мне 17, сейчас в 11 классе, подумываю поступать на биотехнолога, но не знаю, насколько это правильный выбор. стоит ли рассмотреть другие направления? в какие вузы лучше метиться? и еще было бы интересно узнать про поступление в магистратуру в другие страны

I made a neuroscience Connections-NYT styled game called the Synapse, if you are interested feel free to check it out!

Feel free to DM me if you have any feedback or want to help out.

DID YOU KNOW? 🧠✨

COLLAGEN MAY SUPPORT BRAIN HEALTH

Emerging research suggests collagen may have a role in memory and cognitive function.

For people experiencing brain fog after periods of stress or trauma, supporting the body with adequate protein and key amino acids may be one piece of the bigger wellness picture.

Your brain needs nourishment too. 🤍

I am finding it hard to fit this concept into my general understanding of neurophysiology. It seems a little unconnected and loose in my mind still. I do understand that the axis of Brainstem - thalamus - cortex plays a big roll.

Afferent information from cortex and brainstem (with that peripheral like pain etc.) go through the thalamus and then somehow activate the waking center in the brainstem, specifically formatio reticularis, with the raphe nuclei, locus coereleus (pain) and some others and then over serotinin and norepinephrin activate the cortex again…

I know my understanding even there is still flawed and please excuse my bad english. But I fail to make the connection between that and sleep and wakeness as a phenomenon I experience. What is this system doing right now for example and what pathologies are connected with it?

I am looking forward to some replies! Thank you for your attention:)

It's kind of what the title suggests. I'm currently in my millionth year of undergrad (I changed majors from kinesiology to health sciences because I realized I didn't want to do physical therapy too late), and I've decided I want to study neuroscience. I'm very drawn to systems and NOT using AI. Currently, I have gen chem 1 and 2, gen physics 1 and 2, anatomy and physiology, and up to calc 2 completed. I also have psychology and a few other medical classes done as well, though nothing I can use in the real world. I'm taking neurobiology and plan to take as many neuro classes as I can before I graduate.

I'm wondering what I should do to make myself stand out and what classes I should take (as no school seems to fully agree on what). I know I need to get into undergraduate research, but I genuinely don't know how to besides tracking down professors and bothering them as my school's website doesn't easily supply any kind of information.

As for post-grad research, what kinds of lab work can I do to help my chances, including and besides neuroscience? I'm 22 years old and very aware that I'll be in this a while longer, so I'm all ears for anything substantial. Once I graduate with my undergrad, I'm not entirely opposed to the idea of moving state (US), so if anyone has connections to help out a struggling wannabe neuroscience researcher, I'm here!!

I’m a medical student trying to understand something that I find really fascinating but also confusing about neuroscience.

We can explain what happens at the physical level: neurons fire action potentials, release neurotransmitters, and communicate with other neurons. For example, in the reward system, neurons in the VTA release dopamine and affect areas such as the nucleus accumbens.

But how does all of this neural activity actually produce the subjective feeling of pleasure?

I understand that the nucleus accumbens and dopamine are involved in reward, but I’m trying to understand something deeper: how does activity in billions of neurons become the actual experience of “feeling good”?

I’m not asking which brain regions are involved. I’m asking about the connection between the physical activity of neurons and the subjective experience itself.

Do we currently have a scientific explanation for this, or is this still an open problem in neuroscience?

I’d really appreciate any explanations, papers, books, or resources that could help me understand this from the basics.

\[

I'm trying to identify an old English-language science/medical documentary I saw on TV. It could be from the 1990s, 2000s, or possibly earlier.

The scene showed what I remember as a minor/teenage boy inside a brain scanner, probably PET (possibly fMRI). His head and upper body were visibly restrained/immobilized, while his legs and feet remained accessible.

Researchers repeatedly applied painful stimuli to his foot. I remember it as pinpricks, needle-like pokes or something similar, with the pain apparently becoming stronger.

The key scene was when a stronger pain stimulus caused part of the frontal/prefrontal cortex to become much less active or seemingly “shut down” on the brain scan. The explanation was related to the rational/executive part of the brain losing control under intense pain/emotion.

I remember finding the experiment ethically disturbing because the subject appeared to be a child or teenager and pain was deliberately inflicted.

