predictive power or understanding of the data.

Metalearning, in the context of neuroscience, refers to the processes and mechanisms involved in learning about learning. It encompasses the ability to understand, evaluate, and adapt one's own learning strategies and processes. This concept is often discussed in both educational psychology and cognitive neuroscience, where it is understood as an essential component of self-regulated learning.

Metastability in the brain refers to a dynamic state where neural systems exhibit a degree of stability while remaining poised between different configurations or states of activity. This concept is often used in the context of brain function, especially concerning how different brain regions interact and process information. Here are some key aspects of metastability in the brain: 1. **Dynamic Balance**: Metastable states involve a balance between stability and flexibility.

Models of neural computation refer to theoretical frameworks and mathematical representations used to understand how neural systems, particularly in the brain, process information. These models encompass various approaches and techniques that aim to explain the mechanisms of information representation, transmission, processing, and learning in biological and artificial neural networks. Here are some key aspects of models of neural computation: 1. **Neuroscientific Models**: These models draw from experimental data to simulate and describe the functioning of biological neurons and neural circuits.

A modular neural network is a type of neural network architecture that is composed of multiple independent or semi-independent modules, each designed to handle specific parts of a task or a set of related tasks. The key idea behind modular neural networks is to break down complex problems into simpler, more manageable components, allowing for greater flexibility, scalability, and specialization.

The Morris–Lecar model is a mathematical model used to describe the electrical activity of neurons, specifically the action potentials generated by excitable cells. It was developed by biophysicists Gary Morris and Giorgio Lecar in the late 1980s as a simplification of the more complex Hodgkin-Huxley model.

A Multi-Simulation Coordinator is a role or position that typically involves overseeing and managing multiple simulation processes or environments simultaneously. This function is often found in fields such as: 1. **Healthcare**: In medical training, a Multi-Simulation Coordinator might be responsible for organizing and facilitating various simulation scenarios for healthcare professionals, ensuring that different departments or specializations (like surgery, emergency response, or nursing) are effectively trained using realistic simulations.

Nervous system network models refer to computational or conceptual frameworks used to understand the structure and function of neural networks within the nervous system. These models aim to replicate the complexity of neural connections and interactions at various scales, from single neurons to entire neural circuits or brain regions. ### Key Components of Nervous System Network Models: 1. **Neurons**: The basic building blocks of the nervous system, modeled as computational units that can process and transmit information through electrical and chemical signals.

Neural accommodation typically refers to the adjustments that the nervous system makes in response to varying sensory stimuli, allowing it to maintain homeostasis or to adapt to changes in the environment. While the term may not be widely used in mainstream neuroscience, it can be interpreted in a few different contexts: 1. **Sensory Adaptation**: This is the process by which sensory receptors become less sensitive to constant stimuli over time.

Neural backpropagation, commonly referred to as backpropagation, is an algorithm used for training artificial neural networks. It utilizes a method called gradient descent to optimize the weights of the network in order to minimize the error in predictions made by the model. ### Key Components of Backpropagation: 1. **Forward Pass**: - The input data is fed into the neural network, and activations are computed layer by layer until the output layer is reached.

Neural coding refers to the way in which information is represented and processed in the brain by neurons. It encompasses the mechanisms by which neurons encode, transmit, and decode information about stimuli, experiences, and responses. Understanding neural coding is crucial for deciphering how the brain interprets sensory inputs, generates thoughts, and guides behaviors. There are several key aspects of neural coding: 1. **Types of Coding**: - **Rate Coding**: Information is represented by the firing rate of neurons.

Neural computation refers to a field of study that explores how neural systems, particularly biological neural networks (like the human brain), process information. It encompasses various aspects, including the mechanisms of learning, perception, memory, and decision-making that occur in biological systems. Researchers in this field often draw inspiration from the structure and function of the brain to develop mathematical models and computational algorithms.

Neural decoding is a process in neuroscience and artificial intelligence that involves interpreting neural signals to infer information about the external world, brain activities, or cognitive states. It typically focuses on understanding how neural activity corresponds to specific stimuli, behaviors, or cognitive processes. Here are some key aspects of neural decoding: 1. **Measurement of Neural Activity**: Neural decoding often begins with the collection of raw data from neural activity.

Neural oscillation refers to rhythmic or repetitive patterns of neural activity in the brain. These oscillations can be observed in various forms across different frequencies and are associated with a variety of cognitive and behavioral processes. They are typically measured using electroencephalography (EEG) and can be classified into several frequency bands: 1. **Delta Waves (0.5-4 Hz)**: Slow oscillations often associated with deep sleep and restorative processes.

Neurocomputational speech processing is an interdisciplinary field that combines principles from neuroscience, computer science, and linguistics to study and develop systems capable of processing human speech. This area of research seeks to understand how the brain processes spoken language and to model these processes in computational terms.

Neurogrid is a technology developed to simulate large-scale neural networks in real time. It was created by researchers at Stanford University, led by Dmitri B. Chklovskii, and is designed to mimic the way the human brain processes information. The core idea behind Neurogrid is to create neuromorphic circuits that replicate the behavior of biological neurons and synapses, enabling researchers to simulate the activities of thousands or even millions of neurons simultaneously.

