Mainak Patel

Stimulus encoding within the barn owl optic tectum using gamma oscillations vs. spike rate: A modeling approach

The optic tectum of the barn owl is a multimodal structure with multiple layers, with each layer topographically organized according to spatial receptive field. The response of a site to a stimulus can be measured as either spike rate or local field potential (LFP) gamma (25–90 Hz) power; within superficial layers, spike rate and gamma power spatial tuning curves are narrow and contrast-response functions rise slowly. Within deeper layers, however, spike rate tuning curves broaden and gamma power contrast-response functions sharpen. In this work, we employ a computational model to describe the inputs required to generate these transformations from superficial to deep layers and show that gamma power and spike rate can act as parallel information processing streams.

Decoding synchronized oscillations within the brain: Phase-delayed inhibition provides a robust mechanism for creating a sharp synchrony filter

The widespread presence of synchronized neuronal oscillations within the brain suggests that a mechanism must exist that is capable of decoding such activity. Two realistic designs for such a decoder include: (1) a read-out neuron with a high spike threshold, or (2) a phase-delayed inhibition network motif. Despite requiring a more elaborate network architecture, phase-delayed inhibition has been observed in multiple systems, suggesting that it may provide inherent advantages over simply imposing a high spike threshold. In this work, we use a computational and mathematical approach to investigate the efficacy of the phase-delayed inhibition motif in detecting synchronized oscillations. We show that phase-delayed inhibition is capable of creating a synchrony detector with sharp synchrony filtering properties that depend critically on the time course of inputs. Additionally, we show that phase-delayed inhibition creates a synchrony filter that is far more robust than that created by a high spike threshold.

A large-scale model of the locust antennal lobe

The antennal lobe (AL) is the primary structure within the locust’s brain that receives information from olfactory receptor neurons (ORNs) within the antennae. Different odors activate distinct subsets of ORNs, implying that neuronal signals at the level of the antennae encode odors combinatorially. Within the AL, however, different odors produce signals with long-lasting dynamic transients carried by overlapping neural ensembles, suggesting a more complex coding scheme. In this work we use a large-scale point neuron model of the locust AL to investigate this shift in stimulus encoding and potential consequences for odor discrimination. Consistent with experiment, our model produces stimulus-sensitive, dynamically evolving populations of active AL neurons. Our model relies critically on the persistence time-scale associated with ORN input to the AL, sparse connectivity among projection neurons, and a synaptic slow inhibitory mechanism. Collectively, these architectural features can generate network odor representations of considerably higher dimension than would be generated by a direct feed-forward representation of stimulus space.

A Simplified model of mutually inhibitory sleep-active and wake-active neuronal populations employing a noise-based switching mechanism

Infant rats switch randomly between the sleeping and waking states; during early infancy (up to postnatal day 8), sleep and wake bouts are random, brief (with means on the order of several seconds) and exponentially distributed, with the length of a particular bout independent of the length of prior bouts. As the rat ages during this early period, mean sleep and wake bout lengths gradually increase, though sleep and wake bouts remain exponentially distributed. Additionally, sleep and wake bouts are regulated independently of each other - alterations in the development of sleep (wake) bouts has no impact on the regulation wake (sleep) bouts. Sleep and wake bout behavior is associated with the activity of mutually inhibitory sleep-active and wake-active brainstem populations. In this work, I employ a simplified biophysical model of two mutually inhibitory populations consisting of ten integrate-and-fire neurons each and a noise-based switching mechanism. I show that such a noise-based switching mechanism naturally accounts for the experimentally observed features of sleep-wake switching during early infancy - random alternating activity bouts occur as a consequence of noise (provided inhibition is strong relative to excitation), bout durations are exponential (due to a lack of memory within the system), and cross-population inhibition or intrapopulation excitatory coupling provide mechanisms for changing and independently regulated sleep and wake bout means.

Switching mechanisms and bout times in a pair of reciprocally inhibitory neurons

Within the appropriate parameter regime, a deterministic model of a pair of mutually inhibitory neurons receiving excitatory driving currents exhibits bistability—each of the two stable states corresponds to one neuron being active and the other being quiescent. The presence of noise in the driving currents results in a system that randomly switches back and forth between these two states, causing alternating bouts of spiking activity. In this work, we examine the random bout durations of the two neurons and dependence on system parameters. We find that bout durations of each neuron are exponentially distributed, with changes in system parameters altering only the mean of the distribution. Synaptic inhibition independently controls the bout durations of the two neurons—the mean bout time of a neuron is a function of efferent (or outgoing) inhibition, and is independent of afferent (or incoming) inhibition. Furthermore, we find that the mean bout time of a neuron exhibits a critical dependence on the time course (rather than amplitude) of efferent inhibition—mean bout time of a neuron grows exponentially with the time course of efferent inhibition, and the growth rate of this exponential function depends only on the excitatory driving current to that neuron (and not on any other system parameters). We discuss the relevance of our results to the regulation of sleep-wake cycling by medullary and pontine structures within the brain.

Encoding with synchrony: Phase-delayed inhibition allows for reliable and specific stimulus detection

Synchronized oscillations are observed in a diverse array of neuronal systems, suggesting that synchrony represents a common mechanism used by the brain to encode and relay information. Coherent population activity can be deciphered by a decoder neuron with a high spike threshold or by a decoder using phase-delayed inhibition. These two mechanisms are fundamentally different - a high spike threshold detects a minimum number of synchronous input spikes (absolute synchrony), while phase-delayed inhibition requires a fixed fraction of incoming spikes to be synchronous (relative synchrony). We show that, in a system with noisy encoders where stimuli are encoded through synchrony, phase-delayed inhibition enables the creation of a decoder that can respond both reliably and specifically to a stimulus, while a high spike threshold does not.

