@article {69104, title = {Connectivity underlying motor cortex activity during naturalistic goal-directed behavior.}, journal = {bioRxiv}, year = {2023}, month = {2023 Nov 26}, abstract = {

Neural representations of information are shaped by local network interactions. Previous studies linking neural coding and cortical connectivity focused on stimulus selectivity in the sensory cortex\ 1{\textendash}4. Here we study neural activity in the motor cortex during naturalistic behavior in which mice gathered rewards with multidirectional tongue reaching. This behavior does not require training and thus allowed us to probe neural coding and connectivity in motor cortex before its activity is shaped by learning a specific task. Neurons typically responded during and after reaching movements and exhibited conjunctive tuning to target location and reward outcome. We used an all-optical\ 5,4,6,7\ method for large-scale causal functional connectivity mapping\ in vivo. Mapping connectivity between \> 20,000,000 excitatory neuronal pairs revealed fine-scale columnar architecture in layer 2/3 of the motor cortex. Neurons displayed local (\< 100 {\textmu}m) like-to-like connectivity according to target-location tuning, and inhibition over longer spatial scales. Connectivity patterns comprised a continuum, with abundant weakly connected neurons and sparse strongly connected neurons that function as network hubs. Hub neurons were weakly tuned to target-location and reward-outcome but strongly influenced neighboring neurons. This network of neurons, encoding location and outcome of movements to different motor goals, may be a general substrate for rapid learning of complex, goal-directed behaviors.

}, doi = {10.1101/2023.11.25.568673}, url = {https://doi.org/10.1101/2023.11.25.568673}, author = {Arseny Finkelstein and Kayvon Daie and M{\'a}rton R{\'o}zsa and Ran Darshan and Karel Svoboda} } @article {68537, title = {Reward expectations direct learning and drive operant matching in Drosophila}, journal = {Proceedings of the National Academy of Sciences of the U.S.A.}, volume = {120}, year = {2023}, month = {2023 Sep 26}, pages = {e2221415120}, abstract = {

Foraging animals must use decision-making strategies that dynamically adapt to the changing availability of rewards in the environment. A wide diversity of animals do this by distributing their choices in proportion to the rewards received from each option, Herrnstein{\textquoteright}s operant matching law. Theoretical work suggests an elegant mechanistic explanation for this ubiquitous behavior, as operant matching follows automatically from simple synaptic plasticity rules acting within behaviorally relevant neural circuits. However, no past work has mapped operant matching onto plasticity mechanisms in the brain, leaving the biological relevance of the theory unclear. Here we discovered operant matching in\ Drosophila\ and showed that it requires synaptic plasticity that acts in the mushroom body and incorporates the expectation of reward. We began by developing a novel behavioral paradigm to measure choices from individual flies as they learn to associate odor cues with probabilistic rewards. We then built a model of the fly mushroom body to explain each fly{\textquoteright}s sequential choice behavior using a family of biologically-realistic synaptic plasticity rules. As predicted by past theoretical work, we found that synaptic plasticity rules could explain fly matching behavior by incorporating stimulus expectations, reward expectations, or both. However, by optogenetically bypassing the representation of reward expectation, we abolished matching behavior and showed that the plasticity rule must specifically incorporate reward expectations. Altogether, these results reveal the first synaptic level mechanisms of operant matching and provide compelling evidence for the role of reward expectation signals in the fly brain.

}, doi = {10.1073/pnas.2221415120}, url = {https://doi.org/10.1073/pnas.2221415120}, author = {Adithya E. Rajagopalan and Ran Darshan and Karen L. Hibbard and James E. Fitzgerald and Glenn C. Turner} } @article {68466, title = {A scalable implementation of the recursive least-squares algorithm for training spiking neural networks}, journal = {Frontiers in Neuroinformatics}, year = {2023}, month = {2023 Jun 27}, abstract = {

Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a prominent tool to study computations in the brain. With an increasing size and complexity of neural recordings, there is a need for fast algorithms that can scale to large datasets. We present optimized CPU and GPU implementations of the recursive least-squares algorithm in spiking neural networks. The GPU implementation allows training networks to reproduce neural activity of an order of millions neurons at order of magnitude times faster than the CPU implementation. We demonstrate this by applying our algorithm to reproduce the activity of\ \>\ 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables efficient training of large-scale spiking models, thus allowing for\ in-silico\ study of the dynamics and connectivity underlying multi-area computations.

