Electrical and Optical Activation of Mesoscale Neural Circuits with Implications for Coding.

Artificial activation of neural circuitry through electrical microstimulation and optogenetic techniques is important for both scientific discovery of circuit function and for engineered approaches to alleviate various disorders of the nervous system. However, evidence suggests that neural activity generated by artificial stimuli differs dramatically from normal circuit function, in terms of both the local neuronal population activity at the site of activation and the propagation to downstream brain structures. The precise nature of these differences and the implications for information processing remain unknown. Here, we used voltage-sensitive dye imaging of primary somatosensory cortex in the anesthetized rat in response to deflections of the facial vibrissae and electrical or optogenetic stimulation of thalamic neurons that project directly to the somatosensory cortex. Although the different inputs produced responses that were similar in terms of the average cortical activation, the variability of the cortical response was strikingly different for artificial versus sensory inputs. Furthermore, electrical microstimulation resulted in highly unnatural spatial activation of cortex, whereas optical input resulted in spatial cortical activation that was similar to that induced by sensory inputs. A thalamocortical network model suggested that observed differences could be explained by differences in the way in which artificial and natural inputs modulate the magnitude and synchrony of population activity. Finally, the variability structure in the response for each case strongly influenced the optimal inputs for driving the pathway from the perspective of an ideal observer of cortical activation when considered in the context of information transmission. SIGNIFICANCE STATEMENT: Artificial activation of neural circuitry through electrical microstimulation and optogenetic techniques is important for both scientific discovery and clinical translation. However, neural activity generated by these artificial means differs dramatically from normal circuit function, both locally and in the propagation to downstream brain structures. The precise nature of these differences and the implications for information processing remain unknown. The significance of this work is in quantifying the differences, elucidating likely mechanisms underlying the differences, and determining the implications for information processing.

Response reliability observed with voltage-sensitive dye imaging of cortical layer 2/3: the probability of activation hypothesis.

A central assertion in the study of neural processing is that our perception of the environment directly reflects the activity of our sensory neurons. This assertion reinforces the intuition that the strength of a sensory input directly modulates the amount of neural activity observed in response to that sensory feature: an increase in the strength of the input yields a graded increase in the amount of neural activity. However, cortical activity across a range of sensory pathways can be sparse, with individual neurons having remarkably low firing rates, often exhibiting suprathreshold activity on only a fraction of experimental trials. To compensate for this observed apparent unreliability, it is assumed that instead the local population of neurons, although not explicitly measured, does reliably represent the strength of the sensory input. This assumption, however, is largely untested. In this study, using wide-field voltage-sensitive dye (VSD) imaging of the somatosensory cortex in the anesthetized rat, we show that whisker deflection velocity, or stimulus strength, is not encoded by the magnitude of the population response at the level of cortex. Instead, modulation of whisker deflection velocity affects the likelihood of the cortical response, impacting the magnitude, rate of change, and spatial extent of the cortical response. An ideal observer analysis of the cortical response points to a probabilistic code based on repeated sampling across cortical columns and/or time, which we refer to as the probability of activation hypothesis. This hypothesis motivates a range of testable predictions for both future electrophysiological and future behavioral studies.

System identification of the nonlinear dynamics in the thalamocortical circuit in response to patterned thalamic microstimulation in vivo.

OBJECTIVE: Nonlinear system identification approaches were used to develop a dynamical model of the network level response to patterns of microstimulation in vivo. APPROACH: The thalamocortical circuit of the rodent vibrissa pathway was the model system, with voltage sensitive dye imaging capturing the cortical response to patterns of stimulation delivered from a single electrode in the ventral posteromedial thalamus. The results of simple paired stimulus experiments formed the basis for the development of a phenomenological model explicitly containing nonlinear elements observed experimentally. The phenomenological model was fit using datasets obtained with impulse train inputs, Poisson-distributed in time and uniformly varying in amplitude. MAIN RESULTS: The phenomenological model explained 58% of the variance in the cortical response to out of sample patterns of thalamic microstimulation. Furthermore, while fit on trial-averaged data, the phenomenological model reproduced single trial response properties when simulated with noise added into the system during stimulus presentation. The simulations indicate that the single trial response properties were dependent on the relative sensitivity of the static nonlinearities in the two stages of the model, and ultimately suggest that electrical stimulation activates local circuitry through linear recruitment, but that this activity propagates in a highly nonlinear fashion to downstream targets. SIGNIFICANCE: The development of nonlinear dynamical models of neural circuitry will guide information delivery for sensory prosthesis applications, and more generally reveal properties of population coding within neural circuits.