NDLI: The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs

Content Provider	SpringerLink
Author	Giuglia, Michele Camera, Giancarlo Fusi, Stefa Senn, Walter
Copyright Year	2008
Abstract	The response of a population of neurons to time-varying synaptic inputs can show a rich phenomenology, hardly predictable from the dynamical properties of the membrane’s inherent time constants. For example, a network of neurons in a state of spontaneous activity can respond significantly more rapidly than each single neuron taken individually. Under the assumption that the statistics of the synaptic input is the same for a population of similarly behaving neurons (mean field approximation), it is possible to greatly simplify the study of neural circuits, both in the case in which the statistics of the input are stationary (reviewed in La Camera et al. in Biol Cybern, 2008) and in the case in which they are time varying and unevenly distributed over the dendritic tree. Here, we review theoretical and experimental results on the single-neuron properties that are relevant for the dynamical collective behavior of a population of neurons. We focus on the response of integrate-and-fire neurons and real cortical neurons to long-lasting, noisy, in vivo-like stationary inputs and show how the theory can predict the observed rhythmic activity of cultures of neurons. We then show how cortical neurons adapt on multiple time scales in response to input with stationary statistics in vitro. Next, we review how it is possible to study the general response properties of a neural circuit to time-varying inputs by estimating the response of single neurons to noisy sinusoidal currents. Finally, we address the dendrite–soma interactions in cortical neurons leading to gain modulation and spike bursts, and show how these effects can be captured by a two-compartment integrate-and-fire neuron. Most of the experimental results reviewed in this article have been successfully reproduced by simple integrate-and-fire model neurons.
Starting Page	303
Ending Page	318
Page Count	16
File Format	PDF
ISSN	03401200
Journal	Biological Cybernetics
Volume Number	99
Issue Number	4
e-ISSN	14320770
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-11-15
Publisher Place	Berlin/Heidelberg
Access Restriction	Subscribed
Subject Keyword	Populations of spiking neurons Dynamics Integrate-and-fire model Patch clamp Calcium spikes Neurosciences Computer Application in Life Sciences Neurobiology Bioinformatics Statistical Physics, Dynamical Systems and Complexity
Content Type	Text
Resource Type	Article
Subject	Biotechnology Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

The response of cortical neurons to in vivo-like input current: theory and experiment : I. Noisy inputs with stationary statistics

Dynamics and bifurcations of the adaptive exponential integrate-and-fire model

Reconstruction of the input signal of the leaky integrate-and-fire neuronal model from its interspike intervals

Predicting the synaptic information efficacy in cortical layer 5 pyramidal neurons using a minimal integrate-and-fire model

Integrate-and-fire neurons driven by asymmetric dichotomous noise

Feedback-induced gain control in stochastic spiking networks

In Vivo Differences in Inputs and Spiking Between Neurons in Lobules VI/VII of Neocerebellum and Lobule X of Archaeocerebellum

Unfolding an electronic integrate-and-fire circuit

Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents (2003).

The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs

Similar Documents

The response of cortical neurons to in vivo-like input current: theory and experiment : I. Noisy inputs with stationary statistics

Dynamics and bifurcations of the adaptive exponential integrate-and-fire model

Reconstruction of the input signal of the leaky integrate-and-fire neuronal model from its interspike intervals

Predicting the synaptic information efficacy in cortical layer 5 pyramidal neurons using a minimal integrate-and-fire model

Integrate-and-fire neurons driven by asymmetric dichotomous noise

Feedback-induced gain control in stochastic spiking networks

In Vivo Differences in Inputs and Spiking Between Neurons in Lobules VI/VII of Neocerebellum and Lobule X of Archaeocerebellum

Unfolding an electronic integrate-and-fire circuit

Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents (2003).

The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs