Neural syntax: cell assemblies, synapsembles, and readers

Abstract

A widely discussed hypothesis in neuroscience is that transiently active ensembles of neurons, known as "cell assemblies," underlie numerous operations of the brain, from encoding memories to reasoning. However, the mechanisms responsible for the formation and disbanding of cell assemblies and temporal evolution of cell assembly sequences are not well understood. I introduce and review three interconnected topics, which could facilitate progress in defining cell assemblies, identifying their neuronal organization, and revealing causal relationships between assembly organization and behavior. First, I hypothesize that cell assemblies are best understood in light of their output product, as detected by "reader-actuator" mechanisms. Second, I suggest that the hierarchical organization of cell assemblies may be regarded as a neural syntax. Third, constituents of the neural syntax are linked together by dynamically changing constellations of synaptic weights ("synapsembles"). The existing support for this tripartite framework is reviewed and strategies for experimental testing of its predictions are discussed.

Figure 1

Cell assembly and assembly sequences. A. Hebb’s reverberating cell assembly sequences (‘assembly phases’; after Fig.10 of Hebb, 1949). Arrows represent transitions between individual assemblies. The direction of activity flow across assemblies (edges) is determined by the stronger synaptic strengths among assembly members relative to other connections (not shown). The same assembly can participate in a sequence more than once (e.g. pathway 1, 4 indicates recurring transitions). No mechanism is postulated to explain why activity does not spread to all parts of the network and reverberate forever. B. Top, long sequence of two characters (e.g., dot and dash). Its embedded information is virtually impossible to recover. Bottom, same exact sequence as above after adding syntactic segmentation (space= stop-start punctuation) between the short strings of characters. The Morse code message reads: ‘segmentation of information is essence of coding’. By analogy, segmentation or ‘chunking’ of neuronal assemblies can be brought about by salient external stimulus sequences, brain-initiated, modality-specific synchronizing-blanking mechanisms (such as saccadic eye movement, sniffing, whisking, active touch, licking, contraction of middle ear muscles, etc.), internally generated oscillations or other syntactical mechanisms. C. Reader-defined cell assemblies. Neurons that fire within the time integrating window of a reader mechanism (e.g., the ability of an reader neuron to integrate its inputs within the time frame of its membrane time constant) define an assembly (irrespective whether assembly members are connected synaptically or not). Readers a, b, c and w may receive inputs from many neurons (1 to n) by way of synapses differing in strength but respond only to a combination of spiking neurons to which they are most strongly connected (e.g., reader a responds preferentially to co-firing of neurons 1, 5 and 9 at t₁, even though it may be synaptically innervated by neurons 2, 6 and 10 as well; at t₂, neuron b fires in response to the discharge of neurons 2, 6 and 10). Synaptic strengths between neurons vary as a function of the spiking history of both postsynaptic and presynaptic neuron (short-term plasticity). The response of the reader neuron, therefore, depends on both the identity of the spiking upstream neurons and the constellation of current synaptic weights (‘synapsembles’). Reader mechanism q has a longer time integrator and, therefore, can link together assemblies to neural ‘words’, reading out a new quality not present in the individual representations of a, b and c.

Figure 2

Cell assembly: the fundamental unit of neural syntax. (A) Raster plot of a subset of hippocampal pyramidal cells that were active during a 1-s period of spatial exploration on an open field out of a larger set of simultaneously recorded neurons, ordered by stochastic search over all possible orderings to highlight the temporal relationship between anatomically distributed neurons. Color-coded ticks (spikes) refer to recording locations shown in (B). Vertical lines indicate troughs of theta waves (bottom trace). ‘Cell assembly’ organization is visible, with repeatedly synchronous firing of some subpopulations (circled). Note that assemblies can alternate (top and bottom sets) rapidly across theta cycles. (C) Spike timing is predictable from peer activity. Distribution of time scales at which peer activity optimally improved spike time prediction of a given cell, shown for all cells. The median optimal timescale is 23 ms (red line). Modified after Harris et al. (2003).

