Biomedical subjects

Emilio Salinas

Publications and source records attributed to Emilio Salinas.

6 recordsLinked to original sources

PubMed2003

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.

Animals↗

PubMed2003

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.

Animals↗

PubMed2003

Background synaptic activity as a switch between dynamical states in a network.

A bright red light may trigger a sudden motor action in a driver crossing an intersection: stepping at once on the brakes. The same red light, however, may be entirely inconsequential if it appears, say, inside a movie theater. Clearly, context determines whether a particular stimulus will trigger a motor response, but what is the neural correlate of this? How does the nervous system enable or disable whole networks so that they are responsive or not to a given sensory signal? Using theoretical models and computer simulations, I show that networks of neurons have a built-in capacity to switch between two types of dynamic state: one in which activity is low and approximately equal for all units, and another in which different activity distributions are possible and may even change dynamically. This property allows whole circuits to be turned on or off by weak, unstructured inputs. These results are illustrated using networks of integrate-and-fire neurons with diverse architectures. In agreement with the analytic calculations, a uniform background input may determine whether a random network has one or two stable firing levels; it may give rise to randomly alternating firing episodes in a circuit with reciprocal inhibition; and it may regulate the capacity of a center-surround circuit to produce either self-sustained activity or traveling waves. Thus, the functional properties of a network may be drastically modified by a simple, weak signal. This mechanism works as long as the network is able to exhibit stable firing states, or attractors.

Action Potentials↗

PubMed2002

From sensation to action.

Key to understanding somatosensation is the form of how the mechanical stimuli are represented in the evoked neuronal activity of the brain. Here, we focus on studies that address the question of which components of the evoked neuronal activity in the somatosensory system represent the stimulus features. We review experiments that probe whether these neuronal representations are essential to somatosensation. We also discuss recent results that suggest how the somatosensory stimuli are represented in the brain during short-term memory. Finally, we review data that show the neuronal correlates of a decision during somatosensory perception.

Animals↗

PubMed2002

Exploring the cortical evidence of a sensory-discrimination process.

Humans and monkeys have similar abilities to discriminate the difference in frequency between two consecutive mechanical vibrations applied to their fingertips. This task can be conceived as a chain of neural operations: encoding the two consecutive stimuli, maintaining the first stimulus in working memory, comparing the second stimulus with the memory trace left by the first stimulus and communicating the result of the comparison to the motor apparatus. We studied this chain of neural operations by recording and manipulating neurons from different areas of the cerebral cortex while monkeys performed the task. The results indicate that neurons of the primary somatosensory cortex (S1) generate a neural representation of vibrotactile stimuli which correlates closely with psychophysical performance. Discrimination based on microstimulation patterns injected into clusters of S1 neurons is indistinguishable from that produced by natural stimuli. Neurons from the secondary somatosensory cortex (S2), prefrontal cortex and medial premotor cortex (MPC) display at different times the trace of the first stimulus during the working-memory component of the task. Neurons from S2 and MPC appear to show the comparison between the two stimuli and correlate with the behavioural decisions. These neural operations may contribute to the sensory-discrimination process studied here.

Animals↗

PubMed2002

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 neuron's 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.

Action Potentials↗