3 Best Books for Beginner Data-scientists!
Link: Medium
Link: Medium
Medium
3 Best Books for Beginner Data Scientists
Improve your data analysis skills by getting these three key books
Forwarded from G Esfahani
Videos of talks from a recent CNS workshop: https://ocs2020.github.io/
Origins of Common Sense
ALLEN INSTITUTE MODELING WORKSHOP LOG IN INFORMATION
Link: AllenInstitute
#Neuroscience
Follow: @theTuringMachine
Link: AllenInstitute
#Neuroscience
Follow: @theTuringMachine
A How to model guide for neuroscience
Within neuroscience, models have many roles, including driving hypotheses, making assumptions explicit, syn- thesizing knowledge, making experimental predictions, and facilitating applications to medicine. While specific modeling techniques are often taught, the process of constructing models for a given phenomenon or question is generally left opaque. Here, informed by guiding many students through modeling exercises at our summer school in CoSMo (Computational Sensory-Motor Neuroscience), we provide a practical 10-step breakdown of the modeling process. This approach makes choices and criteria more explicit and replicable. Experiment design has long been taught in neuroscience; the modeling process should receive the same attention.
Link
Within neuroscience, models have many roles, including driving hypotheses, making assumptions explicit, syn- thesizing knowledge, making experimental predictions, and facilitating applications to medicine. While specific modeling techniques are often taught, the process of constructing models for a given phenomenon or question is generally left opaque. Here, informed by guiding many students through modeling exercises at our summer school in CoSMo (Computational Sensory-Motor Neuroscience), we provide a practical 10-step breakdown of the modeling process. This approach makes choices and criteria more explicit and replicable. Experiment design has long been taught in neuroscience; the modeling process should receive the same attention.
Link
The Max Planck Schools are taking higher and graduate education in Germany in a new direction. With the participation of German universities and the four large German research organizations[1], the Max Planck School of Cognition, the Max Planck School Matter to Life and the Max Planck School of Photonics are concentrating the Germany-wide distributed excellence within three future-oriented fields to create internationally visible and highly attractive graduate programs. The most forward-thinking researchers of one discipline come together as Fellows of the Max Planck Schools to supervise internationally outstanding young scientists within the framework of a structured doctoral program. Fellows and students alike enjoy access to a truly unique interdisciplinary network.
Link: MaxPlanck
#position
Follow: @theTuringMachine
Link: MaxPlanck
#position
Follow: @theTuringMachine
Presynaptic inhibition rapidly stabilises recurrent excitation in the face of plasticity
Hebbian plasticity, a mechanism believed to be the substrate of learning and memory, detects and further enhances correlated neural activity. Because this constitutes an unsta- ble positive feedback loop, it requires additional homeostatic control. Computational work suggests that in recurrent networks, the homeostatic mechanisms observed in experiments are too slow to compensate instabilities arising from Hebbian plasticity and need to be com- plemented by rapid compensatory processes. We suggest presynaptic inhibition as a candi- date that rapidly provides stability by compensating recurrent excitation induced by Hebbian changes. Presynaptic inhibition is mediated by presynaptic GABA receptors that effectively and reversibly attenuate transmitter release. Activation of these receptors can be triggered by excess network activity, hence providing a stabilising negative feedback loop that weak- ens recurrent interactions on sub-second timescales. We study the stabilising effect of pre- synaptic inhibition in recurrent networks, in which presynaptic inhibition is implemented as a multiplicative reduction of recurrent synaptic weights in response to increasing inhibitory activity. We show that networks with presynaptic inhibition display a gradual increase of fir- ing rates with growing excitatory weights, in contrast to traditional excitatory-inhibitory net- works. This alleviates the positive feedback loop between Hebbian plasticity and network activity and thereby allows homeostasis to act on timescales similar to those observed in experiments. Our results generalise to spiking networks with a biophysically more detailed implementation of the presynaptic inhibition mechanism. In conclusion, presynaptic inhibi- tion provides a powerful compensatory mechanism that rapidly reduces effective recurrent interactions and thereby stabilises Hebbian learning.
Link: PLOS
#Neuroscience
Follow: @theTuringMachine
Hebbian plasticity, a mechanism believed to be the substrate of learning and memory, detects and further enhances correlated neural activity. Because this constitutes an unsta- ble positive feedback loop, it requires additional homeostatic control. Computational work suggests that in recurrent networks, the homeostatic mechanisms observed in experiments are too slow to compensate instabilities arising from Hebbian plasticity and need to be com- plemented by rapid compensatory processes. We suggest presynaptic inhibition as a candi- date that rapidly provides stability by compensating recurrent excitation induced by Hebbian changes. Presynaptic inhibition is mediated by presynaptic GABA receptors that effectively and reversibly attenuate transmitter release. Activation of these receptors can be triggered by excess network activity, hence providing a stabilising negative feedback loop that weak- ens recurrent interactions on sub-second timescales. We study the stabilising effect of pre- synaptic inhibition in recurrent networks, in which presynaptic inhibition is implemented as a multiplicative reduction of recurrent synaptic weights in response to increasing inhibitory activity. We show that networks with presynaptic inhibition display a gradual increase of fir- ing rates with growing excitatory weights, in contrast to traditional excitatory-inhibitory net- works. This alleviates the positive feedback loop between Hebbian plasticity and network activity and thereby allows homeostasis to act on timescales similar to those observed in experiments. Our results generalise to spiking networks with a biophysically more detailed implementation of the presynaptic inhibition mechanism. In conclusion, presynaptic inhibi- tion provides a powerful compensatory mechanism that rapidly reduces effective recurrent interactions and thereby stabilises Hebbian learning.