NeuronStudio is a software tool designed for the analysis and reconstruction of neural morphology, particularly for the study of neurons and their complex structures. It is commonly used in neurobiology and related fields to facilitate the visualization, examination, and quantification of neuron shapes and connections, aiding researchers in understanding the architecture and functional properties of neural networks.

Neuron is a flexible and powerful software tool primarily used for computational modeling of neural systems. It allows researchers to create detailed models of individual neurons and neural circuits, which can be critical for studying brain function and dynamics. Some features of Neuron include: 1. **Simulation of Neuronal Activity**: Neuron can simulate electrical activity in neurons, including ion channel dynamics and synaptic interactions.

Neurosecurity is an emerging field that focuses on the protection of neural data and the safeguarding of brain-computer interfaces (BCIs), neurotechnology, and cognitive functions from unauthorized access and malicious activities. As neuroscience and technology continue to advance, particularly in the development of BCIs, neurosecurity addresses various concerns related to privacy, ethics, and security in neurotechnological applications.

New Lab is a collaborative workspace and innovation hub located in the Brooklyn Navy Yard in New York City. Founded in 2018, New Lab focuses on fostering entrepreneurship, particularly in fields like advanced manufacturing, robotics, artificial intelligence, and other emerging technologies. It provides a platform for startups, artists, engineers, and designers to collaborate, share resources, and develop their projects.

Ogi Ogas is a neuroscientist and author, known for his work on topics related to neuroscience, artificial intelligence, and behavior. He has co-authored several books, including "A Billion Wicked Thoughts," which explores the sexual preferences of men and women using data from online behavior. Ogas has been involved in research that examines how the brain processes information and how this knowledge can be applied to understand human behavior, including aspects related to sexual attraction and decision-making.

Oja's rule is an unsupervised learning algorithm used in the field of neural networks and machine learning, particularly in the context of learning vector representations. It is a type of Hebbian learning rule, which is based on the principle that neurons that fire together, wire together. Oja's rule is specifically designed to allow a neural network to learn the principal components of the input data, effectively performing a form of principal component analysis (PCA).

Parabolic bursting is a term often associated with the phenomenon of explosive or rapid growth in the context of various fields, including finance, economics, and even in physical systems. It typically describes a situation where a variable experiences an exponential increase over a relatively short period, leading to a steep curve that resembles a parabola. In finance, for example, parabolic bursting might refer to the rapid price increase of an asset, followed by a sudden crash, often resembling a parabolic shape when graphed.

Parallel constraint satisfaction processes refer to approaches or methods in computer science and artificial intelligence where multiple constraint satisfaction problems (CSPs) are solved simultaneously or in parallel. Constraint satisfaction problems involve finding values for variables under specific constraints, such that all constraints are satisfied. Examples of CSPs include puzzles like Sudoku, scheduling problems, and various optimization tasks. ### Key Concepts 1.

Paul Bressloff is a notable figure in the field of mathematics, particularly known for his work in applied mathematics and computational neuroscience. He has contributed to the study of mathematical models that explain neural dynamics and brain function. Bressloff has published research on various topics, including neural networks, excitability, and the mathematical modeling of sensory processing.

A population vector is a concept often used in neuroscience, particularly in the study of sensory systems, motor control, and neural coding. It refers to a representation of information within a population of neurons that collectively encode a specific parameter, such as direction of movement or sensory stimuli. Here's how it works: 1. **Population Activity**: Instead of relying on the activity of a single neuron, population vectors consider the collective activity of a group of neurons.

Pulse computation refers to a method of processing information that uses pulses—discrete signals or waveforms that represent data at specific points in time. This approach is often associated with various fields such as digital signal processing, neural networks, and even quantum computing. ### Key Aspects of Pulse Computation: 1. **Pulse Signals:** Information is encoded in the form of pulse signals, typically characterized by sharp changes in voltage or current.

SUPS can refer to different terms depending on the context, but one common interpretation is "Standardized Universal Product Specifications." This term is often used in industries like retail and manufacturing to denote a standardized set of specifications that help in identifying and describing products. Another possible meaning could be "Supplemental Nutritional Products" in the context of nutrition and health.

Sean Hill is a notable scientist in the fields of computational neuroscience and theoretical biology. He is known for his work in understanding brain processes and neural dynamics by developing mathematical models and simulations. His research often focuses on how neural circuits process information, the mechanisms underlying learning and memory, and the mathematical properties of neural networks. Hill has contributed to various scientific publications and has worked on projects that utilize advanced computational techniques to explore complex neural phenomena.

The Softmax function is a mathematical function that converts a vector of real numbers into a probability distribution. It is commonly used in machine learning and statistics, particularly in the context of multiclass classification problems. The Softmax function is often applied to the output layer of a neural network when the task is to classify inputs into one of several distinct classes.

The soliton model in neuroscience is a theoretical concept that describes how certain types of wave-like phenomena in neural tissue can propagate without losing their shape or amplitude. This is particularly relevant in the study of action potentials and the electrical signaling of neurons. In the field of neuroscience, a "soliton" refers to a self-reinforcing solitary wave that maintains its shape while traveling at a constant speed.

