The Difference Between Brain and Mind
Brain is like the hardware and mind is like the software. But in reality, the difference between brain mind are more complicated than software and hardware. Brain is the biological body part, with all the biochemicals, biological cells, physical weight, etc. 2. Mind is th subtle aspect of body. As long as body is alive with. If dualism is not true, the mind is limited to the physical brain. .. Consequently, there is a connection between mind and brain that leads into its differences.
Within a brain, neurons continuously exchange signals with each other, mutually affecting their spatially distributed electrochemical state. In this framework, the mind-brain problem can be formulated as a simple question. Why and how do certain spatio-temporal patterns of neural dynamics relate to declarative mental states? What Would Count as an Explanation?
What’s The Difference Between The Mind And Brain? | HuffPost
Many relationships are routinely observed in the course of everyday life, such as how the weight of bodies relates to their size or how satisfying it feels to drink when thirsty. Why is the mind-brain relationship so different as to constitute a problem?
Brains and minds are so different from each other as to inspire several kinds of dualist philosophies in the course of time. This fundamental difference has been considered challenging by scientists and philosophers alike, from confronting possible inconsistencies in temporal dynamics [ 34 ] through a retreat to the acceptance of limited explanations [ 35 ], to overt calls for giving up scientific accounts altogether [ 36 ]. The more extreme positions maintaining that there is no solution to the mind-brain problem have been refuted both on philosophical [ 37 ] and scientific grounds [ 38 ].
The key issue is in fact to understand what would count as a solution. In other words, if the mind-brain relationship can be explained, what type of explanation is being sought? To answer this central question, it is useful to consider previous scientific breakthroughs that resulted in satisfactory understanding of relationships that had been considered difficult to explain before.
The publication of Newton's Principia [ 39 ] consolidated a century of gains from the scientific method introduced by Galileo [ 40 ] into a coherent and complete theory of mechanics. Newton's famous laws relating force to acceleration and defining the mutual attraction of masses by gravity provided an excellent description of planetary motion as well as accurate predictions of interactions among material objects. More than one hundred years later the first comprehensive treaty on thermodynamics was published [ 41 ], describing the relationship between heat, energy, temperature, and what was later called entropy.
Thermodynamics appeared completely independent of Newton's mechanics which explained instead how bodies move and respond to forces. While both mechanics and thermodynamics were and still are recognized as landmark advancements of scientific progress, they seemed to describe phenomena of different kinds.
Yet, some relationships, discovered earlier by the likes of Gay-Lussac, Avogadro, and Boyle [ 42 ], consistently crossed that divide, such as the proportionality of gas pressure a mechanical attribute and temperature a thermodynamic one in a container of fixed volume.
Specifically, temperature relates to the average quadratic velocity of a large number of microscopic particles and pressure to their momentum. These relationships intuitively match the observation that, when warming up one's hands by rubbing them together, the faster the movement, the higher the generated heat. Entropy is related to the number of possible states a system can be in, which clarifies why disorder tends to increase in the absence of other constraints.
The demonstration of the equivalence between thermodynamics and mechanics is as convincing and direct as the derivation of the corresponding thermodynamic and mechanical laws. In particular, the law of gas can be derived from Newton's formulas.
Furthermore, this link solved the mystery of the lower bound of temperature: Notably, the kinetic theory also led to the famous Einsteinian explanation of Brownian motion recounted in [ 45 ]. The phenomena of refraction and diffraction are fully explained by Maxwell's equations of electrodynamics once light is understood as electromagnetic wave.
Yet the properties of mirrors and the passage of light through various media such as air and water seem so different from the phenomena of electric current and magnetic dipoles.
Other illustrations of the same relationships are the quantum physics foundation of chemistry, the genomics bases of genetics, and the explanation of neuronal firing in terms of voltage-dependent sodium and potassium channels [ 46 ]. When Mendeleev compiled the first draft of the Periodic Table of the Elements, there was no physical justification of the observed proportions of chemical reactions: The atomic nucleus was discovered 42 years later, just 4 years after Mendeleev's death.