Figure 3

Externally-triggered and internally-generated assembly sequences. A. Wiring diagram of the early olfactory system of the locust. An odorant evokes an odor-specific temporal pattern in several of recurrently connected antennal lobe (AL) neurons, coordinated by a 20–30 Hz (gamma) oscillation. Kenyon cells (KC) of the mushroom body (MB) are the readers of the activity of AL projection neurons (PNs) and integrate their spikes. B. Firing patterns of 3 AL neurons (PN1–3) in response to 16 different odors. In each segment of time (e.g., a gamma oscillation cycle), a different constellation of AL neurons fires. This constellation is referred to as the ‘population vector’ or ‘state’ of the network, and the time-varying population vector (i.e., the shifting states) is described as a trajectory. Although each state of the trajectory codes for the same odor, the state evolves over a few hundred milliseconds before relaxing back to baseline activity (illustrated by the curve in the inset in A). C. Activity of 3 KCs. Each KC carries out a pattern matching operation between its connection vector and the PN population activity vector. The AL output synchrony is strongest at its early phase of evolution and evokes a single burst in the reader KC (‘sparse coding’). D. Time-frequency spectrum of a zebra finch song and its amplitude envelope. E. Spike raster plot of eight projection neurons in the high vocal center (HVC). There is not a one-to-one correspondence between the song syntax and cell assemblies (neural ‘letters’ in HVC). Rather, neurons in HVC generate a temporal ‘state’ sequence (i.e., a trajectory), allowing the target neuronal activity and consequent motor actions unfold. F. Interneuronal activity is also temporally organized and relates to the syntactic structure of the song (F). A to C, modified after Laurent (2002). D to F, modified after Hahnloser et al. (2002).

Figure 4

Internally generated assembly sequences during cognitive activity. A. Sequential firing patterns of hippocampal neurons in a memory task. Center, color-coded spikes (dots) of simultaneously recorded hippocampal CA1 pyramidal neurons. The rat was required to run in the wheel facing to the left during the delay between the runs in the maze. Left, normalized firing rate profiles of neurons during wheel running, ordered by the latency of their peak firing rates during left trials (each line is a single cell). Right, normalized firing rates of the same neurons during right trials. B. Sequential firing patterns of prefrontal pyramidal cells in a working memory task. Middle, cheese odor or chocolate odor in the start area signals the availability of cheese or chocolate reward in the left or right goal area (position 1), respectively. Travel trajectories were linearized (0 to 1). Left, neurons were ordered by the location of their peak firing rates relative to the rat’s position in the maze during left trials. Each row represents the position-dependent normalized firing rate of a single neuron. Right, normalized firing rates of the same neurons during right trials. A, reprinted from Pastalkova et al. (2008). B, reprinted from Fujisawa et al. (2008).

Figure 5

A. Schematic of a brain-machine interface (BMI). Activity from multiple ensembles of neurons in several brain areas is recorded and their movement-related information is extracted (‘signal processing’). In the robot-control phase the derived algorithm is used to convert ongoing neuronal activity to generate the desired movements of the robot (reader-actuator). B. Relationship between the numbers of neurons used to predict arm movement position in a monkey and accuracy of the algorithm’s prediction. Note that relatively few neurons are needed to achieve ‘good enough’ performance, whereas very large numbers of neurons may be needed to achieve 100% accuracy. PMd, dorsal premotor cortex, M1, primary motor cortex, S1, primary somatosensory cortex, SMA, supplementary motor area and PP, parietal cortex. C. Differential control of neighboring neurons in the motor cortex by visual feedback in monkey. The firing rate of the one of the neurons (S, blue or L, red) controlled the displacement of a meter arm (Operant Level, upper row). Responses during epochs indicated by the red and blue arrows (upper row) were rewarded on the basis of an arbitrary increase or decrease of firing rate of the chosen reader neuron. The ‘reader’ neurons (S or L) learned to respond differentially to the activity of (hidden) upstream assemblies within minutes. A and B, reprinted from Nicolelis and Lebedev (2010). C, reprinted from Fetz (2007).