SpiNNaker (Spiking Neural Network Architecture) is an innovative hardware platform designed to model and simulate large-scale spiking neural networks. Developed at the University of Manchester, SpiNNaker is built to mimic the way biological neural networks operate, allowing researchers to study brain-like computations and processes. Key features of SpiNNaker include: 1. **Parallel Processing**: The architecture consists of a large number of simple processing cores (over a million), enabling massive parallel processing capabilities.

The spike-triggered average (STA) is a method used in computational neuroscience to characterize the relationship between neuronal spike train activity and sensory stimuli. It involves analyzing how specific inputs or stimuli relate to the output of a neuron, particularly the times at which the neuron fires action potentials (or spikes). Here's how it works, step by step: 1. **Data Collection:** A neuron's spiking activity is recorded alongside a sensory stimulus (such as a visual or auditory signal).

Spike-triggered covariance (STC) is a computational technique used in neuroscience to analyze how the spiking activity of a neuron's action potentials (or 'spikes') relates to the sensory stimuli that the neuron receives. The method helps to identify the preferred stimulus features that drive neuron firing. ### Key Concepts of Spike-Triggered Covariance: 1. **Spike Train:** The sequence of spikes emitted by a neuron over time in response to stimuli.

Spike directivity refers to a phenomenon in neuroscience, particularly in the context of action potentials and neuronal firing patterns. In simple terms, it describes how the direction of action potential propagation in neurons can influence the way information is transmitted and processed in the nervous system. In more specific contexts, such as in studies of neural coding or synaptic transmission, spike directivity may refer to the alignment and orientation of neuronal activity in relation to the specific inputs they receive.

The Spike Response Model (SRM) is a type of mathematical model used to describe the dynamics of neuron firing in response to various stimuli. It is particularly relevant in the field of computational neuroscience and serves as a framework for understanding how neurons process inputs and generate output spikes (action potentials). Here are some key characteristics of the Spike Response Model: 1. **Spike Generation**: The model focuses on the timing of spikes, which are the discrete events when a neuron emits an action potential.

Steady state topography refers to a theoretical state of landforms where the rate of erosion and the rate of uplift or sediment deposition are balanced over time. In this context, the landscape reaches a dynamic equilibrium such that the overall shape and characteristics of the topography remain relatively constant despite ongoing geological processes. In practice, steady state topography is achieved when the forces that shape the landscape (such as tectonic uplift, erosion by wind or water, and sediment transport) are in equilibrium.

Synthetic intelligence refers to forms of artificial intelligence that attempt to mimic or replicate human-like cognitive processes, behaviors, and decisions. It often encompasses various techniques and methodologies, including machine learning, neural networks, natural language processing, and robotics. The term can sometimes be used interchangeably with artificial general intelligence (AGI), which refers to AI systems that possess a level of understanding and capability comparable to that of a human being, allowing for reasoning, problem-solving, and learning across a diverse range of tasks.

Temporal Difference (TD) learning is a central concept in the field of reinforcement learning (RL), which is a type of machine learning concerned with how agents ought to take actions in an environment in order to maximize some notion of cumulative reward. TD learning combines ideas from Monte Carlo methods and Dynamic Programming. Here are some key features of Temporal Difference learning: 1. **Learning from Experience:** TD learning allows an agent to learn directly from episodes of experience without needing a model of the environment.

The Tempotron is a computational model of a neuron that simulates the learning mechanism for spiking neural networks. It was proposed to describe how biological neurons can learn to respond to specific patterns of input over time. In a Tempotron model, the neuron integrates incoming spikes (electrical impulses) from other neurons over time and can fire (generate its own spike) once a certain threshold is reached.

Tensor network theory is a mathematical framework used primarily in quantum physics and condensed matter physics to represent complex quantum states and perform calculations involving them. The core idea is to represent high-dimensional tensors (which can be thought of as a generalization of vectors and matrices) in a more manageable way using networks of interconnected tensors. This representation can simplify computations and help in understanding the structure of quantum states, particularly in many-body systems. ### Key Concepts 1.

Theoretical neuromorphology is an interdisciplinary field that combines principles from neuroscience, biology, and theoretical modeling to understand the structure and organization of nervous systems. It explores the relationship between the physical structure (morphology) of neural systems and their function, focusing on how anatomical features of neurons and neural networks influence processes such as information processing, learning, and behavior.

The Theta model is a statistical forecasting method primarily used for time series data. It was introduced in a paper by Forecasters Koenker and d’Orey in 2001 and has gained recognition due to its strong performance in various forecasting competitions, including the M3 Competition. Key features of the Theta model include: 1. **Decomposition Approach**: The model combines the classical decomposition of time series data into different components—such as trend, seasonality, and noise—with regression techniques.

Vaa3D (Visualization and Analysis Association for 3D Data) is an open-source software platform primarily designed for the visualization and analysis of large-scale three-dimensional (3D) biological datasets. It is particularly useful in fields such as neuroscience, where researchers often work with complex 3D volumetric data from imaging techniques like confocal microscopy, 3D electron microscopy, and other modalities.