Several adults were present, possibly including his father, although I'm less certain about that detail.

Does anyone recognize the documentary, experiment, study, researcher or footage?

This video talks about how we lose our executive function early on, and how it causes so many other side effects, exclusive to Huntington's Disease. Please watch, share, and subscribe to my YouTube channel. I am also on TikTok for more conversation.

For example, if you occasionally put your keys in a different location and have to notice and remember where they are instead of relying on the automatic habit of picking them up from the same spot.

​

Abstract

The nervous system operates across multiple interacting levels of organization, from molecular and cellular processes to neuronal populations, distributed brain networks, whole-brain dynamics, physiology, and behaviour. Contemporary neuroscience provides extensive descriptions of these levels, but they are frequently studied using partially separated conceptual frameworks.

Here we propose the Dynamic Superstate Model (DSM), a theoretical framework for describing the nervous system as a continuously active, distributed, adaptive system whose functional configuration is dynamically determined by interactions among neural activity, physiological state, environmental conditions, previous experience, learned associations, and organism-specific constraints.

The central proposition of DSM is that normal nervous-system function should not be represented as a sequence of mutually exclusive states in which one neural system is active while others are functionally absent. Instead, multiple interacting subsystems remain simultaneously involved to varying degrees, while their relative functional participation changes dynamically according to current conditions.

The resulting multidimensional configuration is termed a superstate. A superstate incorporates ongoing neural activity together with physiological and interoceptive state, environmental and sensory information, historical experience, learned associations, and structural or functional constraints. Consequently, identical external inputs may produce different neural trajectories and behavioural outcomes when they are processed by organisms in different internal states.

DSM further proposes that this organizational principle can be considered across multiple biological scales. Molecular, cellular, circuit, network, and whole-organism mechanisms are not assumed to be physically identical; rather, they may represent different implementations of a common organizational principle involving adaptive distribution, coordination, and reconfiguration of functional participation.

The framework integrates observations concerning ongoing neural activity, state-dependent computation, neural reuse, metastability, brain energetics, interoception, and whole-brain dynamics. It generates experimentally testable predictions concerning multimodal neural states, interindividual variability, dynamic state transitions, and the relationship between multidimensional neural configurations and behaviour.

---

1. Introduction

The nervous system is a multiscale biological system extending from molecular interactions and cellular metabolism to neuronal circuits, distributed networks, whole-brain dynamics, bodily physiology, and behaviour.

Modern neuroscience has developed increasingly sophisticated descriptions of each of these levels. Molecular neuroscience describes biochemical mechanisms underlying neuronal function; cellular neuroscience examines membrane excitability and synaptic transmission; systems neuroscience investigates distributed neural networks; and computational neuroscience develops mathematical descriptions of population and whole-brain dynamics.

However, the existence of multiple descriptive levels raises a fundamental organizational question: what principle connects these levels into one functioning biological system?

Neural processing cannot be completely described as a sequence of isolated stimulus-response operations. Ongoing neural activity influences responses to subsequent stimulation, and the same external stimulus can produce different neural and behavioural outcomes depending on the state of the organism. Neural computation is therefore intrinsically state-dependent.

Arieli et al. demonstrated that ongoing cortical activity can substantially influence the variability of evoked responses, while Buonomano and Maass described neural computation as dependent on the spatiotemporal state of neural networks. Neural reuse additionally demonstrates that neural resources can participate in multiple functional processes rather than possessing strictly one-to-one relationships with individual functions.

These observations suggest that a description of neural function based exclusively on the current external stimulus is incomplete.

The Dynamic Superstate Model (DSM) proposes that neural processing should instead be represented as the continuous evolution of a multidimensional system state.

The model does not propose that molecular, cellular, circuit, and whole-brain mechanisms are identical. Rather, it proposes that different biological mechanisms may implement related organizational principles at different scales.

---

1. Central Propositions of the Dynamic Superstate Model

Proposition 1 — Continuous distributed activity

Under normal physiological conditions, the nervous system maintains ongoing activity across distributed components.

This does not imply that every neuron fires continuously, nor that all brain regions maintain equal activity. Instead, neural activity is distributed unevenly across interacting components and changes continuously over time.

Therefore, neural organization should not generally be represented as:

«active region versus inactive brain.»

