Emilio Salinas

Correlated neuronal activity and the flow of neural information.

For years we have known that cortical neurons collectively have synchronous or oscillatory patterns of activity, the frequencies and temporal dynamics of which are associated with distinct behavioural states. Although the function of these oscillations has remained obscure, recent experimental and theoretical results indicate that correlated fluctuations might be important for cortical processes, such as attention, that control the flow of information in the brain.

FLUTTER DISCRIMINATION: NEURAL CODES, PERCEPTION, MEMORY AND DECISION MAKING

Recent studies combining psychophysical and neurophysiological experiments in behaving monkeys have provided new insights into how several cortical areas integrate efforts to solve a vibrotactile discrimination task. In particular, these studies have addressed how neural codes are related to perception, working memory and decision making in this model. The primary somatosensory cortex drives higher cortical areas where past and current sensory information are combined, such that a comparison of the two evolves into a behavioural decision. These and other observations in visual tasks indicate that decisions emerge from highly-distributed processes in which the details of a scheduled motor plan are gradually specified by sensory information.

Vector reconstruction from firing rates

In a number of systems including wind detection in the cricket, visual motion perception and coding of arm movement direction in the monkey and place cell response to position in the rat hippocampus, firing rates in a population of tuned neurons are correlated with a vector quantity. We examine and compare several methods that allow the coded vector to be reconstructed from measured firing rates. In cases where the neuronal tuning curves resemble cosines, linear reconstruction methods work as well as more complex statistical methods requiring more detailed information about the responses of the coding neurons. We present a new linear method, the optimal linear estimator (OLE), that on average provides the best possible linear reconstruction. This method is compared with the more familiar vector method and shown to produce more accurate reconstructions using far fewer recorded neurons.

Somatosensory discrimination based on cortical microstimulation

The sensation of flutter is produced when mechanical vibrations in the range of 5–50 Hz are applied to the skin. A flutter stimulus activates neurons in the primary somatosensory cortex (S1) that somatotopically map to the site of stimulation,. A subset of these neurons — those with quickly adapting properties, associated with Meissners corpuscles — are strongly entrained by periodic flutter vibrations, firing with a probability that oscillates at the input frequency,. Hence, quickly adapting neurons provide a dynamic representation of such flutter stimuli. However, are these neurons directly involved in the perception of flutter? Here we investigate this in monkeys trained to discriminate the difference in frequency between two flutter stimuli delivered sequentially on the fingertips,. Microelectrodes were inserted into area 3b of S1 and the second stimulus was substituted with a train of injected current pulses. Animals reliably indicated whether the frequency of the second (electrical) signal was higher or lower than that of the first (mechanical) signal, even though both frequencies changed from trial to trial. Almost identical results were obtained with periodic and aperiodic stimuli of equal average frequencies. Thus, the quickly adapting neurons in area 3b activate the circuit leading to the perception of flutter. Furthermore, as far as can be psychophysically quantified during discrimination, the neural code underlying the sensation of fluttercan be finely manipulated, to the extent that the behavioural responses produced by natural and artificial stimuli are indistinguishable.

