This document provides an overview of computational neuroscience from modeling single neurons to neural circuits and behavior. It discusses:
- Models of single neurons from the Hodgkin-Huxley model to reduced models like FitzHugh-Nagumo and Izhikevich neurons.
- How neurons are organized into neural circuits using different connection types and how properties like synchronization emerge from circuit properties.
- Approaches to modeling larger brain areas as neural populations using techniques like neural fields to model mean firing rates over continuous space.
- Phenomena like neural coding, plasticity, learning and their role in computational models of behaviors and cognition. It provides examples of modeling visual attention, decision making and more
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
Here is very good and amazing presentation on Brain chipss...
read this carefully and work on this because the work on brain is very good for future research...
Brain-computer interface (BCI) is a collaboration between a brain and a device that enables signals from the brain to direct some external activity, such as control of a cursor or a prosthetic limb. The interface enables a direct communications pathway between the brain and the object to be controlled. In the case of cursor control
Desafios matemáticos e computacionais da neurociênciaNeuroMat
Does the brain of a child with ADHD process information in the same way of the brain a child with no such disorder? To develop mathematical models to make sense of meaningful differences is a key challenge of Neuromathematics. And this becomes a computational challenge, to the extent it requires the building of neuroscientific databases that they into consideration all characteristics of patients and experiments. Lecturers: Michelle Miranda and Evandro Santos Rocha, NeuroMat team members.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
Here is very good and amazing presentation on Brain chipss...
read this carefully and work on this because the work on brain is very good for future research...
Brain-computer interface (BCI) is a collaboration between a brain and a device that enables signals from the brain to direct some external activity, such as control of a cursor or a prosthetic limb. The interface enables a direct communications pathway between the brain and the object to be controlled. In the case of cursor control
Desafios matemáticos e computacionais da neurociênciaNeuroMat
Does the brain of a child with ADHD process information in the same way of the brain a child with no such disorder? To develop mathematical models to make sense of meaningful differences is a key challenge of Neuromathematics. And this becomes a computational challenge, to the extent it requires the building of neuroscientific databases that they into consideration all characteristics of patients and experiments. Lecturers: Michelle Miranda and Evandro Santos Rocha, NeuroMat team members.
Have We Missed Half of What the Neocortex Does? by Jeff Hawkins (12/15/2017)Numenta
This was a presentation given on December 15, 2017 at the MIT Center for Brains, Minds + Machines as part of their Brains, Minds and Machines Seminar Series.
You can watch the recording of the presentation after Slide 1.
In this talk, Jeff describes a theory that sensory regions of the neocortex process two inputs. One input is the well-known sensory data arriving via thalamic relay cells. We propose the second input is a representation of allocentric location. The allocentric location represents where the sensed feature is relative to the object being sensed, in an object-centric reference frame. As the sensors move, cortical columns learn complete models of objects by integrating sensory features and location representations over time. Lateral projections allow columns to rapidly reach a consensus of what object is being sensed. We propose that the representation of allocentric location is derived locally, in layer 6 of each column, using the same tiling principles as grid cells in the entorhinal cortex. Because individual cortical columns are able to model complete complex objects, cortical regions are far more powerful than currently believed. The inclusion of allocentric location offers the possibility of rapid progress in understanding the function of numerous aspects of cortical anatomy.
Jeff discusses material from these two papers. Others can be found at https://numenta.com/papers
A Theory of How Columns in the Neocortex Enable Learning the Structure of the World
URL: https://doi.org/10.3389/fncir.2017.00081
Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in the Neocortex
URL: https://doi.org/10.3389/fncir.2016.00023
Introduction to modern methods and tools for biologically plausible modeling ...SSA KPI
AACIMP 2010 Summer School lecture by Ruben Tikidji-Hamburyan. "Physics, Chemistry and Living Systems" stream. "Introduction to Modern Methods and Tools for Biologically Plausible Modeling of Neurons and Neural Networks" course. Part 1.
More info at http://summerschool.ssa.org.ua
É revisitada a famosa palestra de Niels Bohr em 1932 com o mesmo título, procurando atualizála. Os tópicos tratados são: 1) Intuição biológica. 2) Avanços básicos. 3) A origem da vida.
4) Dos procariontes aos eucariontes. 5) Luz solar e a vida. 6) A física quântica é relevante para a biologia? 7) Mecânica quântica, cérebro e mente. 8) A consciência. 9) Existe livre arbítrio? 10) A luz como arma da biologia: Pinças óticas. 11) Calibração absoluta das pinças. 12) Proteínas como demônios de Maxwell. 13) A catraca browniana. 14) Proteinas motoras: cinesina, miosina V e ATPsintase. 15) Mecanobiologia. 16) Nanotubos de tunelamento. 17) Comunicação à distância entre células e suas funções. 18) “Le hasard et la nécessité”.
