Neural Algorithms and Circuits for Motor Planning.

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.

Discrete attractor dynamics underlies persistent activity in the frontal cortex.

Short-term memories link events separated in time, such as past sensation and future actions. Short-term memories are correlated with slow neural dynamics, including selective persistent activity, which can be maintained over seconds. In a delayed response task that requires short-term memory, neurons in the mouse anterior lateral motor cortex (ALM) show persistent activity that instructs future actions. To determine the principles that underlie this persistent activity, here we combined intracellular and extracellular electrophysiology with optogenetic perturbations and network modelling. We show that during the delay epoch, the activity of ALM neurons moved towards discrete end points that correspond to specific movement directions. These end points were robust to transient shifts in ALM activity caused by optogenetic perturbations. Perturbations occasionally switched the population dynamics to the other end point, followed by incorrect actions. Our results show that discrete attractor dynamics underlie short-term memory related to motor planning.

θ-Band and ß-Band Neural Activity Reflects Independent Syllable Tracking and Comprehension of Time-Compressed Speech.

Recent psychophysics data suggest that speech perception is not limited by the capacity of the auditory system to encode fast acoustic variations through neural γ activity, but rather by the time given to the brain to decode them. Whether the decoding process is bounded by the capacity of θ rhythm to follow syllabic rhythms in speech, or constrained by a more endogenous top-down mechanism, e.g., involving ß activity, is unknown. We addressed the dynamics of auditory decoding in speech comprehension by challenging syllable tracking and speech decoding using comprehensible and incomprehensible time-compressed auditory sentences. We recorded EEGs in human participants and found that neural activity in both θ and γ ranges was sensitive to syllabic rate. Phase patterns of slow neural activity consistently followed the syllabic rate (4-14 Hz), even when this rate went beyond the classical θ range (4-8 Hz). The power of θ activity increased linearly with syllabic rate but showed no sensitivity to comprehension. Conversely, the power of ß (14-21 Hz) activity was insensitive to the syllabic rate, yet reflected comprehension on a single-trial basis. We found different long-range dynamics for θ and ß activity, with ß activity building up in time while more contextual information becomes available. This is consistent with the roles of θ and ß activity in stimulus-driven versus endogenous mechanisms. These data show that speech comprehension is constrained by concurrent stimulus-driven θ and low-γ activity, and by endogenous ß activity, but not primarily by the capacity of θ activity to track the syllabic rhythm.SIGNIFICANCE STATEMENT Speech comprehension partly depends on the ability of the auditory cortex to track syllable boundaries with θ-range neural oscillations. The reason comprehension drops when speech is accelerated could hence be because θ oscillations can no longer follow the syllabic rate. Here, we presented subjects with comprehensible and incomprehensible accelerated speech, and show that neural phase patterns in the θ band consistently reflect the syllabic rate, even when speech becomes too fast to be intelligible. The drop in comprehension, however, is signaled by a significant decrease in the power of low-ß oscillations (14-21 Hz). These data suggest that speech comprehension is not limited by the capacity of θ oscillations to adapt to syllabic rate, but by an endogenous decoding process.

The frontal cortex adjusts striatal integrator dynamics for flexible motor timing.

Flexible control of motor timing is crucial for behavior. Before movement begins, the frontal cortex and striatum exhibit ramping spiking activity, with variable ramp slopes anticipating movement onsets. This activity may function as an adjustable 'timer,' triggering actions at the desired timing. However, because the frontal cortex and striatum share similar ramping dynamics and are both necessary for timing behaviors, distinguishing their individual roles in this timer function remains challenging. To address this, we conducted perturbation experiments combined with multi-regional electrophysiology in mice performing a lick-timing task. Following transient silencing of the frontal cortex, cortical and striatal activity swiftly returned to pre-silencing levels and resumed ramping, leading to a shift in lick timing close to the silencing duration. Conversely, briefly inhibiting the striatum caused a gradual decrease in ramping activity in both regions, with ramping resuming from post-inhibition levels, shifting lick timing beyond the inhibition duration. Thus, inhibiting the frontal cortex and striatum effectively paused and rewound the timer, respectively. Additionally, the frontal cortex, but not the striatum, encodes trial-history information guiding lick timing. These findings suggest specialized functional allocations within the forebrain: the striatum temporally integrates input from the frontal cortex to generate ramping activity that regulates motor timing.

Feedforward amplification in recurrent networks underlies paradoxical neural coding.

The activity of single neurons encodes behavioral variables, such as sensory stimuli (Hubel & Wiesel 1959) and behavioral choice (Britten et al. 1992; Guo et al. 2014), but their influence on behavior is often mysterious. We estimated the influence of a unit of neural activity on behavioral choice from recordings in anterior lateral motor cortex (ALM) in mice performing a memory-guided movement task (H. K. Inagaki et al. 2018). Choice selectivity grew as it flowed through a sequence of directions in activity space. Early directions carried little selectivity but were predicted to have a large behavioral influence, while late directions carried large selectivity and little behavioral influence. Consequently, estimated behavioral influence was only weakly correlated with choice selectivity; a large proportion of neurons selective for one choice were predicted to influence choice in the opposite direction. These results were consistent with models in which recurrent circuits produce feedforward amplification (Goldman 2009; Ganguli et al. 2008; Murphy & Miller 2009) so that small amplitude signals along early directions are amplified to produce low-dimensional choice selectivity along the late directions, and behavior. Targeted photostimulation experiments (Daie et al. 2021b) revealed that activity along the early directions triggered sequential activity along the later directions and caused predictable behavioral biases. These results demonstrate the existence of an amplifying feedforward dynamical motif in the motor cortex, explain paradoxical responses to perturbation experiments (Chettih & Harvey 2019; Daie et al. 2021b; Russell et al. 2019), and reveal behavioral relevance of small amplitude neural dynamics.