}, doi = {10.3389/fninf.2023.1099510}, url = {https://doi.org/10.3389/fninf.2023.1099510}, author = {Benjamin J. Arthur and Christopher M. Kim and Susu Chen and Stephan Preibisch and Ran Darshan} } @article {68166, title = {Distributing task-related neural activity across a cortical network through task-independent connections}, journal = {bioRxiv}, year = {2022}, month = {2022 Jun 18}, abstract = {

Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to broad activity changes. Here we trained a limited subset of neurons in a spiking network with strong synaptic interactions to reproduce the activity of neurons in the motor cortex during a decision-making task. We found that task-related activity, resembling the neural data, emerged across the network, even in the untrained neurons. Analysis of trained networks showed that strong untrained synapses, which were independent of the task and determined the dynamical state of the network, mediated the spread of task-related activity. Optogenetic perturbations suggest that the motor cortex is strongly-coupled, supporting the applicability of the mechanism to cortical networks. Our results reveal a cortical mechanism that facilitates distributed representations of task-variables by spreading the activity from a subset of plastic neurons to the entire network through task-independent strong synapses.

}, doi = {10.1101/2022.06.17.496618}, url = {https://doi.org/10.1101/2022.06.17.496618}, author = {Christopher M. Kim and Arseny Finkelstein and Carson C. Chow and Karel Svoboda and Ran Darshan} } @article {67306, title = {Learning to represent continuous variables in heterogeneous neural networks}, journal = {Cell Reports}, volume = {39}, year = {2022}, month = {2022 Apr 05}, pages = {110612}, abstract = {

Manifold attractors are a key framework for understanding how continuous variables, such as position or head direction, are encoded in the brain. In this framework, the variable is represented along a continuum of persistent neuronal states which forms a manifold attactor. Neural networks with symmetric synaptic connectivity that can implement manifold attractors have become the dominant model in this framework. In addition to a symmetric connectome, these networks imply homogeneity of individual-neuron tuning curves and symmetry of the representational space; these features are largely inconsistent with neurobiological data. Here, we developed a theory for computations based on manifold attractors in trained neural networks and show how these manifolds can cope with diverse neuronal responses, imperfections in the geometry of the manifold and a high level of synaptic heterogeneity. In such heterogeneous trained networks, a continuous representational space emerges from a small set of stimuli used for training. Furthermore, we find that the network response to external inputs depends on the geometry of the representation and on the level of synaptic heterogeneity in an analytically tractable and interpretable way. Finally, we show that a too complex geometry of the neuronal representation impairs the attractiveness of the manifold and may lead to its destabilization. Our framework reveals that continuous features can be represented in the recurrent dynamics of heterogeneous networks without assuming unrealistic symmetry. It suggests that the representational space of putative manifold attractors in the brain dictates the dynamics in their vicinity.

}, doi = {10.1016/j.celrep.2022.110612}, url = {https://doi.org/10.1016/j.celrep.2022.110612}, author = {Ran Darshan and Alexander Rivkind} } @article {67307, title = {Neuronal activity and learning in local cortical networks are modulated by the action-perception state}, journal = {bioRxiv}, year = {2019}, month = {2019 Feb 01}, abstract = {

During sensorimotor learning, neuronal networks change to optimize the associations between action and perception. In this study, we examine how the brain harnesses neuronal patterns that correspond to the current action-perception state during learning. To this end, we recorded activity from motor cortex while monkeys either performed a familiar motor task (movement-state) or learned to control the firing rate of a target neuron using a brain-machine interface (BMI-state). Before learning, monkeys were placed in an observation-state, where no action was required. We found that neuronal patterns during the BMI-state were markedly different from the movement-state patterns. BMI-state patterns were initially similar to those in the observation-state and evolved to produce an increase in the firing rate of the target neuron. The overall activity of the non-target neurons remained similar after learning, suggesting that excitatory-inhibitory balance was maintained. Indeed, a novel neural-level reinforcement-learning network model operating in a chaotic regime of balanced excitation and inhibition predicts our results in detail. We conclude that during BMI learning, the brain can adapt patterns corresponding to the current action-perception state to gain rewards. Moreover, our results show that we can predict activity changes that occur during learning based on the pre-learning activity. This new finding may serve as a key step toward clinical brain-machine interface applications to modify impaired brain activity.

}, doi = {10.1101/537613}, url = {https://doi.org/10.1101/537613}, author = {Ben Engelhard and Ran Darshan and Nofar Ozeri-Engelhard and Zvi Israel and Uri Werner-Reiss and David Hansel and Hagai Bergman and Eilon Vaadia} }