Thus his corrections, required to describe then-available data parsimoniously, de facto predicted the atomic number nearly half a century prior to its actual discovery. Today we readily accept that many chemical phenomena e. Is there a common thread in these seemingly disparate, if illustrious, precedents? We purport that the illusion of mystery at one level e. Here we surmise that the content and meaning of mental states, the most inescapable yet ineffable puzzle of human cognition, will eventually be understood as a direct reflection, if not simply an aspect, of brain computation, much like thermodynamics is statistical mechanics.
The principle of what would count as an explanation, however, remains the same: We are not simply proposing mental properties to be probabilistically supervenient on brain properties, that is, that they can be inferred statistically from brain measures within any given error rate [ 47 ]. On the contrary, we are asserting the possibility of a formal equivalence between the two, through all temporal scales and plastic changes [ 48 ]. The explanatory power of mathematical theory in neuroscience is recognized in principle [ 49 ], but the extent of its reach has not yet been fully realized, and the path forward has never been chartered before.
This is in stark contrast to the third simulation-based leg of scientific progress complementary to experiments and theorywhich is blossoming into maturity in computational neuroscience and cognitive modeling alike [ 50 ] and in the study of consciousness in particular [ 5152 ]. Neural Connectivity as the Most Informative Constraint in the Brain To explain the equivalence of brain and mind by mapping them onto each other, it is essential to identify the relevant levels of analysis in order to define proper mathematical formalisms for their quantitative description.
We start from the brain in this section and tackle the mind in the next.
Difference Between Mind and Brain
Nervous systems are gigantic networks of intercommunicating neurons. From the computational point of view, it matters relatively little that neurons are electrical devices. Instead, brain signal processing is fundamentally dependent on circuit connectivity [ 53 ]. Specifically, how neurons are connected to each other constrains network dynamics [ 54 ] and therefore determines the possible flow of information transmission [ 55 ].
Because real brains are wired to a certain degree according to stereotypical principles [ 57 ], the actual number of connectivity patterns that could be found in any one human brain is undoubtedly lower. However, brain circuitry is neither random nor regular, and the information content of a single human brain remains far greater than the number of fundamental particles in the whole universe, let alone just the complete biochemical specification of that individual brain.
Thus network connectivity is necessarily more informative than the entire molecular profile of each of all of its neurons, including the expression of every gene and protein constituting the biophysical machinery at the basis of neuronal electrophysiology.
While neuroanatomy provides the foundational roadmap of information transmission in nervous systems, neural activity is itself characterized by chaotic dynamics [ 58 ] typical of complex systems [ 5960 ]. As these aspects are particularly relevant to conscious brain function [ 61 ], a full understanding of the brain as it relates to mental content will have to integrate adequate accounts of both neural dynamics and connectivity [ 62 — 64 ].
Nevertheless, the network architecture specification is absolutely central to the assumed correspondence between spatial-temporal patterns of neural spiking and mental states. The former is further distinguished in the dense reconstruction of the entire synaptic matrix and the statistical potential of synaptic connectivity, both highly relevant to computational processing [ 71 — 73 ].
In contrast, the much coarser description of regional connectivity has less direct implications for a mechanistic understanding of brain cognition.
What is the difference between the mind and the brain?
However, this latter approach is also substantially more realistic to achieve in the near future, using existing histological techniques in animal models [ 7475 ] or noninvasive imaging in humans [ 7677 ]. This flurry of developments along with nonconventional approaches e. The branch of mathematics dealing with connectivity is graph theory. In light of the previous considerations, it is not surprising that graph theory has become a considerably popular topic in neuroscience e. It is remarkable that important properties of general graphs that have been found to apply to many types of networks, including random connections [ 87 ], small-world attributes [ 88 ], scale invariance [ 89 ], and motif distributions [ 90 ], are prominently relevant to neural circuits [ 91 — 94 ].
