## Tags - Part of: [[Natural science]] [[Cognitive science]] - Related: - Includes: [[Computational Neuroscience]] - Additional: ## Definitions - [[Set Theory|Subset]] of [[Biology]] studying the nervous system. ## Main resources - [Neuroscience - Wikipedia](https://en.wikipedia.org/wiki/Neuroscience) <iframe src="https://en.wikipedia.org/wiki/Neuroscience" allow="fullscreen" allowfullscreen="" style="height:100%;width:100%; aspect-ratio: 16 / 5; "></iframe> ## Landscapes - [Outline of neuroscience - Wikipedia](https://en.wikipedia.org/wiki/Outline_of_neuroscience) - <iframe src="https://en.wikipedia.org/wiki/Outline_of_neuroscience" allow="fullscreen" allowfullscreen="" style="height:100%;width:100%; aspect-ratio: 16 / 5; "></iframe> - - [[Neurophysiology]] - study of the function (as opposed to structure) of the nervous system - [[Neuroanatomy]] - study of the anatomy of nervous tissue and neural structures of the nervous system - [[Neuropharmacology]] - study of how [[Substance|substances]] affect cellular function in the nervous system. - [[Neuropsychopharmacology]] -  study of the neural mechanisms that [[Substance|substances]] act upon to influence behavior - [[Behavioral neuroscience]] - application of the principles of [[Biology]] to the study of [[Experience|mental]] processes and behavior in human and non-human animals - [[Developmental neuroscience]] - aims to describe the cellular basis of brain development and to address the underlying mechanisms - [[Cognitive Neuroscience]] - study of biological substrates underlying cognition, with a focus on the neural substrates of mental processes - [[Systems Neuroscience]] - studies the function of neural circuits and [[System theory|systems]],.It is an umbrella term, encompassing a number of areas of study concerned with how nerve cells behave when connected together to form neural networks - [[Molecular neuroscience]] - examines the biology of the nervous system with [[molecular biology]], molecular [[genetics]], [[protein]] [[Chemistry]] and related methodologies - [[Computational Neuroscience]] - study of the [[Information processing theory|information processing]] functions of the nervous system, and the use of digital computers to study the nervous system instegrating [[Neuroscience]], [[Cognitive science]] and [[Psychology]], [[electrical engineering]], [[Computer science]], [[Physics]] and [[Mathematics]]. - [[Philosophy of neuroscience]] - [[Transdisciplinarity|interdisciplionary]] study of [[Neuroscience]] and [[Philosophy]] - [[Neurology]] - medical specialty dealing with disorders of the nervous system - [[Neuropsychology]] - studies the [[Structure]] and [[function]] of the brain related to [[Psychology|psychological]] [[process|processes]] and behaviors - [[Neuroevolution]] - dates back to the first development of nervous systems in animals - [[Neurophysics]] - [[Neurotechnology]] - [[Wellbeing]] - [[Philosophy of mind]] - [[Longevity]], [[Immortality]] - [[Human intelligence amplification]] - [[Neuromodulation]] - [[Artificial Intelligence x Neuroscience]] ## Brainstorming [[Thoughts (computational) neuroscience brain]] [[Resources brain computational neuroscience mind AI]] [[Thoughts mind phenomenology experience]] ## Resources [[Resources computational neuroscience]] [[Links (computational) neuroscience brain]] [[Resources mind]] [[Links mind]] [[Links AI neuroscience]] [[Links AI for neuroscience]] ## Deep dives - [List of unsolved problems in neuroscience - Wikipedia](https://en.wikipedia.org/wiki/List_of_unsolved_problems_in_neuroscience) - <iframe src="https://en.wikipedia.org/wiki/List_of_unsolved_problems_in_neuroscience" allow="fullscreen" allowfullscreen="" style="height:100%;width:100%; aspect-ratio: 16 / 5; "></iframe> - [Frontiers | Integrated world modeling theory expanded: Implications for the future of consciousness](https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2022.642397/full) [Frontiers | An Integrated World Modeling Theory (IWMT) of Consciousness: Combining Integrated Information and Global Neuronal Workspace Theories With the Free Energy Principle and Active Inference Framework; Toward Solving the Hard Problem and Characterizing Agentic