StdpC: A modern dynamic clamp

Nowotny, Thomas; Szűcs, Attila; Pinto, Reynaldo D.; Selverston, Allen I.

doi:10.1016/j.jneumeth.2006.05.034

Cited by 52 publications

(49 citation statements)

References 33 publications

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“…Many previous studies have used dynamic clamp to simulate synaptic activity (e.g. Chance et al, 2002;Desai and Walcott, 2006;Fellous et al, 2003;Wolfart et al, 2005), and some others have simulated different forms of synaptic plasticity (Rabbah and Nadim, 2005;Nowotny et al, 2006;Mittmann and Hausser, 2007). However, to our knowledge, no other study has considered the contributions of synaptic plasticity to the effects of large populations of independent synaptic inputs with independent dynamics.…”

Section: Dynamic Clamp Synaptic Plasticity and Naturalistic Inputsmentioning

confidence: 99%

Control of neuronal firing by dynamic parallel fiber feedback:implications for electrosensory reafference suppression

Lewis

Lindner

Laliberté

et al. 2007

Journal of Experimental Biology

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SUMMARY The cancellation of self-generated components of sensory inputs is a key function of sensory feedback pathways. In many systems, cerebellar parallel fiber feedback mediates this cancellation through anti-Hebbian plasticity,resulting in the generation of a negative image of the reafferent inputs. Parallel fiber feedback involves direct excitation and disynaptic inhibition as well as synaptic plasticity on multiple time scales. How the dynamics of these processes interact with anti-Hebbian plasticity to shape synaptic inputs and provide a cancellation mechanism remains unclear. In the present study, we investigated the influence of parallel fiber feedback onto pyramidal neurons of the electrosensory lateral line lobe (ELL) in weakly electric fish under open loop conditions. We mimicked naturalistic parallel fiber inputs in an ELL brain slice by implementing an experimentally based model of this synaptic pathway using dynamic clamp. We showed that as parallel fiber activity increases, the effective input to ELL pyramidal neurons changes from net excitation to net inhibition, resulting in a non-monotonic firing response. Using a model neuron, we found that this robust non-monotonic response is due to a shift from balanced excitation and inhibition at low parallel fiber input rates, to dominant inhibition at high input rates. We then showed that this non-monotonic response provides a simple basis for negative image generation. Through changes in the mean activation rate of parallel fibers, the feedback can switch roles between enhancement and suppression of sensory inputs in a manner that is directly determined by the slope of the non-monotonic response curve.

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Section: Dynamic Clamp Synaptic Plasticity and Naturalistic Inputsmentioning

confidence: 99%

Control of neuronal firing by dynamic parallel fiber feedback:implications for electrosensory reafference suppression

Lewis

Lindner

Laliberté

et al. 2007

Journal of Experimental Biology

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“…1C). A Digidata 1200B data acquisition interface (Molecular Devices) was used to implement a protocol based on previous homemade implementations of the dynamic clamp (Pinto et al, 2001;Nowotny et al, 2006). In the original dynamic clamp protocol, a computer simulates synapses between neurons by monitoring their membrane potentials and generating the currents to be injected.…”

Section: Methodsmentioning

confidence: 99%

“…this hybrid circuit, interaction was provided by a single inhibitory artificial synapse from AN to PD, implemented through a dynamic clamp protocol (Pinto et al, 2001;Nowotny et al, 2006). The AN was implemented either as a random spike generator or as a conductance-based model with additional stochastic dynamics (Carelli et al, 2005) and it was prepared to mimic the bursting phase and the average number of spikes/burst found in the original LP.…”

Section: A Hybrid Circuit Where Pd Is Connected To a Model Neuron Thrmentioning

confidence: 99%

Single Synapse Information Coding in Intraburst Spike Patterns of Central Pattern Generator Motor Neurons

