Behavioural States
behavioural states
Precise spatio-temporal spike patterns in cortex and model
The cell assembly hypothesis postulates that groups of coordinated neurons form the basis of information processing. Here, we test this hypothesis by analyzing massively parallel spiking activity recorded in monkey motor cortex during a reach-to-grasp experiment for the presence of significant ms-precise spatio-temporal spike patterns (STPs). For this purpose, the parallel spike trains were analyzed for STPs by the SPADE method (Stella et al, 2019, Biosystems), which detects, counts and evaluates spike patterns for their significance by the use of surrogates (Stella et al, 2022 eNeuro). As a result we find STPs in 19/20 data sets (each of 15min) from two monkeys, but only a small fraction of the recorded neurons are involved in STPs. To consider the different behavioral states during the task, we analyzed the data in a quasi time-resolved analysis by dividing the data into behaviorally relevant time epochs. The STPs that occur in the various epochs are specific to behavioral context - in terms of neurons involved and temporal lags between the spikes of the STP. Furthermore we find, that the STPs often share individual neurons across epochs. Since we interprete the occurrence of a particular STP as the signature of a particular active cell assembly, our interpretation is that the neurons multiplex their cell assembly membership. In a related study, we model these findings by networks with embedded synfire chains (Kleinjohann et al, 2022, bioRxiv 2022.08.02.502431).
Relations and Predictions in Brains and Machines
Humans and animals learn and plan with flexibility and efficiency well beyond that of modern Machine Learning methods. This is hypothesized to owe in part to the ability of animals to build structured representations of their environments, and modulate these representations to rapidly adapt to new settings. In the first part of this talk, I will discuss theoretical work describing how learned representations in hippocampus enable rapid adaptation to new goals by learning predictive representations, while entorhinal cortex compresses these predictive representations with spectral methods that support smooth generalization among related states. I will also cover recent work extending this account, in which we show how the predictive model can be adapted to the probabilistic setting to describe a broader array of generalization results in humans and animals, and how entorhinal representations can be modulated to support sample generation optimized for different behavioral states. In the second part of the talk, I will overview some of the ways in which we have combined many of the same mathematical concepts with state-of-the-art deep learning methods to improve efficiency and performance in machine learning applications like physical simulation, relational reasoning, and design.
Causal coupling between neural activity, metabolism, and behavior across the Drosophila brain
Coordinated activity across networks of neurons is a hallmark of both resting and active behavioral states in many species, including worms, flies, fish, mice and humans. These global patterns alter energy metabolism in the brain over seconds to hours, making oxygen consumption and glucose uptake widely used proxies of neural activity. However, whether changes in neural activity are causally related to changes in metabolic flux in intact circuits on the sub-second timescales associated with behavior, is unclear. Moreover, it is unclear whether differences between rest and action are associated with spatiotemporally structured changes in neuronal energy metabolism at the subcellular level. My work combines two-photon microscopy across the fruit fly brain with sensors that allow simultaneous measurements of neural activity and metabolic flux, across both resting and active behavioral states. It demonstrates that neural activity drives changes in metabolic flux, creating a tight coupling between these signals that can be measured across large-scale brain networks. Further, using local optogenetic perturbation, I show that even transient increases in neural activity result in rapid and persistent increases in cytosolic ATP, suggesting that neuronal metabolism predictively allocates resources to meet the energy demands of future neural activity. Finally, these studies reveal that the initiation of even minimal behavioral movements causes large-scale changes in the pattern of neural activity and energy metabolism, revealing unexpectedly widespread engagement of the central brain.
