Ann Kennedy
Co-PI (Core Leadership)
Northwestern University
Ann Kennedy, PhD, is a theoretical neuroscientist at Scripps Research in San Diego. She is broadly interested in the neural control of complex and naturalistic behavior, and her research has focused on the use of methods from machine learning, dynamical systems, and control theory to characterize the structure of animal behavior and its control by the brain. She completed postdoctoral training with Dr. David Anderson at California Institute of Technology, where she modeled hypothalamic control of social and fear behaviors. Prior to that, she pursued her PhD at Columbia University with Dr. Larry Abbott, where she modelled information representation and learning in cerebellum-like structures.
Recent ASAP Preprints & Published Papers
Neural Heterogeneity Controls Computations in Spiking Neural Networks
Significance Neurons are the basic information-encoding units in the brain. In contrast to information-encoding units in a computer, neurons are heterogeneous, i.e., they differ substantially in their electrophysiological properties. How does the brain make use of this heterogeneous substrate to carry out its function of processing information and generating adaptive behavior? We analyze a mathematical model of networks of heterogeneous spiking neurons and show that neural heterogeneity provides a previously unconsidered means of controlling computational properties of neural circuits. We furthermore uncover different capacities of inhibitory vs. excitatory heterogeneity to regulate the gating of signals vs. the encoding and decoding of information, respectively. Our results reveal how a mostly overlooked property of the brain—neural heterogeneity—allows for the emergence of computationally specialized networks. Abstract The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does this neural heterogeneity influence macroscopic neural dynamics, and how might it contribute to neural computation? In this work, we use a mean-field model to investigate computation in heterogeneous neural networks, by studying how the heterogeneity of cell spiking thresholds affects three key computational functions of a neural population: the gating, encoding, and decoding of neural signals. Our results suggest that heterogeneity serves different computational functions in different cell types. In inhibitory interneurons, varying the degree of spike threshold heterogeneity allows them to gate the propagation of neural signals in a reciprocally coupled excitatory population. Whereas homogeneous interneurons impose synchronized dynamics that narrow the dynamic repertoire of the excitatory neurons, heterogeneous interneurons act as an inhibitory offset while preserving excitatory neuron function. Spike threshold heterogeneity also controls the entrainment properties of neural networks to periodic input, thus affecting the temporal gating of synaptic inputs. Among excitatory neurons, heterogeneity increases the dimensionality of neural dynamics, improving the network’s capacity to perform decoding tasks. Conversely, homogeneous networks suffer in their capacity for function generation, but excel at encoding signals via multistable dynamic regimes. Drawing from these findings, we propose intra-cell-type heterogeneity as a mechanism for sculpting the computational properties of local circuits of excitatory and inhibitory spiking neurons,…
Macroscopic Dynamics of Neural Networks with Heterogeneous Spiking Thresholds
Mean-field theory links the physiological properties of individual neurons to the emergent dynamics of neural population activity. These models provide an essential tool for studying brain function at different scales; however, for their application to neural populations on large scale, they need to account for differences between distinct neuron types. The Izhikevich single neuron model can account for a broad range of different neuron types and spiking patterns, thus rendering it an optimal candidate for a mean-field theoretic treatment of brain dynamics in heterogeneous networks. Here we derive the mean-field equations for networks of all-to-all coupled Izhikevich neurons with heterogeneous spiking thresholds. Using methods from bifurcation theory, we examine the conditions under which the mean-field theory accurately predicts the dynamics of the Izhikevich neuron network. To this end, we focus on three important features of the Izhikevich model that are subject here to simplifying assumptions: (i) spike-frequency adaptation, (ii) the spike reset conditions, and (iii) the distribution of single-cell spike thresholds across neurons. Our results indicate that, while the mean-field model is not an exact model of the Izhikevich network dynamics, it faithfully captures its different dynamic regimes and phase transitions. We thus present a mean-field model that can represent different neuron types and spiking dynamics. The model comprises biophysical state variables and parameters, incorporates realistic spike resetting conditions, and accounts for heterogeneity in neural spiking thresholds. These features allow for a broad applicability of the model as well as for a direct comparison to experimental data.
