Neural Sequence Generation Using Spatiotemporal Patterns of Inhibition

Neuronal activation sequences in lateral prefrontal cortex encode visuospatial working memory during virtual navigation

Working memory (WM) is the ability to maintain and manipulate information 'in mind'. The neural codes underlying WM have been a matter of debate. We simultaneously recorded the activity of hundreds of neurons in the lateral prefrontal cortex of male macaque monkeys during a visuospatial WM task that required navigation in a virtual 3D environment. Here, we demonstrate distinct neuronal activation sequences (NASs) that encode remembered target locations in the virtual environment. This NAS code outperformed the persistent firing code for remembered locations during the virtual reality task, but not during a classical WM task using stationary stimuli and constraining eye movements. Finally, blocking NMDA receptors using low doses of ketamine deteriorated the NAS code and behavioral performance selectively during the WM task. These results reveal the versatility and adaptability of neural codes supporting working memory function in the primate lateral prefrontal cortex.

Thalamus drives vocal onsets in the zebra finch courtship song

While motor cortical circuits contain information related to specific movement parameters1, long-range inputs also have a critical role in action execution2,3. Thalamic projections can shape premotor activity2-6 and have been suggested7 to mediate the selection of short, stereotyped actions comprising more complex behaviours8. However, the mechanisms by which thalamus interacts with motor cortical circuits to execute such movement sequences remain unknown. Here we find that thalamic drive engages a specific subpopulation of premotor neurons within the zebra finch song nucleus HVC (proper name) and that these inputs are critical for the progression between vocal motor elements (that is, 'syllables'). In vivo two-photon imaging of thalamic axons in HVC showed robust song-related activity, and online perturbations of thalamic function caused song to be truncated at syllable boundaries. We used thalamic stimulation to identify a sparse set of thalamically driven neurons within HVC, representing ~15% of the premotor neurons within that network. Unexpectedly, this population of putative thalamorecipient neurons is robustly active immediately preceding syllable onset, leading to the possibility that thalamic input can initiate individual song components through selectively targeting these 'starter cells'. Our findings highlight the motor thalamus as a director of cortical dynamics in the context of an ethologically relevant behavioural sequence.

A feedforward inhibitory premotor circuit for auditory-vocal interactions in zebra finches

Significance During conversations, we frequently alternate between listening and speaking. This involves withholding responses while the other person is vocalizing and rapidly initiating a reply once they stop. Similar exchanges also occur in other animals, such as songbirds, yet little is known about how brain areas responsible for vocal production are influenced by areas dedicated to listening. Here, we combined neural recordings and mathematical modeling of a sensorimotor circuit to show that input-dependent inhibition can both suppress vocal responses and regulate the onset latencies of vocalizations. Our resulting model provides a simple generalizable circuit mechanism by which inhibition precisely times vocal output and integrates auditory input within a premotor nucleus.

Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation

Recurrent neural networks (RNNs) have been widely used to model sequential neural dynamics ("neural sequences") of cortical circuits in cognitive and motor tasks. Efforts to incorporate biological constraints and Dale's principle will help elucidate the neural representations and mechanisms of underlying circuits. We trained an excitatory-inhibitory RNN to learn neural sequences in a supervised manner and studied the representations and dynamic attractors of the trained network. The trained RNN was robust to trigger the sequence in response to various input signals and interpolated a time-warped input for sequence representation. Interestingly, a learned sequence can repeat periodically when the RNN evolved beyond the duration of a single sequence. The eigenspectrum of the learned recurrent connectivity matrix with growing or damping modes, together with the RNN's nonlinearity, were adequate to generate a limit cycle attractor. We further examined the stability of dynamic attractors while training the RNN to learn two sequences. Together, our results provide a general framework for understanding neural sequence representation in the excitatory-inhibitory RNN.