AI&BigData Lab 2016. Дмитрий Новицкий: cпайковые и бионические нейронные сети...GeeksLab Odessa
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
В мире машинного обучения многие годы доминируют нейронные сети прямого распространения (feed-forward), которые почти ничего общего не имеют с нейронами и сетями нашего мозга. В этом докладе мы познакомимся с бионическими (biologically plausible) нейронными сетями. В большинстве из них нейроны испускают и принимают импульсы (спайки). Какие возникают проблемы и сложности обучения таких сетей? В каких традиционно нерешаемых (или плохо решаемых) задачах они могут быть эффективны, как эффективен в них мозг человека и животных? Как можно реализовать такие сети аппаратно, и что такое нейроморфный компьютинг? -- Вот вопросы, которым посвящена данная презентация.
Nervous system forms an interconnecting fibers of communication network.
In the ‘hard-wiring’ of the nerves, the signals travel in the form of a flow of electrical current called nerve impulses.
The stimulus-response reactions afford internal constancy in the face of environmental changes.
Cranial Laser Reflex Technique: Healthcare for GeniusesNicholas Wise
Cranial Laser Reflex Technique is an exciting new development in natural pain relief and functional improvement. This stand-alone method allows a practitioner with any cold laser to be able to reduce someone's musculoskeletal pain with amazing speed. This condensed version of Dr. Nick Wise's recent lecture gives the scientific basis of CLRT.
Dynamical Systems Modeling in NeuroscienceYohan John
A lecture I gave recently as an introduction to computational neuroscience.
Note: the "pentagon of science" was proposed by Eric L Schwartz, a professor at BU who coined the term "computational neuroscience".
A STDP RULE THAT FAVOURS CHAOTIC SPIKING OVER REGULAR SPIKING OF NEURONSijaia
We compare the number of states of a Spiking Neural Network (SNN) composed from chaotic spiking
neurons versus the number of states of a SNN composed from regular spiking neurons while both SNNs
implementing a Spike Timing Dependent Plasticity (STDP) rule that we created. We find out that this
STDP rule favors chaotic spiking since the number of states is larger in the chaotic SNN than the regular
SNN. This chaotic favorability is not general; it is exclusive to this STDP rule only. This research falls
under our long-term investigation of STDP and chaos theory.
Design of Cortical Neuron Circuits With VLSI Design Approachijsc
A simple CMOS circuitry using very less number of MOSFETs reproduce most of the electrophysiological cortical neuron types and is capable of producing a variety of different behaviors with diversity similar to that of real biological neuron cell. The firing pattern of basic cell classes like regular spiking (RS), chattering (CH), intrinsic bursting (IB) and fast spiking(FS) are obtained with a simple adjustment of only one biasing voltage makes circuit suitable for applications in reconfigurable neuromorphic devices that implement biologically resemble circuit of cortex. This paper discusses spice simulation of the various spiking pattern ability with required and firing frequency of a given cell type. The circuit operation is verified for both conditions-constant input and pulsating input.
Brain research has made tremendous progress over the last few decades in nearly all areas of investigation and yet, the comprehension of cognition still eludes us because most high-level functions, such as decision making, results from the complex and dynamic interaction between several structures. On the one hand we have a fragmented collection of computational models whose unification is out of reach, on the other hand we have holistic approaches whose complexity obliterates any hope of comprehension. Informed by neuroscience and guided by philosophy, I propose to build the foundations of a research program in computational neuroscience in order to explain how cognition develop in most vertebrates, while guaranteeing its intelligibility.
This course is an introduction to digital typography rendering, providing key concepts of typography as well as introducing several computer graphics techniques to render text, from the oldest and most common techniques (texture based) to the latest methods taking full advantage of shaders with quasi-flawless rendering.
ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research is reproducible.
To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience lives on GitHub where each new implementation of a computational study is made available together with comments, explanations and tests. Each submission takes the form of a pull request that is publicly reviewed and tested in order to guarantee that any researcher can re-use it. If you ever replicated computational results from the literature in your research, ReScience is the perfect place to publish your new implementation.