Gain Modulation: A Major Computational Principle of the Central Nervous System

A Brief History of Gain Fields The seminal problem is about locating objects in the world (Figure 1a). Imagine you are reading the newspaper. You look for your teacup and reach for it. After a sip, you leave the cup in the same place and continue Emilio Salinas*‡ and Peter Thier† *Computational Neurobiology Laboratory Howard Hughes Medical Institute The Salk Institute for Biological Studies 10010 North Torrey Pines Road reading. Now you reach for the cup again, but this time La Jolla, California 92037 you don’t shift your gaze, you use your peripheral vision †Department of Cognitive Neurology to locate the cup. In the two cases, the arm movements University of Tübingen toward the target are identical and its location in space Hoppe-Seyler-Strasse 3 is the same, yet the reaching movements are guided by 72076 Tübingen different images on the retina. Therefore, to reach the Germany cup, a coordinate transformation is required that takes into account the position of the eyes. Where and how are such transformations performed by the brain? A lot is known about how neurons in the brain represent For almost 100 years, work on patients and experithe physical world. In comparison, little is known about mental animals have implicated the posterior parietal how neurons compute, how they transform, combine, cortex (PPC) in spatial vision and in the visual guidance or compare those representations. Although the mechaof movement (for review, see Thier and Karnath, 1997). nisms underlying many single computations have been In the mid 70s, Vernon Mountcastle and colleagues reunraveled (Churchland and Sejnowski, 1992), researchcorded from the PPC of awake monkeys and found ers ultimately seek mechanisms that pervade multiple neurons that discharged immediately before a visually modalities, brain areas, and functions, and the problem guided saccade toward a peripheral target, provided is that these kinds of unifying computational principles that the direction of this saccade corresponded to the have rarely been identified. In the last two decades, preferred direction of the neuron (Lynch et al., 1977). however, gain modulation has emerged as such a neural These neurons seemed to encode a saccade command computational principle—maybe the most general one represented by a vector, in retinal coordinates, from the found so far. This motivated Richard Andersen from target image to the fovea. This vector is independent of Caltech and Larry Abbott from Brandeis University to the position of the eyes relative to the head, so the organize a meeting, sponsored by the Sloan Foundation, discharge of these neurons was expected to be insensithat brought together an international group of physiolotive to eye position. In the early 80s, Richard Andersen gists and theoreticians to a secluded resort in Monterey worked as a postdoc in Mountcastle’s laboratory. When Bay. Here the state of affairs in this subject, after 20 they tested this prediction from the simple retinal coding years of research, was scrutinized. scheme for saccades, they found exactly the opposite: Gain modulation is a change in the response amplineurons in parietal area 7a were highly sensitive to eye tude of a neuron that is independent of its selectivity or position (Andersen and Mountcastle, 1983). receptive field characteristics (although sometimes it is Andersen and collaborators later quantified the dedifficult to draw the line between selectivity proper and pendency of neuronal activity on gaze direction (Andermodulation, as was discussed during the meeting). It is sen et al., 1985; Andersen, 1989; Brotchie et al., 1995). a nonlinear way to combine or integrate information from In their experiments, eye position was first held fixed, different sensory, motor, and cognitive modalities. Much and the response of a parietal neuron was plotted as of the excitement about gain fields has been spurred a function of the position of a spot of light in retinal by theoretical considerations: these distributed, multicoordinates (Figure 1b). We call this position x. Typically, modal representations are ideally configured to facilitate the resulting curve had a single peak that could be fitted certain kinds of computations, most prominently, coorby a Gaussian function; we refer to it as f(x). Then the dinate transformations. Theoreticians have investigated measurements were repeated using a different fixation primarily how gain fields can be used to perform useful point and thus a different gaze direction, y. In this case, the neural responses followed curves with similar computations and how the cortical microcircuitry may shapes and preferred locations, but their amplitudes give rise to multiplicative interactions, which are the changed. Thus, the amplitude or gain of the receptive trademark of gain modulation. On the other hand, the fields of these parietal neurons depended on gaze. The experimental camp has focused on the role of gain fields term “gain field” was coined to describe this gazein sensory–motor integration and have used them to dependent gain modulation. The gain field refers to the obtain clues about the functions of different brain areas. function g(y), where the firing rates of these neurons are As a result, gain fields have been implicated in eye and well fitted by the expression reaching movements, spatial perception, attention, navigation, and object recognition. r 5 f(x)g(y). (1)

TRANSFER OF CODED INFORMATION FROM SENSORY TO MOTOR NETWORKS

During sensory-guided motor tasks, information must be transferred from arrays of neurons coding target location to motor networks that generate and control movement. We address two basic questions about this information transfer. First, what mechanisms assure that the different neural representations align properly so that activity in the sensory network representing target location evokes a motor response generating accurate movement toward the target? Coordinate transformations may be needed to put the sensory data into a form appropriate for use by the motor system. For example, in visually guided reaching the location of a target relative to the body is determined by a combination of the position of its image on the retina and the direction of gaze. What assures that the motor network responds to the appropriate combination of sensory inputs corresponding to target position in body- or arm-centered coordinates? To answer these questions, we model a sensory network coding target position and use it to drive a similarly modeled motor network. To determine the actual motor response we use decoding methods that have been developed and verified in experimental work. We derive a general set of conditions on the sensory-to-motor synaptic connections that assure a properly aligned and transformed response. The accuracy of the response for different numbers of coding cells is computed. We show that development of the synaptic weights needed to generate the correct motor response can occur spontaneously through the observation of random movements and correlation-based synaptic modification. No error signal or external teaching is needed during this process. We also discuss nonlinear coordinate transformations and the presence of both shifting and nonshifting receptive fields in sensory/motor systems.

