Welcome to the MacLean Lab
Unraveling Brain Complexity: Connecting Network Dynamics to BehaviorWhat We Do
Our research aims to create a comprehensive understanding of the connections between circuit mechanisms, dynamics, and computation within the neocortex. By forging links among these interrelated components, our goal is to uncover how networks of neurons contribute to the emergence of natural behavior.
To accomplish our objectives, we employ a wide range of cutting-edge techniques and approaches grounded in complexity science. Our data collection involves the use of advanced multineuronal recording methods, providing a detailed insight into the dynamics of neuron networks at the single-cell resolution. This allows us to examine brain activity in connection with specific behavioral instances. By correlating behavior with network dynamics across various instances, we gain valuable insights into cortical information processing.
Nevertheless, we acknowledge that data alone cannot offer a comprehensive understanding of intricate brain processes. Hence, we employ powerful computational tools to rigorously analyze the data and to simulate and train neuronal networks based on the neocortex. Modeling provides us with the capability to test diverse theories and hypotheses, yielding valuable insights before proceeding with additional behavioral experiments. This integrated approach enables us to unveil deeper connections between neural dynamics and behavior.
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Recent Projects
Mechanistic basis of dynamic and heterogeneous divisive normalization in visual cortex
Neocortical computation emerges from the dynamic interplay of excitation and inhibition, operating in a loose balance regime where recurrent and external inputs contribute comparably to neuronal activity. Neurons display broad heterogeneity in synaptic inputs and firing rates, making it essential to explain the full distribution of responses, not just the mean, when elucidating mechanisms of dynamics and computation. We examine divisive normalization in mouse visual cortex using population calcium imaging of excitatory and parvalbumin (PV) inhibitory neurons, combined with computational models of varying complexity, including stablized supralinear networks and the Allen Institute’s microcircuit model. We found that suppression in PV neurons was transiently reduced, driven by the dynamics of subcortical input, and that heterogeneity in suppression strength was linked to population correlations, variability in excitatory-inhibitory balance, and suppression of both subcortical and local cortical inputs.
Stable encoding of the natural variability of a dexterous motor skill in the cortex of freely behaving mice
The stability over time of the relationship between neural activity and a given external variable remains a matter of dispute in many brain areas. Whether neural representations are stable or changing (“drifting”) has massive implications for how those representations could be read out by downstream neural populations and incorporated into neural computations driving perception, behavior, and memory. Notably, studies of stability of neural coding for movements have generally focused on stereotyped, overtrained behaviors. However, skilled, goal-directed movements can exhibit trial-to-trial variability even in experts, particularly in response to dynamic environmental conditions or when perfect repetition is not required for success. Identifying where, to what extent, and how stably this variability is encoded in the nervous system will yield insight into how such learned movements are robustly maintained over time yet flexibly executed on each trial. We record calcium fluorescence activity in motor cortex—a key node in the multi-areal network responsible for movement control—in freely-moving mice performing a self-paced, precision reach-to-grasp task. High trial counts and rich single-trial variability enable rigorous statistical analysis of moment-to-moment movement encoding across matched behavioral sets over five days. We found that individual neurons in motor cortex stably encode details of paw, digit, and head movements, suggesting that reliable contributions from single cells support the consistent execution of skilled movements over time, even in complex, sensory-guided tasks like reach-to-grasp.
Task success in trained spiking neuronal network models coincides with the emergence of cross-tuned inhibition
The neocortex is composed of spiking neuronal units interconnected in a sparse, recurrent network. Neuronal networks exhibit spiking activity that transforms sensory inputs into appropriate behavioral outputs. In this study, we train biologically realistic spiking neural network (SNN) models to identify the architectural changes which enable task-appropriate computations. Specifically, we employ a binary state change detection task, where each state is defined by motion energy. This task mirrors behavioral paradigms that mice perform in the lab. SNNs are composed of excitatory and inhibitory units randomly interconnected with connection likelihoods and strengths matched to observations from mouse neocortex. Following training, we discover that SNNs selectively adjust firing rates depending on state, and that excitatory and inhibitory connectivity between input and recurrent layers change in accordance with this rate modulation. Input channels that exhibit bias to one specific motion energy input develop stronger connections to recurrent excitatory units during training, while channels that exhibit bias to the other input develop stronger connections to inhibitory units. Furthermore, recurrent inhibitory units which were tuned to one input strengthened their connections to recurrent units of the opposite tuning. The convergence of trained network models on the specific pattern of cross-tuned inhibition serves as further validation of the fundamental role played by interneurons and their connections in shaping dynamics and information processing within the neocortical circuits.