Link: PLOS
#Neuroscience
Follow: @theTuringMachine
journals.plos.org
Presynaptic inhibition rapidly stabilises recurrent excitation in the face of plasticity
Author Summary Synapses between neurons change during learning and memory formation, a process termed synaptic plasticity. Established models of plasticity rely on strengthening synapses of co-active neurons. In recurrent networks, mutually connected neurons…
Forwarded from Scientific Programming (ZiAEE)
I am working on Modeling neural dynamics from Borgers using #brian2.
It's easy to learn and fast for development.
for a quick review and tutorial look at here.
There is also a course by #Gerstner which use brain2 in his book. [link to exercise]
The course is available by EPFL.
I am uploading the jupyter note books of Borgers using brian2 here.
Have fun coding!
It's easy to learn and fast for development.
for a quick review and tutorial look at here.
There is also a course by #Gerstner which use brain2 in his book. [link to exercise]
The course is available by EPFL.
I am uploading the jupyter note books of Borgers using brian2 here.
Have fun coding!
Forwarded from Sci-Hub
10.1371@journal.pcbi.1008119.pdf
285.2 KB
Hagan, A. K., Lesniak, N. A., Balunas, M. J., Bishop, L., Close, W. L., Doherty, M. D., … Schloss, P. D. (2020). Ten simple rules to increase computational skills among biologists with Code Clubs. PLOS Computational Biology, 16(8), e1008119. doi:10.1371/journal.pcbi.1008119
Forwarded from Complex Systems Studies
💰 #PhD positions in neuroscience! Fully-funded, interdisciplinary, Bachelor's or Master's students can apply! Either in Bonn, #Germany or in Jupiter, Florida (#USA). Join the International Max Planck Research School for Brain and Behavior!
Deadline: November 15
https://www.imprs-brain-behavior.org/admissions/application-info/
Deadline: November 15
https://www.imprs-brain-behavior.org/admissions/application-info/
Online resources for system and computational neuroscience:
Link: simsonsfundations
#Neuroscience
Follow: @theTuringMachine
Link: simsonsfundations
#Neuroscience
Follow: @theTuringMachine
Simons Foundation
Online Resources for Systems and Computational Neuroscience
Online Resources for Systems and Computational Neuroscience on Simons Foundation
Forwarded from Complex Systems Studies
Forwarded from Scientific Programming (ZiAEE)
Brian workshop, 9 Sep, 15-18 Iran time.
The material will be uploaded here.
To install the required package look here.
Please fill the google form for the registration (free of charge).
The language of workshop will be in Farsi (Excuseme nonpersian members).
The material will be uploaded here.
To install the required package look here.
Please fill the google form for the registration (free of charge).
The language of workshop will be in Farsi (Excuseme nonpersian members).
Google Docs
Brian workshop
Event Timing: 9 September, 15-18 Iran time
Forwarded from Complex Systems Studies
#Postdoc position in Nonlinear Dynamics/Statistical Physics at Jacobs University Bremen, Germany
The project is on transient processes and rare events in complex systems, related to evolutionary game theory and heteroclinic dynamics. The focus is on the prediction of transient phenomena such as spatio-temporal pattern formation. Patterns range from temporal sequences of excitations in neural networks to self-organized spatial segregation and the formation
of temporary alliances between species in ecological or social systems, including extinction events, whose risk for occurrence should be analytically estimated. Possible applications refer in particular to cognitive dynamics, with transient heteroclinic processes being coupled and going on in parallel.
Very good programming skills and expertise with analytical calculations are required. Experience in bifurcation analyses and large deviation theory is desirable. The position is for 2 years with a possible start by the end of this year or earlier. https://www.jacobs-university.de/directory/meyer-ortmanns .
The project is on transient processes and rare events in complex systems, related to evolutionary game theory and heteroclinic dynamics. The focus is on the prediction of transient phenomena such as spatio-temporal pattern formation. Patterns range from temporal sequences of excitations in neural networks to self-organized spatial segregation and the formation
of temporary alliances between species in ecological or social systems, including extinction events, whose risk for occurrence should be analytically estimated. Possible applications refer in particular to cognitive dynamics, with transient heteroclinic processes being coupled and going on in parallel.
Very good programming skills and expertise with analytical calculations are required. Experience in bifurcation analyses and large deviation theory is desirable. The position is for 2 years with a possible start by the end of this year or earlier. https://www.jacobs-university.de/directory/meyer-ortmanns .