Disk-Drive-Like Operations in the Hippocampus

Wilten Nicola, David Dupret, Claudia Clopath

The brain performs well-defined operations serving behaviour. Are these operations computer-like in any way, allowing for testable predictions? We constructed a tripartite spiking neural network model where the hippocampus is explicitly described as a disk drive with a rotating disk, an actuator arm, and a read/write head. In this Neural Disk Drive (NDD) model, hippocampal oscillations map to disk rotations in the rotating disk network while attractor dynamics in the actuator arm network point to ``tracks" on the disk. The read/write head then writes information onto these tracks, which have temporally-structured spikes. Tracks can be replayed during hippocampal ripples for consolidation. We confirmed the existence of interneuron-ring-sequences, predicted by the rotating disk network, in experimental data. These findings establish the hippocampus as a brain region displaying computer-like operations.

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Embedded Chimeras in Recurrent Neural Networks

Maria Masoliver, Joern Davidsen and Wilten Nicola

https://www.nature.com/articles/s42005-022-00984-2

Masoliver, M., Davidsen, J., & Nicola, W. (2022). Embedded chimera states in recurrent neural networks. Communications Physics, 5(1), 1-9.

Synchronized and partially synchronized brain activity plays a key role in normal cognition and in some neurological disorders, such as epilepsy. However, the mechanism by which synchrony and asynchrony co-exist in a population of neurons remains elusive. Chimera states, where synchrony and asynchrony coexist, have been documented only for precisely specified connectivity and network topologies. Here, we demonstrate how Chimeras can emerge in Recurrent Neural Networks (RNN) by training the networks to display Chimeras with machine learning. These solutions, which we refer to as embedded Chimeras, are generically produced by RNNs with connectivity matrices only slightly perturbed from random networks. We also demonstrate that learning is robust to different biological constraints, such as the excitatory/inhibitory classification of neurons (Dale's law), and the sparsity of connections in neural circuits. The RNN can also be trained to switch Chimera solutions: an input pulse can trigger the RNN to switch the synchronized and the unsynchronized groups of the embedded Chimera, reminiscent of uni-hemispheric sleep in a variety of animals. Our results imply that the emergence of Chimeras is quite generic at the meso- and macroscale suggesting their general relevance in neuroscience.

Particle-Swarm Based Modelling Reveals TwoDistinct Classes of CRH-PVN Neurons

Ewandson L. Lameu, Neilen P. Rasiah, Dinara V. Baimoukhametova, Spencer Loewen, Jaideep S. Bains, Wilten Nicola*

Accepted in the Journal of Physiology!

Electrophysiological data can provide detailed information of single neurons’ dynamical features and responses to stimuli. Rapidly modeling electrophysiological data for inferring network-level behaviours, however, remains challenging. Here, we investigate how modeled single neuron dynamics lead to network-level responses in the paraventricular nucleus of the hypothalamus (PVN). Recordings of corticotropin-releasing-hormone neurons from the PVN (CRH-PVN) were performed using whole-cell current-clamp. These, neurons, which initiate the endocrine response to stress, were rapidly and automatically fit to a modified Adaptive Exponential Integrate and Fire model(AdEx) with Particle Swarm Optimization (PSO). All CRH-PVN neurons were accurately fit by the AdEx model with PSO. Multiple sets of parameters were foundthat reliably reproduced current-clamp traces for any single neuron. Despite multiplesolutions, the dynamical features of the models such as the rheobase current levels, fixed points, and bifurcations, were shown to be stable across fits. We found that CRH-PVN neurons can be divided into two sub-types according to their bifurcationat the onset of firing: saddles (integrators) and sub-critical Hopf (resonators). We constructed networks of these fit (CRHPVN) model neurons to investigate the net-work level responses of CRH-PVN neurons in the PVN. The simulations showed that the neuron bifurcation type is crucial to establish how the system responds to external stimuli. Specifically, the CRH-PVN-resonators maintain baseline firing in networks even when all inputs are inhibitory. The dynamics of a small subset of CRH-PVN neurons may be critical to maintaining a baseline firing tone in the PVN.

Photons guided by axons may enable
backpropagation-based learning in the brain

Parisa Zarkeshian, Taylor Kergan1, Roohollah Ghobadi, Wilten Nicola, and
Christoph Simon

Submitted (2022)

Despite great advances in explaining synaptic plasticity and neuron function, a complete understanding of the brain’s learning mechanisms is still missing. Artificial neural networks provide a powerful learning paradigm through the backpropagation algorithm which modifies synaptic weights by using feedback connections. Backpropagation requires extensive communication of information back through the layers of a network. This has been argued to be biologically implausible and it is not clear whether backpropagation can be realized in the brain. Here we suggest that biophotons guided by axons provide a potential
channel for backward transmission of information in the brain. Biophotons have been experimentally shown to be produced in the brain, yet their purpose is not understood. We propose that biophotons can propagate from each post-synaptic neuron to
its pre-synaptic one to carry the required information backward. To reflect the stochastic character of biophoton emissions, our model includes the stochastic backward transmission of teaching signals. We demonstrate that a three-layered network of neurons can learn the MNIST handwritten digit classification task using our proposed backpropagation-like algorithm with stochastic photonic feedback. We model realistic restrictions and show that our system still learns the task for low rates of biophoton emission, information-limited (one bit per photon) backward transmission, and in the presence of noise. Our results suggest a new functionality for biophotons and provide an alternate mechanism for backward transmission in the brain