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Fabrizio Musacchio

@FabMusacchio@mastodon.social · 18 hours ago

#NeuralDynamics is a central subfield of #ComputationalNeuroscience studying timedependent #NeuralActivity and its governing #mathematics. It examines how #NeuralStates evolve, how stable or unstable patterns arise, and how #learning reshapes them. Neural dynamics forms the backbone for how #neurons & #NeuralNetworks generate complex activity over time. This post gives a brief overview of the field & its historical milestones:

🌍https://www.fabriziomusacchio.com/blog/2026-02-04-neural_dynamics/

#CompNeuro #Neuroscience #DynamicalSystems

3 media

Phase plane (left) of an action potential generated by the FitzHugh–Nagumo model. Neural dynamics is largely concerned with understanding how such action potentials arise from the underlying biophysical and network dynamics. However, it also goes beyond and studies the dynamics of, e.g., neuronal populations, synaptic plasticity, and learning. In this post, we provide a definitional overview of the field of neural dynamics in order to situate it within the broader context of computational neuroscience and clarify some common misconceptions.

Spiking activity in a recurrent network of model neurons (Izhikevich model). Shown are the spike times of all neurons in a recurrent spiking neural network as a function of time. The network consists of 800 excitatory neurons with regular spiking (RS) dynamics and 200 inhibitory neurons with low-threshold spiking (LTS) dynamics, separated by the horizontal line. Each vertical mark corresponds to an action potential (spike) emitted by a single neuron. In the context of neural dynamics, this representation illustrates how single-neuron events, such as the action potentials described above, combine to form structured, time-dependent activity patterns at the network level. Such spiking rasters provide a direct link between microscopic neuronal dynamics and emerging population activity, which can later be analyzed in terms of collective states, low-dimensional structure, and neural manifolds.

Two complementary perspectives on population activity in neural dynamics. The figure contrasts a “circuit” perspective with a “neural manifold” perspective. In circuit models, neurons are organized in an abstract tuning space, where proximity reflects tuning similarity, and recurrent connectivity
W
i
j
together with external inputs generates time-dependent firing rates
r
i
(
t
)
(panels A–C). In the neural manifold view, the joint activity vector
r
(
t
)
∈
R
N
of a recorded population evolves along low-dimensional trajectories embedded in a high-dimensional space (panels D–F). This is illustrated by ring-like manifolds for head-direction representations and by rotational trajectories in motor cortex, both of which can often be captured by a small number of latent variables
κ
1
(
t
)
,
…
,
κ
D
(
t
)
with
D
≪
N
. In the context of our overview post here, I think, the figure highlights very well why neural dynamics naturally connects mechanistic network modeling with state-space descriptions of population activity. These are not competing accounts, but complementary levels of description that emphasize different aspects of the same underlying dynamical system. Source: Figure 1 from Pezon, Schmutz, Gerstner, Linking neural manifolds to circuit structure in recurrent networks, 2024, bioRxiv 2024.02.28.582565, DOI: 10.1101/2024.02.28.582565ꜛ (license: CC-BY-NC-ND 4.0) — Two complementary perspectives on population activity in neural dynamics. The figure contrasts a “circuit” perspective with a “neural manifold” perspective. In circuit models, neurons are organized in an abstract tuning space, where proximity reflects tuning similarity, and recurrent connectivity W i j together with external inputs generates time-dependent firing rates r i ( t ) (panels A–C). In the neural manifold view, the joint activity vector r ( t ) ∈ R N of a recorded population evolves along low-dimensional trajectories embedded in a high-dimensional space (panels D–F). This is illustrated by ring-like manifolds for head-direction representations and by rotational trajectories in motor cortex, both of which can often be captured by a small number of latent variables κ 1 ( t ) , … , κ D ( t ) with D ≪ N . In the context of our overview post here, I think, the figure highlights very well why neural dynamics naturally connects mechanistic network modeling with state-space descriptions of population activity. These are not competing accounts, but complementary levels of description that emphasize different aspects of the same underlying dynamical system. Source: Figure 1 from Pezon, Schmutz, Gerstner, Linking neural manifolds to circuit structure in recurrent networks, 2024, bioRxiv 2024.02.28.582565, DOI: 10.1101/2024.02.28.582565ꜛ (license: CC-BY-NC-ND 4.0)

Fabrizio Musacchio

Neural Dynamics: A definitional perspective

Neural dynamics is a subfield of computational neuroscience that focuses on the time dependent evolution of neural activity and the mathematical structures that govern it. This post provides a definitional overview of neural dynamics, situating it within the broader context of computational neuroscience and outlining its key themes, methods, and historical developments.