Causation](https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2020.00030/full) - “Figure 2. Depiction of the human brain in terms of phenomenological correspondences, as well as computational (or functional), algorithmic, and implementational levels of analysis (Reprinted from Safron, 2021b). Depiction of the human brain in terms of entailed aspects of experience (i.e., phenomenology), as well as computational (or functional), algorithmic, and implementational levels of analysis (Marr, 1983; Safron, 2020b). A phenomenological level is specified to provide mappings between consciousness and these complementary/supervenient levels of analysis. Modal depictions connotate the radically embodied nature of mind, but not all images are meant to indicate conscious experiences. Phenomenal consciousness may solely be generated by hierarchies centered on posterior medial cortex, supramarginal gyrus, and angular gyrus as respective visuospatial (cf. consciousness as projective geometric modeling) (Rudrauf et al., 2017; Williford et al., 2018), somatic (cf. grounded cognition and intermediate level theory) (Varela et al., 1992; Barsalou, 2010; Prinz, 2017), and intentional/attentional phenomenology (cf. Attention Schema Theory) (Graziano, 2019). Computationally, various brain functions are identified according to particular modal aspects, either with respect to generating perception (both unconscious and conscious) or action (both unconscious and potentially conscious, via posterior generative models). (Note: Action selection can also occur via affordance competition in posterior cortices (Cisek, 2007), and frontal generative models could be interpreted as a kind of forward-looking (unconscious) perception, made conscious as imaginings via parameterizing the inversion of posterior generative models). On the algorithmic level, these functions are mapped onto variants of machine learning architectures—e.g., autoencoders and generative adversarial networks, graph neural networks (GNNs), recurrent reservoirs and liquid state machines—organized according to potential realization by neural systems. GNN-structured latent spaces are suggested as a potentially important architectural principle (Zhou et al., 2019), largely due to efficiency for emulating physical processes (Battaglia et al., 2018; Bapst et al., 2020; Cranmer et al., 2020). Hexagonally organized grid graph GNNs are depicted in posterior medial cortices as contributing to quasi-Cartesian spatial modeling (and potentially experience) (Haun and Tononi, 2019; Haun, 2020), as well as in dorsomedial, and ventromedial PFCs for agentic control. With respect to AI systems, such representations could be used to implement not just modeling of external spaces, but of consciousness as internal space (or blackboard), which could potentially be leveraged for reasoning processes with correspondences to category theory, analogy making via structured representations, and possibly causal inference. Neuroimaging evidence suggests these grids may be dynamically coupled in various ways (Faul et al., 2020), contributing to higher-order cognition as a kind of navigation/search process through generalized space (Hills et al., 2010; Kaplan and Friston, 2018; Çatal et al., 2021). A further GNN is speculatively adduced to reside in supramarginal gyrus as a mesh grid placed on top of a transformed representation of the primary sensorimotor homunculus (cf. body image/schema for the sake of efficient motor control/inference). This quasi-homuncular GNN may have some scaled correspondence to embodiment as felt from within, potentially morphed/re-represented to better correspond with externally viewed embodiments (potentially both resulting from and enabling “mirroring” with other agents for coordination and inference) (Rochat, 2010). Speculatively, this partial translation into a quasi-Cartesian reference frame may provide more effective couplings (or information-sharing) with semi-topographically organized representations in posterior medial cortices. Angular gyrus is depicted as containing a ring-shaped GNN to reflect a further level of abstraction and hierarchical control over action-oriented body schemas—which may potentially mediate