scholarly journals A point-process response model for spike trains from single neurons in neural circuits under optogenetic stimulation

2015 ◽  
Vol 35 (3) ◽  
pp. 455-474
Author(s):  
X. Luo ◽  
S. Gee ◽  
V. Sohal ◽  
D. Small
Keyword(s):  
Point Process ◽  
Neural Circuits ◽  
Spike Trains ◽  
Response Model ◽  
Single Neurons ◽  
Optogenetic Stimulation

Stochastic Models for Spike Trains of Single Neurons.

Biometrics ◽  
1978 ◽  
Vol 34 (3) ◽  
pp. 525
Author(s):  
A. G. Hawkes ◽  
G. Sampath ◽  
S. K. Spinivasan
Keyword(s):  
Stochastic Models ◽  
Spike Trains ◽  
Single Neurons

scholarly journals Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long-Term Memory Spike Response Model

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xianghong Lin ◽  
Mengwei Zhang ◽  
Xiangwen Wang
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Spike Trains ◽  
Spiking Neural Networks ◽  
Long Term Memory ◽  
Response Model ◽  
Spike Response ◽  
Spike Response Model

As a new brain-inspired computational model of artificial neural networks, spiking neural networks transmit and process information via precisely timed spike trains. Constructing efficient learning methods is a significant research field in spiking neural networks. In this paper, we present a supervised learning algorithm for multilayer feedforward spiking neural networks; all neurons can fire multiple spikes in all layers. The feedforward network consists of spiking neurons governed by biologically plausible long-term memory spike response model, in which the effect of earlier spikes on the refractoriness is not neglected to incorporate adaptation effects. The gradient descent method is employed to derive synaptic weight updating rule for learning spike trains. The proposed algorithm is tested and verified on spatiotemporal pattern learning problems, including a set of spike train learning tasks and nonlinear pattern classification problems on four UCI datasets. Simulation results indicate that the proposed algorithm can improve learning accuracy in comparison with other supervised learning algorithms.

scholarly journals Emerging ideas and tools to study the emergent properties of the cortical neural circuits for voluntary motor control in non-human primates

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 749 ◽  
Author(s):  
John F. Kalaska
Keyword(s):  
Motor Control ◽  
Neural Control ◽  
Neural Circuits ◽  
Neuron Activity ◽  
Emergent Properties ◽  
Control Mechanisms ◽  
Single Neurons ◽  
Electrode Arrays ◽  
Voluntary Motor ◽  
Neurophysiological Studies

For years, neurophysiological studies of the cerebral cortical mechanisms of voluntary motor control were limited to single-electrode recordings of the activity of one or a few neurons at a time. This approach was supported by the widely accepted belief that single neurons were the fundamental computational units of the brain (the “neuron doctrine”). Experiments were guided by motor-control models that proposed that the motor system attempted to plan and control specific parameters of a desired action, such as the direction, speed or causal forces of a reaching movement in specific coordinate frameworks, and that assumed that the controlled parameters would be expressed in the task-related activity of single neurons. The advent of chronically implanted multi-electrode arrays about 20 years ago permitted the simultaneous recording of the activity of many neurons. This greatly enhanced the ability to study neural control mechanisms at the population level. It has also shifted the focus of the analysis of neural activity from quantifying single-neuron correlates with different movement parameters to probing the structure of multi-neuron activity patterns to identify the emergent computational properties of cortical neural circuits. In particular, recent advances in “dimension reduction” algorithms have attempted to identify specific covariance patterns in multi-neuron activity which are presumed to reflect the underlying computational processes by which neural circuits convert the intention to perform a particular movement into the required causal descending motor commands. These analyses have led to many new perspectives and insights on how cortical motor circuits covertly plan and prepare to initiate a movement without causing muscle contractions, transition from preparation to overt execution of the desired movement, generate muscle-centered motor output commands, and learn new motor skills. Progress is also being made to import optical-imaging and optogenetic toolboxes from rodents to non-human primates to overcome some technical limitations of multi-electrode recording technology.

Stochastic Models for Spike Trains of Single Neurons.