Fabrizio Musacchio

@FabMusacchio@mastodon.social · 2 days ago

#NeuralPlasticity & #learning are distinct but interrelated processes. #Plasticity denotes biological change in #NeuralSystems, while learning is its functional expression in #NetworkDynamics & #behavior. Learning arises from coordinated plastic processes, reshaping #NeuralStateSpace & #attractors to support stable yet flexible representations. Here's a new post on these concepts & their implications for #ComputationalNeuroscience:

🌍https://www.fabriziomusacchio.com/blog/2026-02-02-neural_plasticity_and_learning/

#CompNeuro #Neuroscience

3 media

Schematic illustration of neuronal plasticity across scales. (a) Schematic representation of key cellular elements (neuronal and glial cells) involved in the neuroplasticity (NP) process (Neuronal and Glia plasticity) as well as subcellular compartments (Synaptic, Dendritic, Axonal, Neuromuscular Plasticity). (b) Schematic diagram representing how repetitive synaptic stimulations repetitive LTPs are linked to molecular changes (Doted circle in (a)), and the generation of dendrite and spine remodeling. (c) Schematic illustration showing some of the changes in dendrite formations (dotted dendrite spines) following learning (synaptic re-wiring) and memory formation (synaptic stabilization). (d) Schematic illustration showing axonal mechanism of neuroplasticity and repair (rerouting and sprouting) following brain injury. Abbreviations: LTP, long-term potential. Source: Wikimediaꜛ (license: CC BY-SA 4.0; original figure from Gatto, 2020ꜛ).

Neural plasticity and learning are fundamental adaptive processes that enable the brain to modify its structure and function in response to experience. Shown is a simplified schematic illustration of plastic changes induced by intensive practice of a specific skill at multiple organizational levels. Top (behavior): Repeated practice improves behavioral performance, illustrated here by increased accuracy and reduced variability when practicing a motor task such as throwing darts. Middle (cortex): At the cortical level, practice leads to a reorganization of functional representations, with task relevant neural populations becoming more strongly engaged and more precisely tuned. Bottom (neuron): At the cellular level, these changes are supported by synaptic and structural plasticity, including modifications of synaptic strength and local connectivity at individual neurons. Together, these coordinated changes across scales illustrate how learning emerges from local plastic mechanisms to produce stable improvements in behavior. Source: Wikimedia Commonsꜛ (license: CC BY 3.0).

Neural population dynamics as an embedding of task structure into neural state space. (A) Behavioral paradigm illustrating a reaching movement to a single target. The monkey initiates a movement from a fixed starting position and reaches toward a specified goal. In this simple condition, the task is fully parameterized by time
t
along the movement trajectory. (B) Trial-averaged population firing rates of many simultaneously recorded neurons during the reach. Each trace represents the activity of one neuron as a function of time, illustrating how complex, heterogeneous single-neuron responses unfold during a structured behavior. (C) The same population activity represented as a trajectory in neural state space. At each time point, the joint activity of all recorded neurons defines a single point in a high-dimensional space, with neural dynamics corresponding to a continuous trajectory through this space. Temporal evolution of behavior is thus mapped onto a geometric path in population activity space. (D) Extension of the task to reaching movements in multiple directions. In this case, behavior is no longer described by time alone but by two task variables: Time within the movement and reach angle. (E) The resulting task manifold, here shown schematically as a low-dimensional cylinder parameterized by time and reach direction. Each point on this manifold corresponds to a specific behavioral state of the task. (F) The neural data manifold obtained from population activity. — Neural population dynamics as an embedding of task structure into neural state space. (A) Behavioral paradigm illustrating a reaching movement to a single target. The monkey initiates a movement from a fixed starting position and reaches toward a specified goal. In this simple condition, the task is fully parameterized by time t along the movement trajectory. (B) Trial-averaged population firing rates of many simultaneously recorded neurons during the reach. Each trace represents the activity of one neuron as a function of time, illustrating how complex, heterogeneous single-neuron responses unfold during a structured behavior. (C) The same population activity represented as a trajectory in neural state space. At each time point, the joint activity of all recorded neurons defines a single point in a high-dimensional space, with neural dynamics corresponding to a continuous trajectory through this space. Temporal evolution of behavior is thus mapped onto a geometric path in population activity space. (D) Extension of the task to reaching movements in multiple directions. In this case, behavior is no longer described by time alone but by two task variables: Time within the movement and reach angle. (E) The resulting task manifold, here shown schematically as a low-dimensional cylinder parameterized by time and reach direction. Each point on this manifold corresponds to a specific behavioral state of the task. (F) The neural data manifold obtained from population activity.