" ...we managed to explicitly dissociate reinforcement learning from Hebbian learning and demonstrated covert learning inside the basal ganglia. These results suggest that a behavioral decision results from both the cooperation (acquisition) and competition (expression) of two distinct but entangled memory systems, the goal-directed system and the habit system that may represent the two ends of the same graded phenomenon."
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
2. Word cloud from the 2015
Computational Neuroscience Symposium
3. The biological neuron
A typical neuron consists of a cell
body, dendrites, and an axon.
A neuron is an electrically excitable
cell that processes and transmits
information through electrical and
chemical signals.
4. Neural circuits
Neurons never function in isolation;
they are organized into ensembles
or circuits that process specific
kinds of information.
Afferent neurons, efferent neurons
and interneurons are the basic
constituents of all neural circuits.
5. Brain, body & behavior
The human brain is made of
≈ 86 billions neurons
Each neuron is connected to
≈10,000 other neurons (average)
1mm3 of cortex contains ≈1 billion
connections
6. To model the brain
To emulate → new algorithms
(e.g. deep learning)
To heal → new therapies
(e.g. deep brain stimulation)
To understand→ new knowledge
(e.g. visual attention)
7. What kind of models?
Connectionist models for
performances & learning
Biophysical models for simulation
& prediction
Cognitive models for the
emulation of behavior
8. How to build models?
Basic material
• Anatomy and physiology
• Experiments & recordings
• Pathologies & lesions
Working hypotheses
• Extreme simplifications
• Parallel & distributed computing
• Dynamic systems & learning
Validation
• Predictions
• Explanations
10. Disclaimer
Reminder : Essentially, all models
are wrong but some are useful.
(George E.P. Box, 1987)
Artificial neuron are over-simplified
models of the biological reality, but
some of them can give a fair
account of actual behavior.
11. The frog sciatic nerve
From frogs to integrate-and-fire
Nicolas Brunel · Mark C. W. van Rossum
Biological Cybernetics (2007)
“Lapicque used a more exotic one, namely, a
ballistic rheotome. This is a gun-like contrap-
tion that first shoots a bullet through a first
wire, making the contact, and a bit later the
same bullet cuts a second wire in its path,
breaking the contact.”
12. The formal neuron
The McCulloch-Pitts model (1943)
is an extremely simple artificial
neuron. The inputs could be either
a zero or a one as well as the
output.
13. The squid giant axon
In 1952, Alan Lloyd Hodgkin and
Andrew Huxley described the
ionic mechanisms underlying the
initiation and propagation of action
potentials in the squid giant axon.
14. Anatomy of a spike
An action potential (spike) is a
short-lasting event in which the
electrical membrane potential of a
cell rapidly rises and falls, following
a consistent trajectory.
15. Voltage clamp
Based on a series of breakthrough
voltage-clamp experiments,
Hodgkin & Huxley developed a
detailed mathematical model of
the voltage-dependent and time-
dependent properties of the Na+
and K+ conductances.
16. The Hodgkin & Huxley model (1952)
The empirical work lead to the development of a coupled set of differential
equations describing the ionic basis of the action potential.
The ionic current is subdivided into three distinct components, a sodium current
INa, a potassium current IK, and a small leakage current IL (chloride ions).
Based on the experiments, they were able to accurately estimate all parameters.
18. The Hodgkin & Huxley model (1952)
The Nobel prize was awarded to both men a decade later in 1963.
The field of computational neuroscience was launched.
More than 60 years later, the Hodgkin-Huxley model is still a reference.
A.L HodgkinJ.C. Eccles A. Huxley
19. Reduced models are simpler
The behavior of high-dimensional nonlinear differential equations is difficult to
visualize and even more difficult to analyze.
The four-dimensional model of Hodgkin-Huxley can be reduced to two dimensions
under the assumption that the m-dynamics is fast as compared to u, h, and n, and
that the latter two evolve on the same time scale.
FitzHugh–Nagumo (1961) Morris Lescar (1981)
21. Reduced models are still too complex…
Even if conductance-based models are the simplest possible biophysical
representation of an excitable cell and can be reduced to simpler model, they
remain difficult to analyse (and simulate) due to their intrinsic complexity.
For this reason, simple threshold-based models have been developed and are
highly popular for studying neural coding, memory, and network dynamics.