Local Axonal Conduction Shapes the Spatiotemporal Properties of Neural Sequences

Sequential activation of neurons has been observed during various behavioral and cognitive processes, but the underlying circuit mechanisms remain poorly understood. Here, we investigate premotor sequences in HVC (proper name) of the adult zebra finch forebrain that are central to the performance of the temporally precise courtship song. We use high-density silicon probes to measure song-related population activity, and we compare these observations with predictions from a range of network models. Our results support a circuit architecture in which heterogeneous delays between sequentially active neurons shape the spatiotemporal patterns of HVC premotor neuron activity. We gauge the impact of several delay sources, and we find the primary contributor to be slow conduction through axonal collaterals within HVC, which typically adds between 1 and 7.5 ms for each link within the sequence. Thus, local axonal "delay lines" can play an important role in determining the dynamical repertoire of neural circuits.

Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning

Sequential activity is a prominent feature of many neural systems, in multiple behavioral contexts. Here, we investigate how Hebbian rules lead to storage and recall of random sequences of inputs in both rate and spiking recurrent networks. In the case of the simplest (bilinear) rule, we characterize extensively the regions in parameter space that allow sequence retrieval and compute analytically the storage capacity of the network. We show that nonlinearities in the learning rule can lead to sparse sequences and find that sequences maintain robust decoding but display highly labile dynamics to continuous changes in the connectivity matrix, similar to recent observations in hippocampus and parietal cortex.

Sequential activity has been observed in multiple neuronal circuits across species, neural structures, and behaviors. It has been hypothesized that sequences could arise from learning processes. However, it is still unclear whether biologically plausible synaptic plasticity rules can organize neuronal activity to form sequences whose statistics match experimental observations. Here, we investigate temporally asymmetric Hebbian rules in sparsely connected recurrent rate networks and develop a theory of the transient sequential activity observed after learning. These rules transform a sequence of random input patterns into synaptic weight updates. After learning, recalled sequential activity is reflected in the transient correlation of network activity with each of the stored input patterns. Using mean-field theory, we derive a low-dimensional description of the network dynamics and compute the storage capacity of these networks. Multiple temporal characteristics of the recalled sequential activity are consistent with experimental observations. We find that the degree of sparseness of the recalled sequences can be controlled by nonlinearities in the learning rule. Furthermore, sequences maintain robust decoding, but display highly labile dynamics, when synaptic connectivity is continuously modified due to noise or storage of other patterns, similar to recent observations in hippocampus and parietal cortex. Finally, we demonstrate that our results also hold in recurrent networks of spiking neurons with separate excitatory and inhibitory populations.

Hidden neural states underlie canary song syntax

Coordinated skills such as speech or dance involve sequences of actions that follow syntactic rules in which transitions between elements depend on the identity and order of past actions. Canary songs are comprised of repeated syllables, called phrases, and the ordering of these phrases follows long-range rules¹, where the choice of what to sing depends on song structure many seconds prior. The neural substrates that support these long-range correlations are unknown. Using miniature head-mounted microscopes and cell-type-specific genetic tools, we observed neural activity in the premotor nucleus HVC^2–4 as canaries explore various phrase sequences in their repertoire. We find neurons that encode past transitions, extending over 4 phrases and spanning up to 4 seconds and 40 syllables. These neurons preferentially encode past actions rather than future actions, can reflect more than a single song history, and occur mostly during the rare phrases that involve history-dependent transitions in song. These findings demonstrate that HVC dynamics includes “hidden states” not reflected in ongoing behavior – states that carry information about prior actions. These states provide a possible substrate to control syntax transitions governed by long-range rules.