State-dependent cortical circuits
Spontaneous and sensory-evoked cortical activity is highly state-dependent, promoting the functional flexibility of cortical circuits underlying perception and cognition. Using neural recordings in combination with behavioral state monitoring, we find that arousal and motor activity have complementary roles in regulating local cortical operations, providing dynamic control of sensory encoding. These changes in encoding are linked to altered performance on perceptual tasks. Neuromodulators, such as acetylcholine, may regulate this state-dependent flexibility of cortical network function. We therefore recently developed an approach for dual mesoscopic imaging of acetylcholine release and neural activity across the entire cortical mantle in behaving mice. We find spatiotemporally heterogeneous patterns of cholinergic signaling across the cortex. Transitions between distinct behavioral states reorganize the structure of large-scale cortico-cortical networks and differentially regulate the relationship between cholinergic signals and neural activity. Together, our findings suggest dynamic state-dependent regulation of cortical network operations at the levels of both local and large-scale circuits. Zoom Meeting ID: 964 8138 3003 Contact host if you cannot connect.
Exploring the relationship between the LFP signal and Behavioral States
This talk will focus on different aspects of the Local Field Potential (LFP) signal. Classically, LFP fluctuations are related to changes in the functional state of the cortex. Yet, the mechanisms linking LFP changes with the state of the cortex are not well understood. The presentation will start with a brief explanation of the main oscillatory components of the LFP signal, how these different oscillatory components are generated at cortical microcircuits, and how their dynamics can be studied across multiple areas. Thereafter, a case study of a patient with akinetic mutism will be presented, linking cortical states with the behavior of the patient, as well as some preliminary results about how the LF cortical microcircuit dynamic changes modulate different cortical states and how these changes are reflected in the LFP signal
State-dependent cortical circuits
Spontaneous and sensory-evoked cortical activity is highly state-dependent, promoting the functional flexibility of cortical circuits underlying perception and cognition. Using neural recordings in combination with behavioral state monitoring, we find that arousal and motor activity have complementary roles in regulating local cortical operations, providing dynamic control of sensory encoding. These changes in encoding are linked to altered performance on perceptual tasks. Neuromodulators, such as acetylcholine, may regulate this state-dependent flexibility of cortical network function. We therefore recently developed an approach for dual mesoscopic imaging of acetylcholine release and neural activity across the entire cortical mantle in behaving mice. We find spatiotemporally heterogeneous patterns of cholinergic signaling across the cortex. Transitions between distinct behavioral states reorganize the structure of large-scale cortico-cortical networks and differentially regulate the relationship between cholinergic signals and neural activity. Together, our findings suggest dynamic state-dependent regulation of cortical network operations at the levels of both local and large-scale circuits.
State-dependent regulation of cortical circuits
Spontaneous and sensory-evoked cortical activity is highly state-dependent, promoting the functional flexibility of cortical circuits underlying perception and cognition. Using neural recordings in combination with behavioral state monitoring, we find that arousal and motor activity have complementary roles in regulating local cortical operations, providing dynamic control of sensory encoding. These changes in encoding are linked to altered performance on perceptual tasks. Neuromodulators, such as acetylcholine, may regulate this state-dependent flexibility of cortical network function. We therefore recently developed an approach for dual mesoscopic imaging of acetylcholine release and neural activity across the entire cortical mantle in behaving mice. We find spatiotemporally heterogeneous patterns of cholinergic signaling across the cortex. Transitions between distinct behavioral states reorganize the structure of large-scale cortico-cortical networks and differentially regulate the relationship between cholinergic signals and neural activity. Together, our findings suggest dynamic state-dependent regulation of cortical network operations at the levels of both local and large-scale circuits.
High precision coding in visual cortex
Single neurons in visual cortex provide unreliable measurements of visual features due to their high trial-to-trial variability. It is not known if this “noise” extends its effects over large neural populations to impair the global encoding of stimuli. We recorded simultaneously from ∼20,000 neurons in mouse primary visual cortex (V1) and found that the neural populations had discrimination thresholds of ∼0.34° in an orientation decoding task. These thresholds were nearly 100 times smaller than those reported behaviourally in mice. The discrepancy between neural and behavioural discrimination could not be explained by the types of stimuli we used, by behavioural states or by the sequential nature of perceptual learning tasks. Furthermore, higher-order visual areas lateral to V1 could be decoded equally well. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.