1978 ◽  
Vol 141 (3) ◽  
pp. 415
Author(s):  
W. D. Ray ◽  
G. Sampath ◽  
S. K. Srinivasan
Keyword(s):  
Stochastic Models ◽  
Spike Trains ◽  
Single Neurons

Information encoding and the responses of single neurons in the primate temporal visual cortex

1993 ◽  
Vol 70 (2) ◽  
pp. 640-654 ◽  
Author(s):  
M. J. Tovee ◽  
E. T. Rolls ◽  
A. Treves ◽  
R. P. Bellis
Keyword(s):  
Information Theory ◽  
Firing Rate ◽  
Response Latency ◽  
Principal Components ◽  
Neuronal Response ◽  
Principal Component ◽  
Spike Trains ◽  
Considerable Proportion ◽  
Single Neurons ◽  
Temporal Encoding

1. The possibility of temporal encoding in the spike trains of single neurons recorded in the temporal lobe visual cortical areas of rhesus macaques was analyzed with the use of principal component and information theory analyses of smoothed spike trains. The neurons analyzed had responses selective for faces. 2. Provided that a correction was applied to earlier methods of principal component analysis used for neuronal spike trains, it was shown that the first principal component provides by a great extent the most information, with the second and third adding only small proportions (on average 18.8 and 8.4%, respectively). 3. It was shown that the magnitude of the second and higher principal components is even smaller if the spike train analysis is started after the onset of the neuronal response, instead of before the neuronal response has started. This suggests that variations in response latency are at least a part of what is reflected by the second and higher principal components. 4. The first principal component was correlated with the mean firing rate of the neurons. The second and higher principal components reflected at least partly the onset properties of the neuronal responses, such as response latency differences between the stimuli. 5. A considerable proportion of the information available from principal components 1-3 is available in the firing rate of the neuron. 6. Periods of the firing rate of as little as 50 or even 20 ms are sufficient to give a reasonable estimate of the firing rate of the neuron. 7. Information theory analysis showed that in short epochs (e.g., 50 ms) the information available from the firing rate can be as high, on average, as 84.4% of that available from the firing rate calculated over 400 ms, and 52.0% of that available from principal components 1-3 in the 400-ms period. It was also found that 44.0% of the information calculated from the first three principal components is available in the firing rates calculated over epochs as short as 20 ms. 8. More information was available near the start of the neuronal response, and the information available from short epochs became less later in the neuronal response. 9. Taken together, these analyses provide evidence that a short period of firing taken close to the start of the neuronal response provides a reasonable proportion of the total information that would be available if a long period of neuronal firing (e.g., 400 ms) were utilized to extract it, even if temporal encoding were used.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Spike Train Probability Models for Stimulus-Driven Leaky Integrate-and-Fire Neurons

2008 ◽  
Vol 20 (7) ◽  
pp. 1776-1795 ◽  
Author(s):  
Shinsuke Koyama ◽  
Robert E. Kass
Keyword(s):  
Spike Train ◽  
Point Process ◽  
Renewal Process ◽  
Spike Trains ◽  
Process Models ◽  
Time Varying ◽  
Integrate And Fire ◽  
Lack Of Fit ◽  
Point Process Models ◽  
Spike Train Data

Mathematical models of neurons are widely used to improve understanding of neuronal spiking behavior. These models can produce artificial spike trains that resemble actual spike train data in important ways, but they are not very easy to apply to the analysis of spike train data. Instead, statistical methods based on point process models of spike trains provide a wide range of data-analytical techniques. Two simplified point process models have been introduced in the literature: the time-rescaled renewal process (TRRP) and the multiplicative inhomogeneous Markov interval (m-IMI) model. In this letter we investigate the extent to which the TRRP and m-IMI models are able to fit spike trains produced by stimulus-driven leaky integrate-and-fire (LIF) neurons. With a constant stimulus, the LIF spike train is a renewal process, and the m-IMI and TRRP models will describe accurately the LIF spike train variability. With a time-varying stimulus, the probability of spiking under all three of these models depends on both the experimental clock time relative to the stimulus and the time since the previous spike, but it does so differently for the LIF, m-IMI, and TRRP models. We assessed the distance between the LIF model and each of the two empirical models in the presence of a time-varying stimulus. We found that while lack of fit of a Poisson model to LIF spike train data can be evident even in small samples, the m-IMI and TRRP models tend to fit well, and much larger samples are required before there is statistical evidence of lack of fit of the m-IMI or TRRP models. We also found that when the mean of the stimulus varies across time, the m-IMI model provides a better fit to the LIF data than the TRRP, and when the variance of the stimulus varies across time, the TRRP provides the better fit.

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