Fabrizio Musacchio

Neural plasticity and learning: A computational perspective

After discussing structural plasticity in the previous post, we now take a broader look at neural plasticity and learning from a computational perspective. What are the main forms of plasticity, how do they relate to learning, and how can we formalize these concepts in models of neural dynamics? In this post, we explore these questions and propose a unifying framework.

@neuromatch@neuromatch.social · 3 weeks ago

Don’t miss this opportunity to get ready for #NeuromatchAcademy applications opening in February!

🤓 Register for the Info Sessions here: https://neuromatch.io/neuromatch-and-climatematch-academy-info-session/

#ComputationalNeuroscience #STEMEducation #GlobalLearning #SummerSchool #VirtualSummerSchool #NeuroAI #CompNero

Thinking about applying?
Start here.
Academy info Sessions: Wed 21 Jan 1am & 1pm UTC and Wed 28 Jan 8am & 8 pm UTC. — Thinking about applying? Start here. Academy info Sessions: Wed 21 Jan 1am & 1pm UTC and Wed 28 Jan 8am & 8 pm UTC.

2026 Neuromatch Academy and Climatematch Academy Info Sessions - Neuromatch

Join the Neuromatch & Climatematch Academy info sessions to explore courses, applications, financial aid, and ask your questions live online.

@neuromatch@neuromatch.social · 4 weeks ago

Don’t miss this opportunity to get ready for #NeuromatchAcademy applications opening in February!

🤓 Register for the Info Sessions here: https://neuromatch.io/neuromatch-and-climatematch-academy-info-session/

#ComputationalNeuroscience #STEMEducation #GlobalLearning #SummerSchool #VirtualSummerSchool #NeuroAI #CompNero

Thinking about applying?
Start here.
Academy info Sessions: Wed 21 Jan 1am & 1pm UTC and Wed 28 Jan 8am & 8 pm UTC. — Thinking about applying? Start here. Academy info Sessions: Wed 21 Jan 1am & 1pm UTC and Wed 28 Jan 8am & 8 pm UTC.

2026 Neuromatch Academy and Climatematch Academy Info Sessions - Neuromatch

Join the Neuromatch & Climatematch Academy info sessions to explore courses, applications, financial aid, and ask your questions live online.

@neuromatch@neuromatch.social · 4 weeks ago

We are excited to share that Gideon Alex, a Neuromatch Academy alumnus and Ambassador from Nigeria, is attending the Simons Computational Neuroscience Imbizo this week.

#imbizo2026 #ComNeuro #ComputationalNeuroscience #Imbizo

Attending #Imbizo? Chat with our Neuromach Ambassador
Picture of Gideon and Imbizo info — Attending #Imbizo? Chat with our Neuromach Ambassador Picture of Gideon and Imbizo info

@neuromatch@neuromatch.social · 4 weeks ago

🤓 If you are attending #imbizo2026, please make a point to connect with Gideon about Neuromatch. It is a great opportunity to talk about Neuromatch’s open-source learning resources & our July Academy courses, and to learn what is possible through our global community.

@neuromatch@neuromatch.social replied · 4 weeks ago

➡️ Learn more about our July courses (applications open in February): https://neuromatch.io/courses/

➡️ Ask a Neuromatch Ambassador from your country or in your native language a question: https://neuromatch.io/neuromatch-ambassadors/

#imbizo2026 #ComNeuro
#ComputationalNeuroscience #Imbizo

Courses - Neuromatch

Affordable, global online courses in Comp Neuro, Deep Learning and NeuroAI. Learn in small pods with a TA and an inclusive community.

Neuromatch Ambassadors - Neuromatch

Connect with Neuromatch Ambassadors worldwide; students, researchers, and educators passionate about computational neuroscience, AI, and open science. Explore their experiences, book talks, or ask questions to learn more about our programs.

Marcel Stimberg and 2 others boosted

Laurent Perrinet

@laurentperrinet@neuromatch.social · last month

📣 PhD Position in Computational & Systems Neuroscience (Marseille, France)

⏰ Deadline: January 28, 2026

We are recruiting a PhD student to work on neuromodulatory control of predictive processing in mouse vision, co-supervised by Ede Rancz (INMED) and myself.