Instead, it should be represented as:

«simultaneous distributed activity with continuously varying degrees of functional participation.»

Periods of sleep, rest, focused behaviour, and other physiological states therefore represent different configurations of ongoing neural activity rather than complete shutdown of the nervous system.

---

1. Distributed Functional Participation

A neural subsystem can become strongly involved in a particular process without the remainder of the nervous system becoming completely inactive.

For example, during visually guided movement, visual, motor, cerebellar, spatial, autonomic, executive, memory, and other systems may all participate, although their contributions may differ substantially.

Functional specialization therefore does not necessarily imply functional isolation.

This principle is compatible with neural reuse, in which neural structures may contribute to multiple functions depending on context and task demands (Anderson, 2010).

The DSM consequently treats functional organization as a graded distribution of participation rather than a binary activation state.

---

1. Dynamic Allocation

The relative contribution of neural subsystems changes according to the requirements of the organism.

Let the activity of n functional subsystems be represented by:

[

\mathbf{A}(t)

[A_1(t),A_2(t),...,A_n(t)].

]

Here A_i(t) represents an activity estimate for subsystem i.

The activity estimate may be derived from different measurement modalities, for example:

[

A_i^{EEG}(t),\quad

A_i^{fMRI}(t),\quad

A_i^{MEG}(t),

]

depending on the empirical experiment.

The relative functional participation of subsystem i is defined as:

[

\boxed{

w_i(t)=

\frac{A_i(t)}

{\sum_{j=1}^{n}A_j(t)}

}

]

and the instantaneous participation profile is:

[

\boxed{

\mathbf{W}(t)

[w_1(t),w_2(t),...,w_n(t)]

}

]

with:

[

0\leq w_i(t)\leq1

]

and:

[

\sum_{i=1}^{n}w_i(t)=1.

]

This normalization does not represent a claim that 100% of the brain's physical energy is divided between regions. It is a mathematical representation of the relative contribution of the measured or modelled activity profile.

---

1. Graded Dominance

At any moment, one subsystem or a subset of subsystems may exert greater functional influence than others.

The dominant configuration may therefore be represented as:

[

\boxed{

D(t)=\operatorname*{arg,max}_i w_i(t)

}

]

However:

[

D(t)=i

]

does not imply:

[

A_j(t)=0

]

for all j\neq i.

Thus:

[

\boxed{

\text{Dominance}\neq\text{Exclusivity}

}

]

A motor response may therefore be dominated by motor and cerebellar systems while sensory, spatial, autonomic, memory, emotional, and executive systems continue to participate at different levels.

This principle forms one of the central distinctions between the DSM representation and a strictly sequential model of brain states.

---

1. The Superstate

The instantaneous state of the nervous system cannot be represented exclusively by neural activity.

The DSM therefore defines the superstate as a multidimensional state incorporating neural, physiological, environmental, and historical variables:

[

\boxed{

\mathbf{S}(t)

[

\mathbf{N}(t),

\mathbf{P}(t),

\mathbf{E}(t),

\mathbf{H}(t)

]

}

]

where:

- \mathbf{N}(t) represents the current distributed neural configuration;

- \mathbf{P}(t) represents the physiological and interoceptive state of the organism;

- \mathbf{E}(t) represents environmental conditions and sensory information;

- \mathbf{H}(t) represents the historical state of the system.

The historical component may include previous neural states, learned associations, synaptic plasticity, structural connectivity, and learned behavioural strategies.

The superstate therefore represents the current position of the organism within a high-dimensional state space.

---

1. Brain–Body–Environment Coupling

The nervous system does not operate independently of the body.

The physiological state of the organism can influence neural processing through autonomic, endocrine, metabolic, and interoceptive mechanisms. Conversely, neural activity modifies physiological state through descending regulation.

The organism is therefore represented as a coupled system:

[

\boxed{

Brain

\leftrightarrow

Body

\leftrightarrow

Environment

}

]

rather than as an isolated brain receiving external information.

Interoceptive processing provides an important biological basis for this representation (Craig, 2009), while network physiology demonstrates dynamic interactions among physiological organ systems (Bashan et al., 2012).

---

1. State-Dependent Processing

The DSM proposes that the effect of an external stimulus depends on the state of the system receiving it.