22. Leaky Integrate & Fire
In the leaky integrate and fire (LIF) model, spike occurs when the membrane
potential crosses a given threshold, and is instantaneously reset to a given reset
value. But no bursting mode (B), no adaptation (C), no inhibitory rebound (D)
τ du(t)/dt = -[u(t) - urest] + R.I(t)
if u(t) > ϑ then limδ→0,δ>0 u(t+δ)= ur
23. Izhikevich model
In 2003, E. Izhikevich introduced a
model that reproduces spiking and
bursting behavior of known types
of cortical neurons. The model
combines the biologically
plausibility of Hodgkin-Huxley-type
dynamics and the computational
efficiency of integrate-and-fire
neurons.
dv/dt = 0.04v2 + 5v + 10 - u + I
du/dt = a (bv - u)
if v=30mV then v=c, u=u + d
24. It depends on what you’re trying to achieve…
Which model for what purpose ?
28. Instantaneous connections in a small network
Instantaneous excitatory connections
synchronization
Instantaneous inhibitory connections
no synchronization
29. Delayed connections in a small network
Delayed excitatory connections
no synchronization
Delayed inhibitory connections
synchronization
30. Random population
Simulation (by A.Garenne) of
100,000 Izhikevich neurons.
sparsely and randomly connected
(patchy connections)
→ No input but spontaneous
activity because of noise
→ Spontaneous bursts of activity
(centrifugal propagating wave)
31. Structured population
Max Planck Florida Institute
scientists create first realistic 3D
reconstruction of all nerve cell
bodies in a cortical column in
the whisker system of rats. The
colour indicates the cell type of the
nerve cell.
34. Temporal coding
• Rank order (Thorpe & Gautrais, 1991)
→ most of the information about a new
stimulus is conveyed during the first 20 or
50 milliseconds after the onset of the
neuronal response
35. Temporal coding
• Rank order (Thorpe & Gautrais, 1991)
→ most of the information about a new
stimulus is conveyed during the first 20 or
50 milliseconds after the onset of the
neuronal response
• Synchrony (Wang & Terman, 1995)
→ synchrony between a pair or many
neurons could signify special events and
convey information which is not contained
in the firing rate of the neurons
• Etc…
36. Rate coding
But spike trains are not reliable…
• Average over time
→ the spike count in an interval of
duration T
• Average over population
→ the spike count during in a population
of size N in an interval of duration dt
• Average over runs
→ the spike count for N runs in an interval
of duration dt
37. Rate models
To model a rate model, no need to
first model a spiking neuron and
then compute the rate code.
Better use a direct model instead.
τdV/dt = -V + Isyn +Iext
But not enough time today…
39. Between cells and tissue
The number of neurons and
synapses in even a small piece of
cortex is immense. Because of this
a popular modelling approach
(Wilson & Cowan, 1973) has been
to take a continuum limit and study
neural networks in which space is
continuous and macroscopic state
variables are mean firing rates.
40. Neural fields
We consider a small piece of cortex to be a continuum (Ω). The membrane
potential u(x,t) at any point x is a function of other the input current and the lateral
interaction.
The w(x,y) function is generally a difference of Gaussian (a Mexican hat).
⌧
@u(x, t)
@t
= u(x, t) +
Z
⌦
w(x, y)f(u(y, t))dy + I(x, t) + h
41. Visual attention
“Everyone knows what attention is.
It is the possession by the mind, in
clear and vivid form, of one out of
what seem several simultaneously
possible objects or trains of
thought.” (W. James, 1905)
42. Visual attention
(Vitay & Rougier, 2008)
Several studies suggest that the
population of active neurons in
the superior colliculus encodes
the location of a visual target to
foveate, pursue or attend to.
43. A clockwork orange
Using the output of the focus map
we can control a robot.
Saliency
Input
Focus
C
om
petition
Camera
45. Plasticity
Synaptic plasticity is the ability of
synapses to strengthen or weaken
over time, in response to increases
or decreases in their activity
Structural plasticity is the
reorganisation of synaptic
connections through sprouting or
pruning.
Intrinsic plasticity is the persistent
modification of a neuron’s intrinsic
electrical properties by neuronal or
synaptic activity
46. Hebb’s Postulate: fire together, wire together
When an axon of cell A is near enough to excite a cell B and repeatedly or
persistently takes part in firing it, some growth process or metabolic change
takes place in one or both cells such that A's efficiency, as one of the cells
firing B, is increased (Hebb, 1949).
Problem is that weights are not
bounded and cannot decrease.
Usually, uniform forgetting or an
anti-Hebbian rule is added to
cope with this problem.
47. Some plasticity rules
Spike-timing dependent plasticity is
a temporally asymmetric form of
Hebbian learning induced by tight
temporal correlations between the
spikes of pre- and postsynaptic
neurons.