Cell-type-specific plasticity shapes neocortical dynamics for motor learning.

Neocortical spiking dynamics control aspects of behavior, yet how these dynamics emerge during motor learning remains elusive. Activity-dependent synaptic plasticity is likely a key mechanism, as it reconfigures network architectures that govern neural dynamics. Here, we examined how the mouse premotor cortex acquires its well-characterized neural dynamics that control movement timing, specifically lick timing. To probe the role of synaptic plasticity, we have genetically manipulated proteins essential for major forms of synaptic plasticity, Ca2+/calmodulin-dependent protein kinase II (CaMKII) and Cofilin, in a region and cell-type-specific manner. Transient inactivation of CaMKII in the premotor cortex blocked learning of new lick timing without affecting the execution of learned action or ongoing spiking activity. Furthermore, among the major glutamatergic neurons in the premotor cortex, CaMKII and Cofilin activity in pyramidal tract (PT) neurons, but not intratelencephalic (IT) neurons, is necessary for learning. High-density electrophysiology in the premotor cortex uncovered that neural dynamics anticipating licks are progressively shaped during learning, which explains the change in lick timing. Such reconfiguration in behaviorally relevant dynamics is impeded by CaMKII manipulation in PT neurons. Altogether, the activity of plasticity-related proteins in PT neurons plays a central role in sculpting neocortical dynamics to learn new behavior.

Attractor dynamics gate cortical information flow during decision-making.

Decisions are held in memory until enacted, which makes them potentially vulnerable to distracting sensory input. Gating of information flow from sensory to motor areas could protect memory from interference during decision-making, but the underlying network mechanisms are not understood. Here, we trained mice to detect optogenetic stimulation of the somatosensory cortex, with a delay separating sensation and action. During the delay, distracting stimuli lost influence on behavior over time, even though distractor-evoked neural activity percolated through the cortex without attenuation. Instead, choice-encoding activity in the motor cortex became progressively less sensitive to the impact of distractors. Reverse engineering of neural networks trained to reproduce motor cortex activity revealed that the reduction in sensitivity to distractors was caused by a growing separation in the neural activity space between attractors that encode alternative decisions. Our results show that communication between brain regions can be gated via attractor dynamics, which control the degree of commitment to an action.

Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions.

Neural oscillations are ubiquitously observed in the mammalian brain, but it has proven difficult to tie oscillatory patterns to specific cognitive operations. Notably, the coupling between neural oscillations at different timescales has recently received much attention, both from experimentalists and theoreticians. We review the mechanisms underlying various forms of this cross-frequency coupling. We show that different types of neural oscillators and cross-frequency interactions yield distinct signatures in neural dynamics. Finally, we associate these mechanisms with several putative functions of cross-frequency coupling, including neural representations of multiple environmental items, communication over distant areas, internal clocking of neural processes, and modulation of neural processing based on temporal predictions.

Speech encoding by coupled cortical theta and gamma oscillations.

Many environmental stimuli present a quasi-rhythmic structure at different timescales that the brain needs to decompose and integrate. Cortical oscillations have been proposed as instruments of sensory de-multiplexing, i.e., the parallel processing of different frequency streams in sensory signals. Yet their causal role in such a process has never been demonstrated. Here, we used a neural microcircuit model to address whether coupled theta-gamma oscillations, as observed in human auditory cortex, could underpin the multiscale sensory analysis of speech. We show that, in continuous speech, theta oscillations can flexibly track the syllabic rhythm and temporally organize the phoneme-level response of gamma neurons into a code that enables syllable identification. The tracking of slow speech fluctuations by theta oscillations, and its coupling to gamma-spiking activity both appeared as critical features for accurate speech encoding. These results demonstrate that cortical oscillations can be a key instrument of speech de-multiplexing, parsing, and encoding.

Analytical insights on theta-gamma coupled neural oscillators.

In this paper, we study the dynamics of a quadratic integrate-and-fire neuron, spiking in the gamma (30-100 Hz) range, coupled to a delta/theta frequency (1-8 Hz) neural oscillator. Using analytical and semianalytical methods, we were able to derive characteristic spiking times for the system in two distinct regimes (depending on parameter values): one regime where the gamma neuron is intrinsically oscillating in the absence of theta input, and a second one in which gamma spiking is directly gated by theta input, i.e., windows of gamma activity alternate with silence periods depending on the underlying theta phase. In the former case, we transform the equations such that the system becomes analogous to the Mathieu differential equation. By solving this equation, we can compute numerically the time to the first gamma spike, and then use singular perturbation theory to find successive spike times. On the other hand, in the excitable condition, we make direct use of singular perturbation theory to obtain an approximation of the time to first gamma spike, and then extend the result to calculate ensuing gamma spikes in a recursive fashion. We thereby give explicit formulas for the onset and offset of gamma spike burst during a theta cycle, and provide an estimation of the total number of spikes per theta cycle both for excitable and oscillator regimes.