Patterns of neuronal activity underlying behavioral decisions

National Institute Of Mental Health

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Abstract

Cortical neurons fire in complex patterns of activity during behavior and cognition. In sensory regions of the cortex, animals' interactions with the sensory world evoke neural activity in the cortex. But not all patterns of cortical activity can be elicited by sensory stimuli. We study how non-sensory, artificially induced activity patterns can be used by animals to make behavioral responses. These studies shed light on the limits of cortical function, and how circuit properties like neural connections constrain the set of activity patterns the cerebral cortex can process. First, we have developed tools to enable the control of activity in the brain with single-cell resolution. State-of-the-art all-optical systems promise unprecedented access to neural activity in vivo, using multiphoton optogenetics to allow simultaneous imaging and control of activity in selected neurons at cellular resolution. However, to achieve wide use of all-optical stimulation and imaging, simple strategies are needed to robustly and stably express opsins and indicators in the same cells. Here, we describe a bicistronic adeno-associated virus (AAV) that expresses both the fast and bright calcium indicator jGCaMP8s, and a soma-targeted (st) and two-photon-activatable opsin, ChrimsonR. With this method, stChrimsonR stimulation with two-photon holography in the visual cortex of mice drives robust spiking in targeted cells, and neural responses to visual sensory stimuli and spontaneous activity are strong and stable. Cells expressing this bicistronic construct show responses to both photostimulation and visual stimulation that are similar to responses measured from cells expressing the same opsin and indicator via separate viruses. This approach is a simple and robust way to prepare neurons in vivo for two-photon holography and imaging. We have also studied plasticity and learning in cortical networks as animals learn to use entirely novel activity patterns induced by optogenetic stimulation. Cerebral cortex supports representations of the world in patterns of neural activity, used by the brain to make decisions and guide behavior. Past work has found diverse, or limited, changes in the primary sensory cortex in response to learning, suggesting that the key computations might occur in downstream regions. Alternatively, sensory cortical changes may be central to learning. We studied cortical learning by using controlled inputs we insert: We trained mice to recognize entirely novel, non-sensory patterns of cortical activity in the primary visual cortex (V1) created by optogenetic stimulation. As animals learned to use these novel patterns, we found that their detection abilities improved by an order of magnitude or more. The behavioral change was accompanied by large increases in V1 neural responses to fixed optogenetic input. Neural response amplification to novel optogenetic inputs had little effect on existing visual sensory responses. A recurrent cortical model shows that this amplification can be achieved by a small mean shift in recurrent network synaptic strength. Amplification would seem to be desirable to improve decision-making in a detection task; therefore, these results suggest that adult recurrent cortical plasticity plays a significant role in improving behavioral performance during learning. We have developed a hypothesis that the extensive recurrent connections within cortical areas filter and amplify certain sequences of input in sensory cortex. Organisms interact with a sensory world that changes dynamically over time. In visual cortex, natural, dynamic sensory input produces sparse, strong responses, yet how these responses are created by brain circuits has been unclear. Here we show that the recurrent network in mouse visual cortex creates sparse responses to natural input, as groups of different neurons interact across time. Using two-photon optogenetics, we measure responses to single natural scenes and find that playing back those neural responses during the correct dynamic context leads to amplification and sparsification. We find a network mechanism for this amplification: input to some cells produces suppression in other local neurons, the suppression is sustained through visual input changes, and suppressed cells produce sublinear, attenuated responses to later-arriving inputs. These data suggest a novel function, sequence amplification, for recurrent connections in sensory cortex. Sparse responses are created as cortical circuits amplify some input sequences and attenuate others. These effects allow the brain to be sensitive to the dynamic and changing aspects of the world, from understanding visual movies we see to processing speech that we hear. Our results suggest that sequential processing, a major role of recurrent connectivity in artificial intelligence or machine learning systems, may also be a major role of recurrent connectivity in biological brains.