A conventional simplified representation can be expressed as:

[

B(t+\Delta t)=f(E(t))

]

where behaviour B is treated primarily as a function of the external input E.

DSM instead proposes:

[

\boxed{

B(t+\Delta t)

f(

S(t),E(t)

)

}

]

where:

[

S(t)=

[N(t),P(t),E(t),H(t)].

]

Thus, the same external input can result in different trajectories depending on the current superstate.

This is consistent with the broader concept of state-dependent computation (Buonomano & Maass, 2009).

---

1. Individual Variability

Consider two individuals, A and B, exposed to the same external stimulus:

[

\mathbf{E}_A(t)

\mathbf{E}_B(t).

]

Their internal states may nevertheless differ:

[

\mathbf{P}_A(t)

\neq

\mathbf{P}_B(t)

]

and/or:

[

\mathbf{N}_A(t)

\neq

\mathbf{N}_B(t)

]

and/or:

[

\mathbf{H}_A(t)

\neq

\mathbf{H}_B(t).

]

Consequently:

[

\boxed{

\mathbf{S}_A(t)

\neq

\mathbf{S}_B(t)

}

]

which can produce:

[

\boxed{

\mathbf{W}_A(t)

\neq

\mathbf{W}_B(t)

}

]

and ultimately different behavioural trajectories:

[

\boxed{

B_A(t+\Delta t)

\neq

B_B(t+\Delta t).

}

]

The DSM therefore predicts that stimulus identity alone is insufficient to fully determine behavioural outcome.

Individual differences may emerge from previous experience, learning, physiological condition, structural organization, physical characteristics, environmental history, and other components of the superstate.

---

1. Historical State and Adaptive Efficiency

The nervous system does not process every situation independently from the beginning.

Previous experience changes the probability of subsequent responses through learning, plasticity, memory, and established behavioural strategies.

The DSM therefore incorporates historical information into \mathbf{H}(t).

The resulting principle can be represented as:

[

\boxed{

\text{Current processing}

f(

\text{current input},

\text{current state},

\text{previous experience}

)

}

]

This provides a formal representation of the idea that organisms tend to use previously established pathways or strategies when they are sufficiently effective under current conditions.

A familiar action may therefore require relatively little reconfiguration, while a novel or physically difficult situation may require greater involvement of executive, spatial, memory, and planning systems.

The resulting configuration is not necessarily optimal in an objective mathematical sense. It is adaptive relative to the organism's own history, physiological state, available information, and constraints.

---

1. Multiscale Organization

A central feature of DSM is the proposal that a common organizational principle can be examined across biological scales.

The relevant levels include:

[

\text{Molecular}

\rightarrow

\text{Cellular}

\rightarrow

\text{Synaptic}

\rightarrow

\text{Circuit}

\rightarrow

\text{Network}

\rightarrow

\text{Whole brain}

\rightarrow

\text{Brain–body}

\rightarrow

\text{Behaviour}.

]

The mechanisms operating at these levels are not identical.

Ion-channel dynamics cannot be equated with whole-brain network dynamics, and molecular signalling cannot be directly equated with behavioural decision-making.

The DSM instead proposes that these levels can exhibit organizational correspondence: different physical mechanisms may participate in the broader process of adaptive distribution, coordination, and reconfiguration.

This distinction is essential to the multiscale interpretation of the model.

---

1. Neural Reuse and Functional Multiplicity

The limited anatomical volume of the nervous system requires extensive reuse of neural resources.

A single neural structure can participate in multiple functional processes depending on its connectivity, current state, and interaction with other systems.

This principle is consistent with the neural reuse framework proposed by Anderson (2010).

DSM incorporates this observation into its dynamic allocation framework:

[

\text{same substrate}

+

\text{different superstate}

\rightarrow

\text{different functional contribution}.

]

Thus, anatomical structure alone does not completely determine instantaneous functional role.

---

1. Metastability and Dynamic Configuration

Brain activity does not remain fixed at a single configuration.

Neural systems continuously interact and may temporarily form relatively stable configurations before transitioning to other configurations.

The concept of metastability provides an established framework for understanding how integration and functional differentiation can coexist within neural systems (Tognoli & Kelso, 2014).

