Parallel recordings of spike trains of several single cortical neurons in behaving monkeys were analyzed as a hidden Markov process. The parallel spike trains were considered as a multivariate Poisson process whose vector firing rates change with time. As a consequence of this approach, the complete recording can be segmented into a sequence of a few statistically discriminated hidden states, whose dynamics are modeled as a first-order Markov chain. The biological validity and benefits of this approach were examined in several independent ways: (i) the statistical consistency of the segmentation and its correspondence to the behavior of the animal; (ii) direct measurement of the collective flips of activity, obtained by the model; and (iii) the relation between the segmentation and the pair-wise shortterm cross-correlations between the recorded spike trains. Comparison with surrogate data was also carried out for each of the above examinations to assure their significance. Our results indicated the existence of well-separated states of activity, within which the firing rates were approximately stationary. With our present data we could reliably discriminate six to eight such states. The transitions between states were fast and were associated with concomitant changes of firing rates of several neurons. Different behavioral modes and stimuli were consistently reflected by different states of neural activity. Moreover, the pair-wise correlations between neurons varied considerably between the different states, supporting the hypothesis that these distinct states were brought about by the cooperative action of many neurons.While early sensory and late motor processes can be carried out in parallel, many intermediate processes are carried out serially (1-4). Our own introspective experience tells us that our thought processes evolve serially one after the other. Some current models of neural networks (5-7) also suggest a series of quasi-stable states which follow each other in succession.Usually, the analysis of the activity of single neurons is done by looking at their firing rates in relation to some external marker, such as a visual stimulus or a movement. In the work presented here, we treat the activity of several single neurons, which were recorded in parallel, as a spike-count vector-i.e., a vector whose first component is the number of spikes generated by the first neuron in a given time window, the second component is the spike count of the second neuron in the same window, and so forth.Until recently, almost no attempt was made to search for experimental evidence that the brain, or some part of it, goes through a sequence of distinct states.l In the present work we examined whether spike count vectors can be regarded as the output of a hidden Markov process which switches among discrete states of underlying collective activity.The HMM is a well-known technique of stochastic modeling used so far mostly for speech and handwriting recognition (10). Within this model, the observations are considered as...
A widely held idea regarding information processing in the brain is the cellassembly hypothesis suggested by Hebb in 1949. According to this hypothesis, the basic unit of information processing in the brain is an assembly of cells, which can act briefly as a closed system, in response to a specific stimulus. This work presents a novel method of characterizing this supposed activity using a hidden Markov model. This model is able to reveal some of the underlying cortical network activity of behavioural processes. In our study the process in hand was the simultaneous activity of several cells recorded from the frontal cortex of behaving monkeys. Using such a model we were able to identify the behavioural mode of the animal and directly identify the corresponding collective network activity. Furthermore, the segmentation of the data into the discrete states also provides direct evidence for the state dependence of the short-time correlation functions between the same pair of cells. Thus, this cross-correlation depends on the network state of activity and not on local connectivity alone.
Driver inattention and poor judgment are the major causes of motor vehicle accidents (MVA). Extensive research has shown that intelligent driver assistance systems can significantly reduce the number and severity of these accidents. The driver's visual perception abilities are a key factor in the design of the driving environment. This makes image processing a natural candidate in any effort to impact MVAs. The vision system described here encompasses 3 major capabilities: (i) Lane Departure Warning (ii) Headway Monitoring and Warning (iii) Forward Collision Warning. This paper describes in detail the different warning features, the HMI (visual and acoustic) application design rules, and results of a study in which the system was installed in a commercial fleet and passenger vehicles.
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