The application of graph theoretic analysis to neural circuit has already revealed a number of features, including network communities [ 95 ] and rich clubs [ 96 ], but also general principles of wiring economy [ 97 ] and network organization [ 98 ] as well as potential implications of circuit structure on signal communication [ 99]. It is important to stress that, while two cells are never exactly alike, neurons can be organized in distinct classes such that neurons within each class are much more similar to each other than across classes [ ].
Thus the statistical properties of brain connectivity are likely to be strongly determined at the level of connection probability among neuron classes. Initial progress is being made in the application of the relevant field of mathematics, stochastic block modeling, to this problem .
Two further facets are worth considering in the characterization of the brain in terms of its network connectivity. The first is the all-important issue of intersubject diversity. While in invertebrates it is sometimes possible to recognize the same individual neurons across subjects, in mammals it is not even possible to match the same types of neurons bilaterally within subject, such as in motor neurons innervating symmetric muscles [ ]. In humans, intersubject variability is already very considerable at the regional level [ ] and can be expected to be extraordinary large at the level of individual neurons across subjects.
The second vital element of brain circuitry is structural plasticity, that is, dynamical changes in the synaptic connectivity not just during development but throughout adulthood. Abundant experimental evidence suggests that this form of plasticity is activity- and experience-dependent [ — ]. This is just one of many mechanisms underlying neural plasticity across spatial and temporal scales, from short- or long-term alterations in synaptic strengths to neurogenesis [ ], which are believed to support memory storage .
Much as the brain is in constant flux and its functional connectivity continuously changes with every spike and synaptic discharge, so is the mind never exactly the same before and after instantiating each and every subjective representation.
Quantifying Declarative Mental States: But it is somehow even more peculiar that in spite of direct, detailed, continuous, and complete access to each and all conscious mental states we experience, we find it difficult to describe them comprehensively, let alone quantitatively.
Indeed, it seems absurd that we can measure the concentration of Substance P in single neurons to the fifth significant digit; yet we can only measure the resulting sensation of pain semiqualitatively on a 7-point discrete scale. In order to bring the study of conscious content into the realm of hard science, we need to devise a quantitative measurement system for subjective states [ ]. Language has often been considered a convenient proxy to access mental states, if not the most direct tool to describe them.
The scientific characterization of the meaning of language, or semantic analysis, has a long history and remains one of the most active research areas in computational linguistics. Here we do not aim to review or even to provide a balanced commentary on the state of the art of semantic analysis techniques.
Instead, we introduce and explain a very specific, nonconventional approach to this problem that is particularly pertinent to the topic of this spotlight paper. Most if not all of the best known computational methods of semantic analysis are based on variations of the common principle that the meaning of words relates to the contextual occurrence of their use in language [ ].
For example apples, oranges, and grapes tend to be used in similar contexts as reflected by their cooccurrence with similar words in the same sentence e. Thus, they share similar semantics they are all types of fruit.
The notion that word meaning relates to the relative frequency of their cooccurrence is shared by many broadly adopted approaches, including Latent Semantic Indexing [ ], Latent Dirichlet Allocation [ ], Hyperspace Analogue to Language [ ], and many others [ ]. In practice, these techniques rely on the identification of statistical patterns of word usage in large-scale text corpora by computational parameter extraction. Although the details vary among types of computational semantic analysis, words or more generally, concepts are often allocated to a multidimensional abstract space such that the location of each concept reflects its meaning.
Alternatively, meanings can be identified with clusters of words in this space. For instance, all fruit words in the previous example would be located in the same region of the space.
By nature of its own principle, latent semantic analysis and its variations generate results that are highly context dependent. In other words, the semantics extracted from a cookbook are typically quite different from those detected in movie reviews or obituaries. In fact, use of nonhomogeneous collections of corpora from different domains typically fails to yield meaningful semantics. Moreover, this general class of methodologies tends to produce a large number of highly specific dimensions.