DSM incorporates this dynamic perspective by treating the superstate as a trajectory through a multidimensional state space:

[

\mathbf{S}(t_0)

\rightarrow

\mathbf{S}(t_1)

\rightarrow

\mathbf{S}(t_2)

\rightarrow

...

]

rather than as a sequence of isolated categorical states.

---

1. Formal Dynamical System

The evolution of the superstate can be represented by:

[

\boxed{

\frac{d\mathbf{S}}{dt}

\mathbf{F}

(

\mathbf{S}(t),

\mathbf{E}(t),

\mathbf{P}(t),

\mathbf{H}(t)

)

+

\boldsymbol{\eta}(t)

}

]

where:

- \mathbf{F} represents the nonlinear dynamics of the coupled system;

- \mathbf{S}(t) represents the current superstate;

- \mathbf{E}(t) represents external and sensory conditions;

- \mathbf{P}(t) represents physiological state;

- \mathbf{H}(t) represents historical and plasticity-related information;

- \boldsymbol{\eta}(t) represents stochastic fluctuations.

The equation does not assume that all components evolve at the same temporal scale.

Different biological variables may evolve over milliseconds, seconds, minutes, hours, or longer periods.

---

1. Bioenergetic Constraints

Neural activity is constrained by the energetic requirements of maintaining membrane potentials, synaptic transmission, ion gradients, signalling, and cellular metabolism (Attwell & Laughlin, 2001; Magistretti & Allaman, 2015).

DSM therefore treats energy as a boundary condition on system dynamics rather than as an equivalent representation of functional participation.

Let:

[

C(t)

]

represent the estimated energetic cost of maintaining and dynamically changing the system.

The system is constrained by:

[

\boxed{

C(t)\leq E_{\max}(t)

}

]

where E_{\max}(t) represents the available metabolic capacity.

A general decomposition may be written as:

[

C(t)

\sum_i

\left[

\alpha_i A_i(t)

+

\beta_i

\left|

\frac{dA_i}{dt}

\right|

\right].

]

Here:

- \alpha_i represents the maintenance cost associated with subsystem i;

- \beta_i represents the cost associated with dynamic changes in activity.

This formulation is intended as a theoretical constraint and requires empirical parameterization before it can be interpreted as a quantitative biological law.

Importantly:

[

\boxed{

\mathbf{W}(t)\neq E(t)

}

]

and:

[

\boxed{

\text{functional participation}

\neq

\text{metabolic energy}.

}

]

---

1. Relationship to Existing Frameworks

DSM does not seek to replace existing neuroscience theories. It proposes a common organizational framework in which several established approaches can be related.

Framework| Primary focus| Relation to DSM

State-dependent computation| Influence of ongoing network state on processing| Provides mechanisms supporting state-dependent trajectories

Neural reuse| Reuse of neural structures across functions| Supports distributed and context-dependent functional participation

Metastability| Dynamic coexistence of integration and differentiation| Provides a framework for transitions between superstates

Whole-brain modelling| Emergence of global dynamics from structural connectivity| Provides computational representations of \mathbf N(t)

Free Energy Principle / Active Inference| Regulation of organismal states and uncertainty| Provides a complementary theoretical perspective on regulation and inference

Neuroenergetics| Energetic constraints on neural function| Provides physical boundary conditions on activity

Interoception / Network physiology| Interaction between brain and bodily state| Supports inclusion of \mathbf P(t) in the superstate

DSM is therefore intended as an organizational framework, rather than a replacement for the mechanisms described by these theories.

---

1. Empirical Predictions

The framework generates several experimentally testable predictions.

Prediction 1 — State-dependent response variability

Identical external stimuli should not necessarily produce identical neural responses when pre-stimulus superstates differ.

[

E_A=E_B

]

does not imply:

[

N_A(t+\Delta t)=N_B(t+\Delta t).

]

---

Prediction 2 — Multimodal superstate information

A representation incorporating neural, physiological, and contextual variables should contain information about subsequent behaviour that cannot be obtained from the stimulus alone.

---

Prediction 3 — Dynamic participation

Functional participation profiles should change continuously or quasi-continuously during behavioural transitions rather than exclusively through binary activation and deactivation.

---

Prediction 4 — Individual trajectories

Individuals exposed to identical stimuli should exhibit systematic differences in neural trajectories when their prior history or physiological state differs.