coherent functional couplings between the “lived body” and the “mind’s eye”—functionally entailing vectors/tensors over attentional (and potentially intentional) processes (Graziano, 2018). Frontal homologs to posterior GNNs are also depicted, which may provide a variety of higher-order modeling abilities, including epistemic access for extended/distributed self-processes and intentional control mechanisms. These higher-order functionalities may be achieved via frontal cortices being more capable of temporally extended generative modeling (Parr et al., 2019c), and potentially also by virtue of being located further from primary sensory cortices, so affording (“counterfactually rich”) dynamics that are more decoupled from immediate sensorimotor contingencies. Further, these frontal control hierarchies afford multi-scale goal-oriented behavior via bidirectional effective connectivity with the basal ganglia (i.e., winner-take-all dynamics and facilitation of sequential operations) and canalization via diffuse neuro-modulator nuclei of the brainstem (i.e., implicit policies and value signals) (Houk et al., 2007; Humphries and Prescott, 2010; Stephenson-Jones et al., 2011; Dabney et al., 2020; Morrens et al., 2020). Finally, the frontal pole is described as a highly non-linear recurrent system capable of shaping overall activity via bifurcating capacities (Tani, 2016; Wang et al., 2018)—with potentially astronomical combinatorics—providing sources of novelty and rapid adaptation via situation-specific attractor dynamics. While the modal character of prefrontal computation is depicted at the phenomenological level of analysis, IWMT proposes frontal cortices might only indirectly contribute to consciousness via influencing dynamics in posterior cortices. Speculatively, functional analogs for ring-shaped GNN salience/relevance maps may potentially be found in the central complexes of insects and the tectums of all vertebrates (Honkanen et al., 2019), although it is unclear whether those structures would be associated with any kind of subjective experience. Even more speculatively, if these functional mappings were realized in a human-mimetic, neuromorphic AI, then it may have both flexible general intelligence and consciousness. In this way, this figure is a sort of pseudocode for (partially human-interpretable) AGI with “System 2” capacities (Bengio, 2017; Thomas et al., 2018), and possibly also phenomenal consciousness. (Note: The language of predictive processing provides bridges between implementational and computational (and also phenomenological) levels, but descriptions such as vector fields and attracting manifolds could have alternatively been used to remain agnostic as to which implicit algorithms might be entailed by physical dynamics). On the implementational level, biological realizations of algorithmic processes are depicted as corresponding to flows of activity and interactions between neuronal populations, canalized by the formation of metastable synchronous complexes (i.e., “self-organizing harmonic modes”; Safron, 2020a). (Note: The other models discussed in this manuscript do not depend on the accuracy of these putative mappings, nor the hypothesized mechanisms of centralized homunculi and “Cartesian theaters” with semi-topographic correspondences with phenomenology).” [[Images/e6daefd3b6a96fb3b8a0704f8db287a6_MD5.jpeg|Open: Pasted image 20240920043523.png]] ![[Images/e6daefd3b6a96fb3b8a0704f8db287a6_MD5.jpeg]] - An integrative, multiscale view on neural theories of consciousness Combination of global neuronal workspace theory + integrated information theory + recurrent processing theory + predictive processing theory + neurorepresentationalism + dendritic integration theory [An integrative, multiscale view on neural theories of consciousness: Neuron](https://www.cell.com/neuron/fulltext/S0896-6273(24)00088-6) ![[Pasted image 20240920043334.png]] ## Written by AI (may include factually incorrect information)may include incorrect information) #### Map 1 # Comprehensive Map of Computational Neuroscience **Introduction** Computational neuroscience is an interdisciplinary field that employs mathematical models, theoretical analysis, and