Biology subjects

Doiron, B. D.

Publications and source records attributed to Doiron, B. D..

3 recordsLinked to original sources

bioRxiv2020

The mechanics of Temporal Interference stimulation

We utilize single neuron models to understand mechanisms behind Temporal Interference (TI) stimulation (also called "Interferential Stimulation"). We say that a neuron exhibits TI stimulation if it does not fire for a high-frequency sinusoidal input, but fires when the input is a low-frequency modulation of the high-frequency sinusoid (specifically that generated by addition of two high frequency sinusoids with a small difference in their frequencies), while the maximum amplitude is kept the same in both cases. Our key observation - that holds for both FitzHugh-Nagumo and Hodgkin-Huxley neuron models - is that for neuron models that do exhibit TI stimulation, a high frequency pure sinusoidal input results in a current balance between inward and outward currents. This current balance leads to a subthreshold periodic orbit that keeps the membrane potential from spiking for sinusoidal inputs. However, the balance is disturbed when the envelope of the sinusoids is modulated with a high slope: the fast-changing envelope activates fast depolarizing currents without giving slow outward currents time to respond. This imbalance causes the membrane potential to build up, causing the neuron to fire. This mechanistic understanding can help design current waveforms for neurons that exhibit TI stimulation, and also help classify which neuron-types may or may not exhibit TI stimulation.

neuroscience↗

bioRxiv2020

Internally generated population activity in cortical networks hinders information transmission

How neuronal variability impacts neuronal codes is a central question in systems neuroscience, often with complex and model dependent answers. Most population models are parametric, with a tacitly assumed structure of neuronal tuning and population-wide variability. While these models provide key insights, they purposely divorce any mechanistic relationship between trial average and trial variable neuronal activity. By contrast, circuit based models produce activity with response statistics that are reflection of the underlying circuit structure, and thus any relations between trial averaged and trial variable activity are emergent rather than assumed. In this work, we study information transfer in networks of spatially ordered spiking neuron models with strong excitatory and inhibitory interactions, capable of producing rich population-wide neuronal variability. Motivated by work in the visual system we embed a columnar stimulus orientation map in the network and measure the population estimation of an orientated input. We show that the spatial structure of feedforward and recurrent connectivity are critical determinants for population code performance. In particular, when network wiring supports stable firing rate activity then with a sufficiently large number of decoded neurons all available stimulus information is transmitted. However, if the inhibitory projections place network activity in a pattern forming regime then the population-wide dynamics compromise information flow. In total, network connectivity determines both the stimulus tuning as well as internally generated population-wide fluctuations and thereby dictates population code performance in complicated ways where modeling efforts provide essential understanding.

neuroscience↗

bioRxiv2017

Circuit-based models of shared variability in cortical networks

Trial-to-trial variability is a reflection of the circuitry and cellular physiology that makeup a neuronal network. A pervasive yet puzzling feature of cortical circuits is that despite their complex wiring, population-wide shared spiking variability is low dimensional with all neurons fluctuating en masse. Previous model cortical networks are at loss to explain this global variability, and rather assume it is from external sources. We show that if the spatial and temporal scales of inhibitory coupling match known physiology, model spiking neurons internally generate low dimensional shared variability that captures the properties of in vivo population recordings along the visual pathway. Shifting spatial attention into the receptive field of visual neurons has been shown to reduce low dimensional shared variability within a brain area, yet increase the variability shared between areas. A top-down modulation of inhibitory neurons in our network provides a parsimonious mechanism for this attentional modulation, providing support for our theory of cortical variability. Our work provides a critical and previously missing mechanistic link between observed cortical circuit structure and realistic population-wide shared neuronal variability and its modulation.

neuroscience↗