Stochastic Models for Spike Trains of Single Neurons. G. Sampath , S. K. Srinivasan

1978 ◽  
Vol 53 (4) ◽  
pp. 487-487
Author(s):  
George P. Moore
Keyword(s):  
Stochastic Models ◽  
Spike Trains ◽  
Single Neurons

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

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 Discrimination of Communication Vocalizations by Single Neurons and Groups of Neurons in the Auditory Midbrain

2010 ◽  
Vol 103 (6) ◽  
pp. 3248-3265 ◽  
Author(s):  
David M. Schneider ◽  
Sarah M. N. Woolley
Keyword(s):  
Individual Recognition ◽  
Spike Trains ◽  
Single Neurons ◽  
Neural Responses ◽  
Auditory Midbrain ◽  
Bird Songs ◽  
Complex Sounds ◽  
Temporal Precision ◽  
Midbrain Neurons ◽  
Wide Range

Many social animals including songbirds use communication vocalizations for individual recognition. The perception of vocalizations depends on the encoding of complex sounds by neurons in the ascending auditory system, each of which is tuned to a particular subset of acoustic features. Here, we examined how well the responses of single auditory neurons could be used to discriminate among bird songs and we compared discriminability to spectrotemporal tuning. We then used biologically realistic models of pooled neural responses to test whether the responses of groups of neurons discriminated among songs better than the responses of single neurons and whether discrimination by groups of neurons was related to spectrotemporal tuning and trial-to-trial response variability. The responses of single auditory midbrain neurons could be used to discriminate among vocalizations with a wide range of abilities, ranging from chance to 100%. The ability to discriminate among songs using single neuron responses was not correlated with spectrotemporal tuning. Pooling the responses of pairs of neurons generally led to better discrimination than the average of the two inputs and the most discriminating input. Pooling the responses of three to five single neurons continued to improve neural discrimination. The increase in discriminability was largest for groups of neurons with similar spectrotemporal tuning. Further, we found that groups of neurons with correlated spike trains achieved the largest gains in discriminability. We simulated neurons with varying levels of temporal precision and measured the discriminability of responses from single simulated neurons and groups of simulated neurons. Simulated neurons with biologically observed levels of temporal precision benefited more from pooling correlated inputs than did neurons with highly precise or imprecise spike trains. These findings suggest that pooling correlated neural responses with the levels of precision observed in the auditory midbrain increases neural discrimination of complex vocalizations.

scholarly journals Identification of Functionally Interconnected Neurons Using Factor Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jorge H. Soletta ◽  
Fernando D. Farfán ◽  
Ana L. Albarracín ◽  
Alvaro G. Pizá ◽  
Facundo A. Lucianna ◽  
...  
Keyword(s):  
Neural Networks ◽  
Factor Analysis ◽  
Spike Trains ◽  
Single Neurons ◽  
Joint Activity ◽  
Functional Dynamics ◽  
Increasing Demand ◽  
Complex Valued ◽  
Electrophysiological Methods ◽  
Processing Mechanism

The advances in electrophysiological methods have allowed registering the joint activity of single neurons. Thus, studies on functional dynamics of complex-valued neural networks and its information processing mechanism have been conducted. Particularly, the methods for identifying neuronal interconnections are in increasing demand in the area of neurosciences. Here, we proposed a factor analysis to identify functional interconnections among neurons via spike trains. This method was evaluated using simulations of neural discharges from different interconnections schemes. The results have revealed that the proposed method not only allows detecting neural interconnections but will also allow detecting the presence of presynaptic neurons without the need of the recording of them.

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