computational techniques to understand the function and mechanisms of the nervous system. It bridges neuroscience with disciplines like physics, computer science, and engineering to decode how neural circuits process information. --- ### a. Neuron Models - **Hodgkin-Huxley Model**: Describes how action potentials in neurons are initiated and propagated through ion channel dynamics. - **Integrate-and-Fire Models**: Simplified neuron models that accumulate input until a threshold is reached, triggering a spike. - **Compartmental Models**: Represent neurons as interconnected compartments to simulate complex dendritic structures. ### b. Synaptic Transmission - **Chemical Synapses**: Modeling neurotransmitter release and receptor binding affecting post-synaptic potentials. - **Electrical Synapses**: Simulating gap junctions that allow direct ionic current flow between neurons. ### c. Neural Coding - **Rate Coding**: Information represented by the firing rate of neurons. - **Temporal Coding**: Emphasizes the timing of spikes for information transmission. - **Population Coding**: Considers ensembles of neurons for robust information encoding. --- ### a. Artificial Neural Networks (ANNs) - **Feedforward Networks**: Layers of neurons where connections move in one direction; foundational for deep learning. - **Recurrent Neural Networks (RNNs)**: Networks with feedback connections; useful for temporal data processing. - **Deep Learning**: Multi-layered ANNs that learn hierarchical representations; revolutionized machine learning. ### b. Biologically Plausible Networks - **Spiking Neural Networks**: Incorporate spike timing for more realistic neuron modeling. - **Self-Organizing Maps**: Unsupervised learning models that mimic cortical feature maps. --- ## 3. Computational Techniques - **Differential Equations**: Used to model continuous changes in neural states. - **Stochastic Processes**: Account for randomness in neural firing and synaptic transmission. - **Dynamical Systems**: Analyze stability and patterns in neural activity over time. --- ### a. Large-Scale Brain Models - **Blue Brain Project**: Aims to create a digital reconstruction of the rodent brain at the cellular level. - **Human Brain Project**: European initiative to simulate the human brain's complexity. ### b. Neural Circuit Simulations - Modeling specific circuits like the visual cortex or hippocampus to understand functionality. --- ### a. EEG/MEG Analysis - Techniques for interpreting electrical activity recorded from the scalp. - Source localization and time-frequency analysis. ### b. fMRI Data Analysis - Modeling hemodynamic responses to infer neural activity. - Functional connectivity and network analysis. ### c. Single-Unit Recording - Spike sorting algorithms to distinguish neuron signals. - Analysis of firing patterns and receptive fields. --- ### a. Databases - **Allen Brain Atlas**: Gene expression and connectivity data. - **NeuroMorpho.Org**: Repository of neuron morphological data. ### b. Computational Tools - **NEURON**: Simulation environment for modeling neurons and networks. - **Brian**: Simulator for spiking neural networks. --- ### a. Perception - Computational models of visual and auditory processing. - Object recognition and sensory integration. ### b. Memory - Models of short-term and long-term memory storage. - Synaptic plasticity mechanisms like Long-Term Potentiation (LTP). ### c. Decision Making - Drift-diffusion models for two-choice tasks. - Reinforcement learning algorithms to model reward-based learning. --- ### a. Brain-Machine Interfaces (BMIs) - Decoding neural signals to control external devices. - Applications in prosthetics and communication for paralyzed individuals. ### b. Neuromorphic Computing - Hardware that mimics neural architectures for efficient computation. - Uses in low-power, high-efficiency processing systems. ### c. Neuroprosthetics - Development of devices that replace or enhance neural function. - Cochlear implants and retinal prostheses as examples. --- ### a. Connectomics - Mapping neural connections at various scales. - **Human Connectome Project**: Comprehensive mapping of human brain connectivity. ### b. Computational Psychiatry - Modeling