Project Overview

This CENTURI project combines:

Computational modeling (spiking and normative models)
In vivo electrophysiology, imaging, behavior, and optogenetics
Investigation of how serotonin and noradrenaline shape prediction error signaling during sensory-motor mismatch

Candidate Profile

Motivated individual with a background in neuroscience or computational sciences
Experience in either experimental in vivo work or modeling (Python, neural networks)
Interest in working at the interface of theory and biology

Application Details

📄 Full project description
📬 Application form
We welcome applicants from all backgrounds and celebrate diversity and free creative thinking in our research environment.

Please share this opportunity or contact us directly if you know strong candidates who would thrive in this interdisciplinary environment.

#Neuroscience #PhD #ComputationalNeuroscience #Marseille

https://laurentperrinet.github.io/post/2026-01-28_phd-position_neuromodulatory-predictive-processing/
https://bsky.app/profile/ederancz.bsky.social/post/3mbot3n2uzk2e
https://www.linkedin.com/posts/ede-rancz-8a68b5_phd-position-in-computational-systems-activity-7413972661300510720-FKAu

https://bsky.app

@neuromatch@neuromatch.social · last month

Thinking about applying to Neuromatch Academy? Chathura took the leap, found direction and mentors, and built a career he once thought was out of reach.
Your leap could be next.

➡️ Full story: https://www.linkedin.com/feed/update/urn:li:activity:7404530928267489281/

#Neuromatch #MyNeuromatchStory #ComputationalNeuroscience

Chathura Ranasinghe's Neuromatch Academy Journey to Ambassador | Neuromatch posted on the topic | LinkedIn

✨ From dedicated learner to active ambassador and mentor; Chathura Ranasinghe's story is one of learning, collaboration, and contribution. 🎤 "In 2021, when I was living in Sri Lanka, I was actively looking for opportunities to pursue graduate studies in computational neuroscience. Although I was working as a software engineer, I didn’t have formal academic qualifications in biology or neuroscience. Still, I had a strong passion for becoming a scientist. To bridge that gap, I began self-studying through webinars, YouTube lectures, and online resources. However, the lack of local opportunities remained a major barrier. Fortunately, in 2021 I discovered Neuromatch Academy and their Computational Neuroscience and Deep Learning courses. I enrolled in the three-week intensive Computational Neuroscience program, which became a turning point in my journey. The program gave me the chance to learn, get research experience, and collaborate with experts in the field. It greatly strengthened my knowledge and confidence. After completing the program, I began receiving research and collaboration opportunities from the Netherlands and USA. Many of the professors I worked with expressed great appreciation for Neuromatch Academy and the valuable work they do. This made me truly appreciate the value and mission of NMA. In 2022, I decided to give back by volunteering as an ambassador to help spread the word about NMA. After migrating to the United States, I continued volunteering and in 2024 I served as a Deep Learning mentor. Now, after a brief break, I’m committed to contributing even more, because I know firsthand how Neuromatch Academy can change the lives of young scientists." ✨ His advice to anyone considering a Neuromatch Academy course: "I’d tell them that Neuromatch Academy and Climatematch Academy are amazing opportunities to learn from experts, work on real projects, and join a global, supportive community. You don’t need a perfect background, just curiosity and motivation. These programs can open doors, build confidence, and truly change your career path. If you’re interested in neuroscience, deep learning, or climate science, applying is absolutely worth it." 🌟 Now a 2026 Neuromatch Ambassador, Chathura is eager to share with others how one opportunity can grow into a commitment to others thrive. He is happy to take potential student questions and share his experience. 🤓 Learn more about our courses: https://lnkd.in/gaEhbVHz 💬 Have a question? Chat with one of our Neuromatch Ambassadors: https://lnkd.in/gtK4mDtv 📬 Stay in the loop: https://lnkd.in/gvhYizCc #Neuromatch #MyNeuromatchStory #ImpactStory #ComputationalNeuroscience #GlobalEducation #STEMEducation #Neuroscience #ScientificCommunity #MachineLearning #DataScience #Mentorship #NeuromatchAcademy #Ambassador

@neuromatch@neuromatch.social · last month

Neuromatch Ambassadors share what it’s like to take a Neuromatch or Climatematch course & help students prep for Feb applications!

✨ Connect with an Ambassador: https://neuromatch.io/neuromatch-ambassadors/

✨ Join a Jan Info Session: https://neuromatch.io/neuromatch-and-climatematch-academy-info-session/

#ComputationalNeuroscience #ClimateScience

Got a question?
Looking for someone to present?
Ask an Ambassador! — Got a question? Looking for someone to present? Ask an Ambassador!