Correlated neuronal discharges that increase coding efficiency during perceptual discrimination.

During a sensory discrimination task, the responses of multiple sensory neurons must be combined to generate a choice. The optimal combination of responses is determined both by their dependence on the sensory stimulus and by their cofluctuations across trials-that is, the noise correlations. Positively correlated noise is considered deleterious, because it limits the coding accuracy of populations of similarly tuned neurons. However, positively correlated fluctuations between differently tuned neurons actually increase coding accuracy, because they allow the common noise to be subtracted without signal loss. This is demonstrated with data recorded from the secondary somatosensory cortex of monkeys performing a vibrotactile discrimination task. The results indicate that positive correlations are not always harmful and may be exploited by cortical networks to enhance the neural representation of features to be discriminated.

Perceptual decision making in less than 30 milliseconds

In perceptual discrimination tasks, a subjects response time is determined by both sensory and motor processes. Measuring the time consumed by the perceptual evaluation step alone is therefore complicated by factors such as motor preparation, task difficulty and speed-accuracy tradeoffs. Here we present a task design that minimizes these confounding factors and allows us to track a subjects perceptual performance with unprecedented temporal resolution. We find that monkeys can make accurate color discriminations in less than 30 ms. Furthermore, our simple task design provides a tool for elucidating how neuronal activity relates to sensory as opposed to motor processing, as demonstrated with neural data from cortical oculomotor neurons. In these cells, perceptual information acts by accelerating and decelerating the ongoing motor plans associated with correct and incorrect choices, as predicted by a race-to-threshold model, and the time course of these neural events parallels the time course of the subjects choice accuracy.

Inhibitory synchrony as a mechanism for attentional gain modulation.

Recordings from area V4 of monkeys have revealed that when the focus of attention is on a visual stimulus within the receptive field of a cortical neuron, two distinct changes can occur: The firing rate of the neuron can change and there can be an increase in the coherence between spikes and the local field potential (LFP) in the gamma-frequency range (30-50 Hz). The hypothesis explored here is that these observed effects of attention could be a consequence of changes in the synchrony of local interneuron networks. We performed computer simulations of a Hodgkin-Huxley type neuron driven by a constant depolarizing current, I, representing visual stimulation and a modulatory inhibitory input representing the effects of attention via local interneuron networks. We observed that the neurons firing rate and the coherence of its output spike train with the synaptic inputs was modulated by the degree of synchrony of the inhibitory inputs. When inhibitory synchrony increased, the coherence of spiking model neurons with the synaptic input increased, but the firing rate either increased or remained the same. The mean number of synchronous inhibitory inputs was a key determinant of the shape of the firing rate versus current (f-I) curves. For a large number of inhibitory inputs (approximately 50), the f-I curve saturated for large I and an increase in input synchrony resulted in a shift of sensitivity-the model neuron responded to weaker inputs I. For a small number (approximately 10), the f-I curves were non-saturating and an increase in input synchrony led to an increase in the gain of the response-the firing rate in response to the same input was multiplied by an approximately constant factor. The firing rate modulation with inhibitory synchrony was highest when the input network oscillated in the gamma frequency range. Thus, the observed changes in firing rate and coherence of neurons in the visual cortex could be controlled by top-down inputs that regulated the coherence in the activity of a local inhibitory network discharging at gamma frequencies.

Integrate-and-fire neurons driven by correlated stochastic input

Neurons are sensitive to correlations among synaptic inputs. However, analytical models that explicitly include correlations are hard to solve analytically, so their influence on a neurons response has been difficult to ascertain. To gain some intuition on this problem, we studied the firing times of two simple integrate-and-fire model neurons driven by a correlated binary variable that represents the total input current. Analytic expressions were obtained for the average firing rate and coefficient of variation (a measure of spike-train variability) as functions of the mean, variance, and correlation time of the stochastic input. The results of computer simulations were in excellent agreement with these expressions. In these models, an increase in correlation time in general produces an increase in both the average firing rate and the variability of the output spike trains. However, the magnitude of the changes depends differentially on the relative values of the input mean and variance: the increase in firing rate is higher when the variance is large relative to the mean, whereas the increase in variability is higher when the variance is relatively small. In addition, the firing rate always tends to a finite limit value as the correlation time increases toward infinity, whereas the coefficient of variation typically diverges. These results suggest that temporal correlations may play a major role in determining the variability as well as the intensity of neuronal spike trains.