neural circuits implicated in mental disorders. - Personalized medicine approaches through computational diagnostics. ### c. Neuroeconomics - Studying decision-making processes involving economic choices. - Computational models integrating psychology and economics. --- **Conclusion** Computational neuroscience is a vast and evolving field that continues to deepen our understanding of the nervous system through interdisciplinary collaboration. By integrating computational models with empirical data, it holds the promise of unraveling the complexities of brain function and dysfunction. #### Map 2 - Neuroscience is an interdisciplinary field that studies the nervous system and its impact on behavior and cognitive functions. It encompasses a wide range of sub-disciplines and specializations. Here's a comprehensive list: ### 1. Cellular Neuroscience - Neuron Anatomy - Synaptic Transmission - Neurogenesis - Glial Cell Function - Cellular Neurophysiology - Molecular Neurobiology - Signal Transduction ### 2. Molecular Neuroscience - Neurochemistry - Neuropharmacology - Neurogenetics - Molecular Neuroanatomy - Neurotransmitters and Receptors - Molecular Basis of Neurological Disorders - Genetic Engineering in Neuroscience ### 3. [[Systems Neuroscience]] - Sensory Systems - Motor Systems - Neural Circuits - Neurophysiology - Comparative Neuroscience - Brain Mapping and Imaging - Neural Networks ### 4. Behavioral Neuroscience - Neuropsychology - Cognitive Neuroscience - Emotion and Affective Neuroscience - Social Neuroscience - Neuroethology (Animal Behavior) - Psychophysics - Learning and Memory ### 5. [[Cognitive Neuroscience]] - Memory and Learning - Attention and Decision-Making - Language Processing - Perception - Consciousness Studies - Executive Functions - Neuroimaging ### 6. Clinical Neuroscience - Neurology - Psychiatry - Neurosurgery - Pediatric Neuroscience - Neuropsychiatry - Neurorehabilitation - Brain Injury Medicine ### 7. [[Computational Neuroscience]] - Neural Modeling - Computational Neuroanatomy - Neuroinformatics - Theoretical Neuroscience - Brain-Computer Interfaces - Machine Learning in Neuroscience - Network Neuroscience ### 8. Neuroimaging - MRI and fMRI - PET and SPECT - EEG and MEG - DTI and QBI - Optical Imaging - Single-Photon Emission Computed Tomography - Near-Infrared Spectroscopy ### 9. Developmental Neuroscience - Neuroembryology - Neural Development - Synaptic Plasticity - Neurotrophic Factors - Developmental Neurobiology - Pediatric Neurology - Neurodevelopmental Disorders ### 10. Neuropharmacology and Neurochemistry - Psychopharmacology - Neurotoxicology - Drug Development and Testing - Neurotransmitter Systems - Chemical Neuroanatomy - Neuromodulators and Neurohormones ### 11. Neuroanatomy - Gross Anatomy of the Brain - Comparative Neuroanatomy - Histology of Nervous Tissue - Tractography - Functional Neuroanatomy - Cytoarchitecture ### 12. Neurophysiology - Electrophysiology - Neuroendocrinology - Sensory Physiology - Motor Physiology - Autonomic Nervous System - Neurovascular Physiology ### 13. Neurogenetics - Genetic Disorders of the Nervous System - Epigenetics in Neuroscience - Neurogenomic Studies - Gene Therapy in Neurological Diseases - Molecular Genetics of Neural Development ### 14. Evolutionary Neuroscience - Evolution of the Nervous System - Comparative Neurology - Paleo-Neurobiology - Animal Models in Neuroscience ### 15. Neuropathology - Neurodegenerative Diseases - Stroke and Cerebrovascular Diseases - Brain Tumors - Infectious Diseases of the Nervous System - Traumatic Brain Injury - Neuroimmunology ### 16. Interventional Neuroscience - Deep Brain Stimulation - Neural Prosthetics - Neuromodulation Therapies - Neurosurgical Techniques - Transcranial Magnetic Stimulation ### 17. Specialized Research Areas - Nutritional Neuroscience - Music Neuroscience - Sports Neuroscience - Geriatric Neuroscience - Environmental Neuroscience - Neuroaesthetics Each of these branches focuses on different aspects of the nervous system, from molecular and cellular processes to complex behaviors and cognitive functions. Neuroscience is a dynamic and expanding field, continuously evolving with new discoveries and technologies.