Potential factors influencing replay across CA1 during sharp-wave ripples

Sharp-wave ripples are complex neurophysiological events recorded along the trisynaptic hippocampal circuit (i.e. from CA3 to CA1 and the subiculum) during slow-wave sleep and awake states. They arise locally but scale brain-wide to the hippocampal target regions at cortical and subcortical structures. During these events, neuronal firing sequences are replayed retrospectively or prospectively and in the forward or reverse order as defined by experience. They could reflect either pre-configured firing sequences, learned sequences or an option space to inform subsequent decisions. How can different sequences arise during sharp-wave ripples? Emerging data suggest the hippocampal circuit is organized in different loops across the proximal (close to dentate gyrus) and distal (close to entorhinal cortex) axis. These data also disclose a so-far neglected laminar organization of the hippocampal output during sharp-wave events. Here, I discuss whether by incorporating cell-type-specific mechanisms converging on deep and superficial CA1 sublayers along the proximodistal axis, some novel factors influencing the organization of hippocampal sequences could be unveiled. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Manipulations of inhibition in cortical circuitry differentially affect spectral and temporal features of Bengalese finch song

The interplay between inhibition and excitation can regulate behavioral expression and control, including the expression of communicative behaviors like birdsong. Computational models postulate varying degrees to which inhibition within vocal motor circuitry influences birdsong, but few studies have tested these models by manipulating inhibition. Here we enhanced and attenuated inhibition in the cortical nucleus HVC (used as proper name) of Bengalese finches (Lonchura striata var. domestica). Enhancement of inhibition (with muscimol) in HVC dose-dependently reduced the amount of song produced. Infusions of higher concentrations of muscimol caused some birds to produce spectrally degraded songs, whereas infusions of lower doses of muscimol led to the production of relatively normal (nondegraded) songs. However, the spectral and temporal structures of these nondegraded songs were significantly different from songs produced under control conditions. In particular, muscimol infusions decreased the frequency and amplitude of syllables, increased various measures of acoustic entropy, and increased the variability of syllable structure. Muscimol also increased sequence durations and the variability of syllable timing and syllable sequencing. Attenuation of inhibition (with bicuculline) in HVC led to changes to song distinct from and often opposite to enhancing inhibition. For example, in contrast to muscimol, bicuculline infusions increased syllable amplitude, frequency, and duration and decreased the variability of acoustic features. However, like muscimol, bicuculline increased the variability of syllable sequencing. These data highlight the importance of inhibition to the production of stereotyped vocalizations and demonstrate that changes to neural dynamics within cortical circuitry can differentially affect spectral and temporal features of song.NEW & NOTEWORTHY We reveal that manipulations of inhibition in the cortical nucleus HVC affect the structure, timing, and sequencing of syllables in Bengalese finch song. Enhancing and blocking inhibition led to opposite changes to the acoustic structure and timing of vocalizations, but both caused similar changes to vocal sequencing. These data provide support for computational models of song control but also motivate refinement of existing models to account for differential effects on syllable structure, timing, and sequencing.

Unsupervised Learning of Persistent and Sequential Activity

Two strikingly distinct types of activity have been observed in various brain structures during delay periods of delayed response tasks: Persistent activity (PA), in which a sub-population of neurons maintains an elevated firing rate throughout an entire delay period; and Sequential activity (SA), in which sub-populations of neurons are activated sequentially in time. It has been hypothesized that both types of dynamics can be “learned” by the relevant networks from the statistics of their inputs, thanks to mechanisms of synaptic plasticity. However, the necessary conditions for a synaptic plasticity rule and input statistics to learn these two types of dynamics in a stable fashion are still unclear. In particular, it is unclear whether a single learning rule is able to learn both types of activity patterns, depending on the statistics of the inputs driving the network. Here, we first characterize the complete bifurcation diagram of a firing rate model of multiple excitatory populations with an inhibitory mechanism, as a function of the parameters characterizing its connectivity. We then investigate how an unsupervised temporally asymmetric Hebbian plasticity rule shapes the dynamics of the network. Consistent with previous studies, we find that for stable learning of PA and SA, an additional stabilization mechanism is necessary. We show that a generalized version of the standard multiplicative homeostatic plasticity (Renart et al., 2003; Toyoizumi et al., 2014) stabilizes learning by effectively masking excitatory connections during stimulation and unmasking those connections during retrieval. Using the bifurcation diagram derived for fixed connectivity, we study analytically the temporal evolution and the steady state of the learned recurrent architecture as a function of parameters characterizing the external inputs. Slow changing stimuli lead to PA, while fast changing stimuli lead to SA. Our network model shows how a network with plastic synapses can stably and flexibly learn PA and SA in an unsupervised manner.

Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters

Studies of Boolean recurrent neural networks are briefly introduced with an emphasis on the attractor dynamics determined by the sequence of distinct attractors observed in the limit cycles. We apply this framework to a simplified model of the basal ganglia-thalamocortical circuit where each brain area is represented by a "neuronal" node in a directed graph. Control parameters ranging from neuronal excitability that affects all cells to targeted local connections modified by a new adaptive plasticity rule, and the regulation of the interactive feedback affecting the external input stream of information, allow the network dynamics to switch between stable domains delimited by highly discontinuous boundaries and reach very high levels of complexity with specific configurations. The significance of this approach with regard to brain circuit studies is briefly discussed.

EM connectomics reveals axonal target variation in a sequence-generating network

The sequential activation of neurons has been observed in various areas of the brain, but in no case is the underlying network structure well understood. Here we examined the circuit anatomy of zebra finch HVC, a cortical region that generates sequences underlying the temporal progression of the song. We combined serial block-face electron microscopy with light microscopy to determine the cell types targeted by HVC(RA) neurons, which control song timing. Close to their soma, axons almost exclusively targeted inhibitory interneurons, consistent with what had been found with electrical recordings from pairs of cells. Conversely, far from the soma the targets were mostly other excitatory neurons, about half of these being other HVC(RA) cells. Both observations are consistent with the notion that the neural sequences that pace the song are generated by global synaptic chains in HVC embedded within local inhibitory networks.

Unstable neurons underlie a stable learned behavior

Motor skills can be maintained for decades, but the biological basis of this memory persistence remains largely unknown. The zebra finch, for example, sings a highly stereotyped song that is stable for years, but it is not known whether the precise neural patterns underlying song are stable or shift from day to day. Here we demonstrate that the population of projection neurons coding for song in the premotor nucleus, HVC, change from day to day. The most dramatic shifts occur over intervals of sleep. In contrast to the transient participation of excitatory neurons, ensemble measurements dominated by inhibition persist unchanged even after damage to downstream motor nerves. These observations offer a principle of motor stability: spatiotemporal patterns of inhibition can maintain a stable scaffold for motor dynamics while the population of principal neurons that directly drive behavior shift from one day to the next.

Model of the songbird nucleus HVC as a network of central pattern generators

We propose a functional architecture of the adult songbird nucleus HVC in which the core element is a "functional syllable unit" (FSU). In this model, HVC is organized into FSUs, each of which provides the basis for the production of one syllable in vocalization. Within each FSU, the inhibitory neuron population takes one of two operational states: 1) simultaneous firing wherein all inhibitory neurons fire simultaneously, and 2) competitive firing of the inhibitory neurons. Switching between these basic modes of activity is accomplished via changes in the synaptic strengths among the inhibitory neurons. The inhibitory neurons connect to excitatory projection neurons such that during state 1 the activity of projection neurons is suppressed, while during state 2 patterns of sequential firing of projection neurons can occur. The latter state is stabilized by feedback from the projection to the inhibitory neurons. Song composition for specific species is distinguished by the manner in which different FSUs are functionally connected to each other. Ours is a computational model built with biophysically based neurons. We illustrate that many observations of HVC activity are explained by the dynamics of the proposed population of FSUs, and we identify aspects of the model that are currently testable experimentally. In addition, and standing apart from the core features of an FSU, we propose that the transition between modes may be governed by the biophysical mechanism of neuromodulation.