Brochini¹,

Carelli²,

Pinto³

2011

J. Neurosci.

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Burst firing is ubiquitous in nervous systems and has been intensively studied in central pattern generators (CPGs). Previous works have described subtle intraburst spike patterns (IBSPs) that, despite being traditionally neglected for their lack of relation to CPG motor function, were shown to be cell-type specific and sensitive to CPG connectivity. Here we address this matter by investigating how a bursting motor neuron expresses information about other neurons in the network. We performed experiments on the crustacean stomatogastric pyloric CPG, both in control conditions and interacting in real-time with computer model neurons. The sensitivity of postsynaptic to presynaptic IBSPs was inferred by computing their average mutual information along each neuron burst. We found that details of input patterns are nonlinearly and inhomogeneously coded through a single synapse into the fine IBSPs structure of the postsynaptic neuron following burst. In this way, motor neurons are able to use different time scales to convey two types of information simultaneously: muscle contraction (related to bursting rhythm) and the behavior of other CPG neurons (at a much shorter timescale by using IBSPs as information carriers). Moreover, the analysis revealed that the coding mechanism described takes part in a previously unsuspected information pathway from a CPG motor neuron to a nerve that projects to sensory brain areas, thus providing evidence of the general physiological role of information coding through IBSPs in the regulation of neuronal firing patterns in remote circuits by the CNS.

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“…This platform can be used to build activity-dependent stimulus-response loops to interact with living systems below the millisecond time scale. So far, RT technology has been used to introduce artificial membrane or synaptic conductances and to create hybrid circuits of real and electronic neurons [2][3][4]. Generalizing the principles underlying dynamic-clamp, new protocols of RT eventdependent control, stimulation, and recording can be developed with applications in a broad spectrum of biomedical research.…”

mentioning

confidence: 99%

RTBiomanager: a software platform to expand the applications of real-time technology in neuroscience

2009

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A grand challenge in Computational Neuroscience is the integration of data arising from experimental techniques and theoretical work that involves large regions of unconstrained parameter space. Neural systems have drift, adaptation and learning mechanisms that result in transient behavior. Many information-processing mechanisms take place in this nonstationary activity that extends over several spatial and temporal scales. Studying this type of activity requires the use of activity-dependent stimulation techniques that can probe transient input/output relationships of neurons and circuits over different timescales (including under the millisecond). This requirement points towards the creation of new real-time software protocols to perform closed-loop adaptive interaction with neural systems to observe, manipulate and actively probe their function. These protocols may include the use of models that interact with living neurons to constrain their parameter space and even to refine themselves through this interaction [1]. RT activity-dependent stimulation can involve many types of stimuli (electrical, chemical, mechanical, etc.) and monitoring techniques (intra-or extracellular recordings, imaging, etc).Here we present RTBiomanager, a real-time software platform that can help to study neural transient dynamics. This platform can be used to build activity-dependent stimulus-response loops to interact with living systems below the millisecond time scale. So far, RT technology has been used to introduce artificial membrane or synaptic conductances and to create hybrid circuits of real and electronic neurons [2][3][4]. Generalizing the principles underlying dynamic-clamp, new protocols of RT eventdependent control, stimulation, and recording can be developed with applications in a broad spectrum of biomedical research. RTBiomanager is a multipurpose platform to control bi-directional interactions among living RT agents and artificial RT agents. Examples of living RT agents are cells, neurons, membranes, synapses, neural network and tissues. Examples of artificial RT agents are computer models, electronic devices, artificial neurons, sensors, microinjectors, motors [5], lasers and CCD cameras. RTBiomanager runs under RTAI (Real-Time Application Interface), a hard real-time layer over the Linux operating system to assure that the timing constraints in the detection, stimulation and control of artificial RT agents involved in physiological experiments can be accomplished.RTBiomanager is a user-friendly and customizable application that provides its own data monitor and analysis tools. This platform is designed for neuroscientists to explore the use of real-time technology to build a set of novel experiments that combine different recording and stimulation techniques. RTBiomanager will be available for free to download from our webpage.

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StdpC: A modern dynamic clamp

Cited by 52 publications

References 33 publications

Control of neuronal firing by dynamic parallel fiber feedback:implications for electrosensory reafference suppression

Control of neuronal firing by dynamic parallel fiber feedback:implications for electrosensory reafference suppression

Single Synapse Information Coding in Intraburst Spike Patterns of Central Pattern Generator Motor Neurons

RTBiomanager: a software platform to expand the applications of real-time technology in neuroscience

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