A rather complementary and historically precedent goal of lexical semantics has been to seek the fundamental or at least context independent dimensions of word meaning. In that work, subjects were asked to rate a large number of words in various hand-picked dimensions defined by two opposite extremes e.
Subsequent analysis identified three principal dimensions that were robust to cultural and geographical differences, namely, evaluation also known as valence: A limitation of these studies and other similar psychometric approaches [ ] is that they involve human subjects and arbitrary choices of starting terms.
Thus, they are not amenable to automated, high-throughput computational extraction. Word meaning has of course also been characterized for thousands of years in many languages and cultures through the creation of dictionaries.
Here the beginning of modern times can be considered to correspond to the systematic but again, ultimately arbitrary classification introduced by Roget's Thesaurus of English words and phrases, now accessible online years after its original publication [ ].
Among contemporary efforts, the most comprehensive academic resource is Princeton's WordNet [ ].
Difference Between Mind and Brain | Difference Between | Mind vs Brain
Researchers in computational linguistics are vigorously pursuing the topic of conceptual ontologies [ ]. Yet, it remains to be established if and how formal ontological theories could map semantic spaces such as those generated by latent semantic analyses.
Specifically, using a novel self-organization process, we constructed a semantic map of natural language that simultaneously represents synonymy and antonymy. Synonyms and antonyms are commonly listed in dictionaries for most terms. We extracted these relationships from digitally accessible dictionaries Microsoft Word and Princeton's WordNet in each of several languages English, French, German, and Spanish.
For each dictionary and language, we initially allocated words at random locations in a finite, multidimensional spherical space. Then we started moving the position of every word following a simple rule: Thus, pairs of synonyms would tend to move closer to each other, and pairs of antonyms would move farther apart within the bounds of the multidimensional sphere.
Most importantly, the emergent semantics of the map's principal components are clearly identifiable: The semantic map is sufficiently robust to allow the automated extraction of synonyms and antonyms not originally present in the dictionaries used to construct the map, as well as to predict connotation from their coordinates.
The map's geometric characteristics include a bimodal distribution of the first component, increasing kurtosis of subsequent unimodal components, and a U-shaped maximum-spread planar projection.
Both the semantic content and the main geometric features of the map are consistent between dictionaries, among tested Western languages, and with previously established psychometric measures. Some of the mathematical formalism and speculative interpretations are elaborated in a second follow-up paper [ ]. Interestingly, the main emerging dimensions of this semantic map loosely correspond to the primary modulatory neurotransmitter systems in the mammalian brain [ ].
The previous paradigm can be expanded with appropriate adaptations to extract additional, independent dimensions of word meaning by considering other linguistic relations besides synonyms and antonyms.
However, hypernyms and hyponyms are seldom listed in immediately machine-readable form in digital collections, the way synonyms and antonyms are. One exception is provided once again by WordNet, which explicitly provides is-a relationships among many of its terms. Unlike synonyms and antonyms, which are symmetric relations if A is synonym of B, B is synonym of Ahypernyms and hyponyms are directional and mutually antisymmetric if A is hypernym of B, B is hyponym of A.
We thus changed the form of the energy functional in the previously described optimization procedure [ ]. The resulting allocation of words in space yielded a ranking of all terms along a single dimension, that is, a simple scalar measure of their abstractness ontological generality.
The bottom 11 ex aequo of the list reads Edmontonia, Coelophysis, Deinocheirus, Struthiomimus, Deinonychus, dromaeosaur, Mononykus olecranus, oviraptorid, superslasher, Utahraptor, and Velociraptor. Moreover, because the measure is quantitative, it allows evaluation of relative comparisons. This opens the possibility to establish a probabilistic estimate of whether a word is more abstract than another.
The metrics of context-independent word meaning along the principal dimensions described previously can be applied to characterize declarative mental states. The most straightforward application is to quantify the content of verbal examples along the main dimensions of the map.