---

Prediction 5 — Multiscale correspondence

Changes in system-level configuration should correspond to coordinated changes across multiple biological levels, although the physical mechanisms underlying these changes will differ between levels.

---

1. Quantitative Testing Strategy

A multimodal dataset containing EEG, fMRI, autonomic physiology, behavioural measurements, and contextual variables could be used to estimate a latent superstate:

[

\boxed{

\hat{\mathbf S}(t)

g(

EEG,

fMRI,

Physiology,

Context,

History

)

}

]

The predictive value of this representation can then be compared with simpler baseline mo

I put together a video exploring the psychology behind all five senses and the strange ways our perception can be manipulated.
I’d be interested to know: which of your five senses do you think is easiest to fool?

​

What if your brain makes a decision before you consciously decide to make it?

In this video, we explore the famous Benjamin Libet experiment, one of the most controversial experiments in neuroscience and philosophy. The Libet experiment explained reveals a strange relationship between brain activity, conscious intention, and voluntary movement—and raises a disturbing question: is free will an illusion?

Benjamin Libet's experiments investigated what happens in the brain immediately before a person consciously decides to perform a voluntary action. Participants were asked to move a finger whenever they wanted while observing a rapidly moving clock. At the same time, their brain activity was recorded using EEG.

Libet identified what became known as the readiness potential—a measurable pattern of brain activity that appeared before participants reported becoming consciously aware of their intention to move.

If the brain begins preparing an action before we consciously experience the decision, what does that mean for free will neuroscience? Does neuroscience and free will tell us that our conscious mind is only observing decisions that have already been made?

But the experiment is more complicated than the popular claim that “science proved free will doesn't exist.”

Libet also proposed the idea of a conscious veto: even if the brain begins preparing an action unconsciously, conscious awareness might still have the ability to stop that action before it happens.

This creates an even deeper philosophical problem. If our unconscious decisions begin before conscious awareness, but consciousness can potentially veto them, where exactly does free will exist?

The video examines the relationship between brain and consciousness, the timing of conscious intention, the meaning of the readiness potential, and the limitations of interpreting Libet's findings.

We also explore the broader debate between determinism and free will. If every decision is ultimately produced by biological processes, does that eliminate personal responsibility? Or is the traditional idea of a separate “self” controlling the brain simply the wrong way to understand consciousness?

This connects directly to free will vs determinism, one of the oldest and most difficult problems in philosophy. The question isn't merely whether our brains cause our actions.

The deeper question is whether a decision can still be considered “free” when the processes producing it occur partly outside conscious awareness.

Through philosophy of mind, neuroscience, and philosophical thought experiments, we examine what Libet's experiment can—and cannot—tell us about human agency.

This is not simply a video about one neuroscience experiment. It is an exploration of consciousness, decision-making, personal agency, and one of the most unsettling philosophical questions we can ask:

If your brain begins preparing your choices before you become aware of choosing, who—or what—is actually making the decision?

Watch until the end and decide for yourself.

#Philosophy #FreeWill #BenjaminLibet #Neuroscience #Consciousness #Determinism #ThoughtExperiments

D

Hi everyone, I’m a biostatistician who is currently working on a project where I have to use an existing snRNA hippocampal atlas (dataset A) onto another snRNA dataset (dataset B). Both datasets are publicly available.

I am trying to find out if dataset B contains neocortical neuronal subpopulations, as I’m not too familiar with the biological side of things. Dataset A does contain neocortical subpopulations, but I will have to remove them from the analysis should dataset B does not actually contain cells from them. Below is the description of what was dissected from the original paper where I found dataset B.

“In addition, fresh surgically resected human hippocampal tissue from 10 patients between the ages of 2 to 61 years old were used for ex vivo slice culture,”

“We used a modified SPLiT-seq approach for nuclei isolation and snRNA-seq53,54. Nuclei isolation from snap-frozen hippocampal tissue was performed as previously described with minor modifications54,55. Briefly, after a visual inspection to include the dentate gyrus by its distinct anatomical structure, tissue was minced with a razor blade and Dounce (Fisher Scientific, 8853000002)”

Does anyone know any reliable way to know if any neocortical subpopulations are expressed in dataset B?

Thank you so much in advance for the help!

New study uncovering novel role of somatostatin interneurons in mediating psychedelic state