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      Thalamic regulation of switching between cortical representations enables cognitive flexibility

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      1 , 2 , 3 , 1 , 2 , *
      Nature neuroscience

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          Abstract

          Interactions between the prefrontal cortex (PFC) and mediodorsal thalamus (MD) are critical for cognitive flexibility, yet the underlying computations are unknown. To investigate fronto-thalamic substrates of cognitive flexibility, we developed a behavioral task, where mice switched between different sets of learned cues that guided attention towards either visual or auditory targets. We found that PFC responses reflected both the individual cues and their meaning as task rules, indicating a hierarchical cue-to-rule transformation. Conversely, MD responses reflected the statistical regularity of cue presentation, and were required for switching between such experimentally-specified cueing contexts. A subset of these thalamic responses sustained context-relevant PFC representations, while another suppressed the context-irrelevant ones. Through modeling and experimental validation, we find that thalamic-mediated suppression may not only reduce PFC representational interference but could also preserve unused cortical traces for future use. Overall, our study provides a computational foundation for thalamic engagement in cognitive flexibility.

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          Most cited references44

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          Real-time computing without stable states: a new framework for neural computation based on perturbations.

          A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.
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            Spatio-temporal correlations and visual signalling in a complete neuronal population.

            Statistical dependencies in the responses of sensory neurons govern both the amount of stimulus information conveyed and the means by which downstream neurons can extract it. Although a variety of measurements indicate the existence of such dependencies, their origin and importance for neural coding are poorly understood. Here we analyse the functional significance of correlated firing in a complete population of macaque parasol retinal ganglion cells using a model of multi-neuron spike responses. The model, with parameters fit directly to physiological data, simultaneously captures both the stimulus dependence and detailed spatio-temporal correlations in population responses, and provides two insights into the structure of the neural code. First, neural encoding at the population level is less noisy than one would expect from the variability of individual neurons: spike times are more precise, and can be predicted more accurately when the spiking of neighbouring neurons is taken into account. Second, correlations provide additional sensory information: optimal, model-based decoding that exploits the response correlation structure extracts 20% more information about the visual scene than decoding under the assumption of independence, and preserves 40% more visual information than optimal linear decoding. This model-based approach reveals the role of correlated activity in the retinal coding of visual stimuli, and provides a general framework for understanding the importance of correlated activity in populations of neurons.
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              Dissociation in prefrontal cortex of affective and attentional shifts.

              The prefrontal cortex is implicated in such human characteristics as volition, planning, abstract reasoning and affect. Frontal-lobe damage can cause disinhibition such that the behaviour of a subject is guided by previously acquired responses that are inappropriate to the current situation. Here we demonstrate that disinhibition, or a loss of inhibitory control, can be selective for particular cognitive functions and that different regions of the prefrontal cortex provide inhibitory control in different aspects of cognitive processing. Thus, whereas damage to the lateral prefrontal cortex (Brodmann's area 9) in monkeys causes a loss of inhibitory control in attentional selection, damage to the orbito-frontal cortex in monkeys causes a loss of inhibitory control in 'affective' processing, thereby impairing the ability to alter behaviour in response to fluctuations in the emotional significance of stimuli. These findings not only support the view that the prefrontal cortex has multiple functions, but also provide evidence for the distribution of different cognitive functions within specific regions of prefrontal cortex.
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                Author and article information

                Journal
                9809671
                21092
                Nat Neurosci
                Nat. Neurosci.
                Nature neuroscience
                1097-6256
                1546-1726
                4 October 2018
                19 November 2018
                December 2018
                15 May 2020
                : 21
                : 12
                : 1753-1763
                Affiliations
                [1 ]McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139
                [2 ]Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
                [3 ]Neural Network Dynamics and Computation Group, Institute for Genetics, University of Bonn, Kirschallee 1, 53115 Bonn, Germany
                Author notes

                Author contributions

                RVR conceived and performed experiments, analyzed and interpreted data, and wrote the paper. AG developed, simulated and analyzed the thalamo-cortical computational model. MMH conceived and supervised experiments, analyzed and interpreted the data and wrote the paper. MMH also acquired funding.

                [* ]Correspondence to: Michael M. Halassa, mhalassa@ 123456mit.edu
                Article
                NIHMS1508796
                10.1038/s41593-018-0269-z
                7225728
                30455456
                0ec5025a-5953-4efb-8e78-44486fd9c900

                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

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