The BCM (Bienenstock, Cooper,
and Munro) is characterized by a
rule expressing synaptic change as
a Hebb-like product of the
presynaptic activity and a nonlinear
function ϕ(y) of the postsynatic
activity, y.
48. Learning
• The cerebellum is specialized for
supervised learning, which is guided
by the error signal encoded in the
climbing fiber input from the inferior
olive learning
• The basal ganglia are specialized for
reinforcement learning, which is
guided by the reward signal encoded
in the dopaminergic input from the
substantia nigra
• The cerebral cortex is specialized for
unsupervised learning, which is
guided by the statistical properties of
the input signal itself, but may also
be regulated by the ascending
neuromodulatory inputs
50. A model of area 3b
(Detorakis & Rougier, 2013)
Using a neural field, we’ve
modelled the primary sensory
cortex (3b) in the primate using
unsupervised learning.
Stimulus
Surface
200 microns
shift
Random dot pattern
Receptive Field
Surface
2.0 mm
5 mm
B
C
40 mm
A
Cortical Layer
Neuron
wf
Input Layer
Stimulus
Receptor
we(x)
wi(x)
51. Decision making
(Topalidou et al, 2016)
Striatum
Cognitive 4 units
GPe
Cognitive 4 units
GPi
Cognitive 4 units
Thalamus
Cognitive 4 units
STN
Cognitive 4 units
Cognitive loop
Cortex
Cognitive 4 units
Striatum
Motor 4 units
GPi
Motor 4 units
GPe
Motor 4 units
Thalamus
Motor 4 units
STN
Motor 4 units
Motor loop
Cortex
Motor 4 units
Associative loop
Task
Environment
Cue
Positions
Cue
Identities
Substantia
nigra pars
compacta
Substantia
nigra pars
compacta
COMPETITION
dopamine
dopamine
reward reward
Lesion
sites
RL
HL
Cortex
Associative
4x4 units
Striatum
Associative
4x4 units
EXT EXT EXT EXT
COMPETITION
CN
+
+
+
+
+
–
–
P
Brain stem structures
(e.g., superior colliculus, PPN)
pr
pc
VTA
––
+
Prefrontal
cortex
Premotor cortex
Motor
cortex
Parieto-
temporo-
occipital
cortex
Hippocampus
Thalamus
Globus
pallidus
Striatum
Cerebellum
Amygdala
Subthalamic
nucleus
Ventral
pallidum
Nucleus
accumbens
Substantia
nigra
Claustrum
Saline or muscimol injection
into the internal part of
the Globus Pallidus (GPi)
15 minutes before session
Cue
presentation
(1.0
- 1.5
second)
Trial Start
(0.5
- 1.5
second)
Decision
(1.0
- 1.5
second)
Go
Signal
Reward
U
p
D
ow
n
Left
R
ight
Reward (juice) delivered
according to the reward
probability associated
with the chosen stimulus
Control
P=0.75
P=0.25
53. Clock-driven vs event-driven simulation
There are two families of algorithms
for the simulation of neural networks:
• synchronous or clock-driven
algorithms, in which all neurons are
updated simultaneously at every
tick of a clock
• asynchronous or event-driven
algorithms, in which neurons are
updated only when they receive or
emit a spike
54. NEURON
www.neuron.yale.edu/neuron
NEURON is a simulation
environment for modelling
individual neurons and networks of
neurons. It provides tools for
conveniently building, managing,
and using models in a way that is
numerically sound and
computationally efficient.
55. NEST simulator
www.nest-simulator.org
NEST is a simulator for spiking
neural network models that
focuses on the dynamics, size and
structure of neural systems rather
than on the exact morphology of
individual neurons.
56. Brian simulator
briansimulator.org
Brian is a simulator for spiking
neural networks available on
almost all platforms. The
motivation for this project is that a
simulator should not only save the
time of processors, but also the
time of scientists.
57. Reproducible Science
Over the years, the Python language
has become the preferred language
for computational neuroscience.
PyNN is an interface that make
possible to write a simulation script
once, using the Python programming
language, and run it without
modification on any supported
simulator.
rescience.github.io
58. Beyond this short course
• D Purves, GJ Augustine GJ, D Fitzpatrick, et al. Neuroscience, 2001.
• P Dayan, L Abbott, Theoretical Neuroscience, MIT Press, 2005.
• W Gerstner, W Kistler, Spiking Neuron Models, Cambridge Univ. Press, 2002.
• C Koch, I Segev, Methods in Neuronal Modeling, MIT Press, 1998.
• M Arbib, Handbook of Brain Theory and Neural Networks, MIT Press, 1995.