Neuromatch Ambassadors - Neuromatch

Connect with Neuromatch Ambassadors worldwide; students, researchers, and educators passionate about computational neuroscience, AI, and open science. Explore their experiences, book talks, or ask questions to learn more about our programs.

2026 Neuromatch Academy and Climatematch Academy Info Sessions - Neuromatch

Join the Neuromatch & Climatematch Academy info sessions to explore courses, applications, financial aid, and ask your questions live online.

Laurent Perrinet

@laurentperrinet@neuromatch.social · last month

📣 PhD Position in Computational & Systems Neuroscience (Marseille, France)

⏰ Deadline: January 28, 2026

We are recruiting a PhD student to work on neuromodulatory control of predictive processing in mouse vision, co-supervised by Ede Rancz (INMED) and myself.

Project Overview

This CENTURI project combines:

Computational modeling (spiking and normative models)
In vivo electrophysiology, imaging, behavior, and optogenetics
Investigation of how serotonin and noradrenaline shape prediction error signaling during sensory-motor mismatch

Candidate Profile

Motivated individual with a background in neuroscience or computational sciences
Experience in either experimental in vivo work or modeling (Python, neural networks)
Interest in working at the interface of theory and biology

Application Details

📄 Full project description
📬 Application form
We welcome applicants from all backgrounds and celebrate diversity and free creative thinking in our research environment.

Please share this opportunity or contact us directly if you know strong candidates who would thrive in this interdisciplinary environment.

#Neuroscience #PhD #ComputationalNeuroscience #Marseille

https://laurentperrinet.github.io/post/2026-01-28_phd-position_neuromodulatory-predictive-processing/
https://bsky.app/profile/ederancz.bsky.social/post/3mbot3n2uzk2e
https://www.linkedin.com/posts/ede-rancz-8a68b5_phd-position-in-computational-systems-activity-7413972661300510720-FKAu

https://bsky.app

@neuromatch@neuromatch.social · 3 months ago

MohammadFazel Abdhaghighi, a student and research assistant from Iran, discovered new ways of thinking and exploring science through Neuromatch Academy’s Computational Neuroscience course.

➡️ Read his story here: https://www.linkedin.com/feed/update/urn:li:activity:7399202404241567744

#Neuromatch #MyNeuromatchStory #ImpactStory #ComputationalNeuroscience #Neuroscience #NeuromatchAcademy

Sorry, no caption provided by author

jonny (good kind) and 3 others boosted

@neuralreckoning@neuromatch.social · 3 months ago

Postdoc fellowship opportunity for ECRs (<3 yrs post-PhD). Note that if you want to apply to work with me as your mentor, our dept has an internal deadline of Dec 4th so please email me asap. Our internal process is shorter than the full application.

https://royalcommission1851.org/fellowships/research-fellowships

#Neuroscience #ComputationalNeuroscience #SpikingNeuralNetworks

Fabrizio Musacchio boosted

@neuralreckoning@neuromatch.social · 3 months ago

Reminder if you missed #SNUFA spiking neural network and neuromorphic workshop earlier this month, all our talks were recorded and are now available to watch.

https://www.youtube.com/playlist?list=PL09WqqDbQWHEyTZPLMKEi9-RD6kax0tCR

#Neuroscience #ComputationalNeuroscience #SpikingNeuralNetworks

@neuralreckoning@neuromatch.social · 3 months ago

Postdoc fellowship opportunity for ECRs (<3 yrs post-PhD). Note that if you want to apply to work with me as your mentor, our dept has an internal deadline of Dec 4th so please email me asap. Our internal process is shorter than the full application.

https://royalcommission1851.org/fellowships/research-fellowships

#Neuroscience #ComputationalNeuroscience #SpikingNeuralNetworks

@neuromatch@neuromatch.social · 3 months ago

What an incredible week at #SfN2025!

Thank you to everyone who stopped by the Neuromatch booth, joined our sessions, and came to our social event. It was great to connect with our amazing community of students, alumni, TAs, and volunteers! It’s been such a pleasure meeting you all in person!

➡️ If you didn’t get a chance to visit us, you can still join our mailing list & leave us a note here: https://airtable.com/appWO8lu7xm3VSdVj/pagKeB66DV7WtyhMP/form

#Neuromatch #Neuroscience #ComputationalNeuroscience #NeuroscienceCommunity

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