This can help in relating semantic content to neural signals. It should be noted that the semantic map described here represents a complementary, rather than alternative, tool to more established latent semantic analyses. While maps produced by the latter are corpus and context dependent, this space adds general dimensions that are applicable to all corpora and context. We indeed found that the first dimension good-bad was an excellent quantitative predictor not only of the movie critique score but also together with the second dimension of its genre high valence and arousal: A Radical View of Reality, Information, Consciousness, and Remaining Challenges Semantic mapping provides a possible approach to quantifying mental states that can be expressed declaratively.
In this framework, mental states and their relationships can be themselves represented as graphs of nodes and edges, respectively. If one believes that at least some mental states reflect properties of outer reality, it is possible to conceive reality itself as occurring in a giant graph in which any possible observable is a node, and edges correspond to probabilities that two observables would cooccur. We call this conceptual construct the Universal Reality Graph. In this view, reality would unfold in time as a sequence of events constituting patterns of activation of subsets of nodes and all edges among them within the University Reality Graph.
Any agent capable of observation will witness a subset of these activation patterns, that is, a sequence of partial events, each consisting of a collection of active nodes and edges. Most importantly, the possibility to conceive reality as a graph offers interesting vistas on the solution of the mind-brain problem.
If agents form graph-like minds to represent and therefore predict their experience of graph-like reality, it stands to reason that the fittest physical substrates selected by evolution to encode these representations be themselves graph like, namely, brain networks.
- The Mind-Brain Relationship as a Mathematical Problem
- What’s The Difference Between The Mind And Brain?
The relationship between minds and brains could then be resolved as a mapping between their respective graphs and their embeddings. In this framework, the fundamental operation to grow a mind is pairwise association between observables [ ], that is, establishment of edges between nodes in the mental graph based on corresponding experiences in the reality graph.
An interesting aspect of mental representation is that we only learn a small fraction of associations from those observed in reality. In particular, our ability to learn is gated by previously acquired background information.
We have recently proposed that this constraint may be a consequence of the spatial relationship among the tree-like shaped neuronal axons and dendrites that underlie brain connectivity .
Specifically, in order for new synapses to be formed, the axon of the presynaptic neuron must be sufficiently close to a dendrite of the postsynaptic neuron, arguably because of preexisting connectivity with other neurons encoding for related knowledge. These ideas are also consonant with the Information Integration Theory IIT of consciousness [ ], which is emerging as a leading candidate among the fundamental theories of mental content. The underlying assumption of IIT is that consciousness is fundamentally a property of information processing.
This happens through the spoken or written word. In person, this transfer also happens through eye contact, facial expression, body language, posture, and gesture. The New Science of Personal Transformationhe explains: Our minds are created within relationships — including the one that we have with ourselves… Each of us has a unique mind: These patterns shape the flow of energy and information inside us, and we share them with other minds.
The third point on the triangle is the mind which is the process that regulates this flow of information and energy. In Mindsight, Seigel writes: Consider the act of driving.
No matter how hard you try. The mind observes and monitors the flow of energy and information across time while modifying it by giving it characteristics and patterns. Because of neuroplasticitythe capacity of the brain to create new neural connections and grow new neurons in response to thoughts and experience, each point on the triangle influences the others, and the flow of energy and information along this triangle goes in all directions. The mind can change the structure of the brain and relationships.
The brain can change the structure of the mind and relationships. Relationships can change the mind and the brain. Mindsight integrates the different parts of the system to cultivate well-being or mental health. On his website, Siegel explains that: The ultimate outcome of integration is harmony.
The absence of integration leads to chaos and rigidity—a finding that enables us to re-envision our understanding of mental disorders and how we can work together in the fields of mental health, education, and other disciplines, to create a healthier, more integrated world.Mind Brain Relationship
We all can develop mindsight through mindfulness practices. Mental health practitioners, educators, and parents can take advantage of this information to promote more compassionate, kind, resilient, emotionally intelligent, and mentally healthy individuals.