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...
Even the simplest environmental stimuli elicit responses in large populations of neurons in early sensory cortical areas. How these distributed responses are read out by subsequent processing stages to mediate behavior remains unknown. Here we used voltage-sensitive dye imaging to measure directly population responses in the primary visual cortex (V1) of monkeys performing a demanding visual detection task. We then evaluated the ability of different decoding rules to detect the target from the measured neural responses. We found that small visual targets elicit widespread responses in V1, and that response variability at distant sites is highly correlated. These correlations render most previously proposed decoding rules inefficient relative to one that uses spatially antagonistic center-surround summation. This optimal decoder consistently outperformed the monkey in the detection task, demonstrating the sensitivity of our techniques. Overall, our results suggest an unexpected role for inhibitory mechanisms in efficient decoding of neural population responses.A fundamental feature of mammalian cerebral cortex is its use of orderly topographic maps to represent sensory and motor information 1-3 . Because cortical neurons tend to respond to a broad range of stimuli 4 or movements 5 , and because there are generally multiple neurons tuned to the same range of parameters within one cortical column 6,7 , even the simplest sensory stimulus or motor response elicits activity that is distributed over a substantial population of neurons 5,8,9 . Electrophysiological studies in behaving primates suggest that perceptual and motor responses are indeed mediated by populations of neurons rather than by single neurons 10-13 . These observations raise several fundamental questions: how are stimuli and movements encoded by neural population responses, what are the optimal strategies for decoding (pooling) the population responses, and how efficient are different non-optimal pooling strategies?Several models of neural pooling in the brain have been proposed 11,14-19 . These include monitoring only the most sensitive neurons (at the extreme, a single neuron) 16 , simple averaging over the active neural population 11 and weighted summation, where the contribution of each neuron in the pool is proportional to its sensitivity 17 or proportional to the parameter value at the peak of its tuning function 14,15,18,19 .
In the mammalian cerebral cortex, neural responses are highly variable during spontaneous activity and sensory stimulation. To explain this variability, the cortex of alert animals has been hypothesized to be in an asynchronous high conductance state in which irregular spiking arises from the convergence of large numbers of uncorrelated excitatory and inhibitory inputs onto individual neurons [1][2][3][4] . Signatures of this state are that a neuron's membrane potential (Vm) hovers just below spike threshold, and its aggregate synaptic input is nearly Gaussian, arising from many uncorrelated inputs [1][2][3][4] . Alternatively, irregular spiking could arise from infrequent correlated input events that elicit large Vm fluctuations 5,6 . To distinguish these hypotheses, we developed a technique to carry out whole-cell Vm measurements from the cortex of behaving monkeys, focusing on primary visual cortex (V1) of monkeys performing a visual fixation task. Contrary to the predictions of an asynchronous state, mean Vm during fixation was far from threshold (14 mV) and spiking was triggered by occasional large spontaneous fluctuations. Distributions of Vm values were skewed beyond that expected for a range of Gaussian input 6,7 , but were consistent with synaptic input arising from infrequent correlated events 5,6 . Furthermore, spontaneous Vm fluctuations were correlated with the surrounding network activity, as reflected in simultaneously recorded nearby local field potential (LFP). Visual stimulation, however, led to responses more consistent with an asynchronous state: mean Vm approached threshold, fluctuations became more Gaussian, and correlations between single neurons and the surrounding network were disrupted. These observations demonstrate that sensory drive can shift a common cortical circuitry from a synchronous to an asynchronous state.Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms Correspondence to: Andrew Y. Y. Tan (atyy@alum.mit.edu) and Nicholas J. Priebe (nico@austin.utexas.edu). * These authors contributed equally to this work. † These authors contributed equally to this work. Author Contributions Competing financial interestsThe authors declare no competing financial interests. HHS Public Access Author Manuscript Author ManuscriptAuthor Manuscript Author ManuscriptCortical neurons exhibit variable activity even after efforts are taken to fix temporal variations in sensory stimuli and attentional state 8 . This ongoing activity affects stimulus encoding and synaptic plasticity 9 , but its neural basis is not well understood. One hypothesis is that the variable activity in alert animals arises from connections between numerous uncorrelated excitatory and inhibitory inputs [1][2][3][4] . Such a network is consistent with studies of neural architecture 10 , and exhibits spiking statistics similar to ...
To test whether spiking activity of six to eight simultaneously recorded neurons in the frontal cortex of a monkey can be characterized by a sequence of discrete and stable states, neuronal activity is analyzed by a hidden Markov model (HMM). Using the HMM method, we are able to detect distinct states of neuronal activity within which firing rates are approximately stationary. Transitions between states, as expressed by concomitant changes in the firing rates of several units, occur quite abruptly. The significance and consistency of the states are confirmed by comparison with simulated data. The detected states are specific to a monkey's response in a delayed localization task, allowing correct prediction of the response in approximately 90% of the trials. Similar predictive power is achieved by a model based simply on the response histograms (PSTH) of the units. The two models reach this predictive ability with different time courses: the PSTH model gains predictive power with a higher rate in the first second of the delay, and the HMM gains predictive power with higher rate in the next 3 sec. In this later period, conventional methods such as the PSTH cannot detect any firing rate modulations, but the HMM successfully captures transitions between distinct states that are specific to the monkey's behavioral response and occur at highly variable times from trial to trial. Our results suggest that neuronal activity in this later period is described best as transitions among distinct states that may reflect discrete steps in the monkey's mental processes.
Perceptual studies suggest that visual motion perception is mediated by opponent mechanisms that correspond to mutually suppressive populations of neurons sensitive to motions in opposite directions. We tested for a neuronal correlate of motion opponency using functional magnetic resonance imaging (fMRI) to measure brain activity in human visual cortex. There was strong motion opponency in a secondary visual cortical area known as the human MT complex (MT+), but there was little evidence of motion opponency in primary visual cortex. To determine whether the level of opponency in human and monkey are comparable, a variant of these experiments was performed using multiunit electrophysiological recording in areas MT and MST of the macaque monkey brain. Although there was substantial variability in the degree of opponency between recording sites, the monkey and human data were qualitatively similar on average. These results provide further evidence that: (1) direction-selective signals underly human MT+ responses, (2) neuronal signals in human MT+ support visual motion perception, (3) human MT+ is homologous to macaque monkey MT and adjacent motion sensitive brain areas, and (4) that fMRI measurements are correlated with average spiking activity.
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