Modeling the evolving oscillatory dynamics of the rat locus coeruleus through early infancy

The mammalian locus coeruleus (LC) is a brainstem structure that displays extensive interconnections with numerous brain regions, and in particular plays a prominent role in the regulation of sleep and arousal. Postnatal LC development is known to drastically alter sleep-wake switching behavior through early infancy, and, in rats, exerts its most significant influence from about postnatal day 8 to postnatal day 21 (P8-P21). Physiologically, several dramatic changes are seen in LC functionality through this time period. Prior to P8, LC neurons are extensively coupled via electrical gap junctions and chemical synapses, and the entire LC network exhibits synchronized ~0.3 Hz subthreshold oscillations and spiking. From P8 to P21, the network oscillation frequency rises up to ~3 Hz (at P21) while the amplitude of the network oscillation decreases. Beyond P21, synchronized network oscillations vanish and gap junction coupling is sparse or nonexistent. In this work, we develop a large-scale, biophysically realistic model of the rat LC and we use this model to examine the changing physiology of the LC through the pivotal P8-P21 developmental period. We find that progressive gap junction pruning is sufficient to account for all of the physiological changes observed from P8 to P21.

Encoding whisker deflection velocity within the rodent barrel cortex using phase-delayed inhibition

The primary sensory feature represented within the rodent barrel cortex is the velocity with which a whisker has been deflected. Whisker deflection velocity is encoded within the thalamus via population synchrony (higher deflection velocities entail greater synchrony among the corresponding thalamic population). Thalamic (TC) cells project to regular spiking (RS) cells within the barrel cortex, as well as to inhibitory cortical fast-spiking (FS) neurons, which in turn project to RS cells. Thus, TC spikes result in EPSPs followed, with a small time lag, by IPSPs within an RS cell, and hence the RS cell decodes TC population synchrony by employing a phase-delayed inhibition synchrony detection scheme. As whisker deflection velocity is increased, the probability that an RS cell spikes rises, while jitter in the timing of RS cell spikes remains constant. Furthermore, repeated whisker deflections with fixed velocity lead to system adaptation – TC →RS, TC →FS, and FS →RS synapses all weaken substantially, leading to a smaller probability of spiking of the RS cell and increased jitter in the timing of RS cell spikes. Interestingly, RS cell activity is better able to distinguish among different whisker deflection velocities after adaptation. In this work, we construct a biophysical model of a basic ‘building block’ of barrel cortex – the feedforward circuit consisting of TC cells, FS cells, and a single RS cell – and we examine the ability of the purely feedforward circuit to explain the experimental data on RS cell spiking probability, jitter, adaptation, and deflection velocity discrimination. Moreover, we study the contribution of the phase-delayed inhibition network structure to the ability of an RS cell to decode whisker deflection velocity encoded via TC population synchrony.

Coding of odors by temporal binding within a model network of the locust antennal lobe

The locust olfactory system interfaces with the external world through antennal receptor neurons (ORNs), which represent odors in a distributed, combinatorial manner. ORN axons bundle together to form the antennal nerve, which relays sensory information centrally to the antennal lobe (AL). Within the AL, an odor generates a dynamically evolving ensemble of active cells, leading to a stimulus-specific temporal progression of neuronal spiking. This experimental observation has led to the hypothesis that an odor is encoded within the AL by a dynamically evolving trajectory of projection neuron (PN) activity that can be decoded piecewise to ascertain odor identity. In order to study information coding within the locust AL, we developed a scaled-down model of the locust AL using Hodgkin–Huxley-type neurons and biologically realistic connectivity parameters and current components. Using our model, we examined correlations in the precise timing of spikes across multiple neurons, and our results suggest an alternative to the dynamic trajectory hypothesis. We propose that the dynamical interplay of fast and slow inhibition within the locust AL induces temporally stable correlations in the spiking activity of an odor-dependent neural subset, giving rise to a temporal binding code that allows rapid stimulus detection by downstream elements.

Spiking and Excitatory/Inhibitory Input Dynamics of Barrel Cells in Response to Whisker Deflections of Varying Velocity and Angular Direction

The spiking of barrel regular-spiking (RS) cells is tuned for both whisker deflection direction and velocity. Velocity tuning arises due to thalamocortical (TC) synchrony (but not spike quantity) varying with deflection velocity, coupled with feedforward inhibition, while direction selectivity is not fully understood, though may be due partly to direction tuning of TC spiking. Data show that as deflection direction deviates from the preferred direction of an RS cell, excitatory input to the RS cell diminishes minimally, but temporally shifts to coincide with the time-lagged inhibitory input. This work constructs a realistic large-scale model of a barrel; model RS cells exhibit velocity and direction selectivity due to TC input dynamics, with the experimentally observed sharpening of direction tuning with decreasing velocity. The model puts forth the novel proposal that RS→RS synapses can naturally and simply account for the unexplained direction dependence of RS cell inputs - as deflection direction deviates from the preferred direction of an RS cell, and TC input declines, RS→RS synaptic transmission buffers the decline in total excitatory input and causes a shift in timing of the excitatory input peak from the peak in TC input to the delayed peak in RS input. The model also provides several experimentally testable predictions on the velocity dependence of RS cell inputs. This model is the first, to my knowledge, to study the interaction of direction and velocity and propose physiological mechanisms for the stimulus dependence in the timing and amplitude of RS cell inputs.