Figure 6

Interleaved cell assemblies. A. Spiking activity of two hippocampal neurons (blue and green ticks) and LFP theta in a single run (1 second is shown). Temporal distance T is the time needed for the rat to run the distance between the peaks of the two place fields (‘real time’). Tau, time offset between the two neurons within the theta cycle (‘theta time’). B. Distribution of oscillation frequencies of CA1 pyramidal cells (n>1000) during running on the maze relative to the reference LFP theta (8.09 Hz=0 Hz). Gray dashed line: mean oscillation frequency of pyramidal cells (8.61 Hz=0.52 Hz). Note that nearly all place cells oscillate faster than the frequency of the concurrent LFP theta. C. Three example model neurons (color-coded) with identical oscillation frequency but different phase onset, according to their maximal discharge location. Bottom, the summed activity of the entire population of model neurons (black dashed line) oscillates slower than each transiently active individual neuron (color-coded). D. The phase of the three example neurons with respect to the oscillation of the population is plotted against time. Note that the neuronal spikes phase-precess approximately 360° (O’Keefe and Recce, 1993). Right: spike density for the example neurons. E. Correlation between the distances of place fields peaks and theta-scale time lag for >3000 pairs of neurons (as in A). Above and right: histograms of distance and time lag, respectively. F. Interleaved neuron sequences represent position and distance relationships. The width of the bars indicates firing intensity of the hypothesized assemblies while the theta-time scale temporal differences between assemblies reflect their respective distance representations. In successive theta cycles, assemblies representing overlapping place fields (P1 to P8) shift together in time and sustain a temporal order relationship with each other so that the assembly that fires on the earliest phase represents a place field whose center the animal traverses first. The temporal compression mechanism (Skaggs et al., 1996) allows distances to be translated into time. Approximately, 7±2 assemblies/gamma cycles, are present in a given theta period (Bragin et al., 1995; Lisman and Idiart, 1995). The assembly sequences within theta cycles could be conceived as a neural word. Note that neighboring overlapping words differ only by one assembly. The rat has to travel 7±2 theta cycles until a word with entirely different assemblies appear. A to E, modified after Geisler et al. (2010). F, modified after Dragoi and Buzsáki (2006).

Figure 7

Time-compressed ‘off-line’ replay of learned neural patterns. (A) Forward and reverse pre- and replay of place-cell sequences. (a) Spike trains of 13 neurons during a single lap (CA1 local field potential shown on top). Bottom panels magnify 250-ms sections of the spike train, depicting forward preplay and reverse replay, respectively. Each place cell is assumed to be a member of an assembly of distributed hippocampal neurons defining a particular position, and the ensemble sequence constitutes a neural word. (B) Replay of waking neural words during sleep in hippocampus. Smoothed place fields (colored lines) of 8 place cells during runs from left to right on a track (average of 30 trials). Vertical bars mark the positions of the normalized peaks of the smoothed fields. Non-uniform time axis below shows time within an average lap when above positions were passed. Bottom panels, three SPW-R-related sequences from slow wave sleep after the waking session. Note similar sequences during SPW-Rs and run. Note also difference in timescale. Bar = 50 ms. (C) Time-compressed replay of waking assembly sequences during sleep in mPFC. Sorted cross-correlations from simultaneously recorded cell pairs. Each row in each subpanel shows the cross-correlation between a single pair of cells, sorted according to the temporal offset of the maximum peak during the task. Red indicates the highest coincidence rate and blue, the lowest. The time axis during sleep epochs is magnified. Note similar sequences during the task and post-task sleep. Part A is modified after Diba and Buzsáki (2007) with permission; (B) modified after Lee and Wilson (2002); (C) modified after Euston et al., (2007).

Figure 8

Short-term synaptic plasticity in a working memory task. A. Top: Illustration of facilitating and depressing synaptic connections between pyramidal cell and interneuron, as reflected by the changes of EPSPs in the postsynaptic neuron. Middle: superimposed traces (10 msec) of intracellular recording from a hippocampal CA1 pyramidal cell (pyr) and extracellular recording from a putative interneuron (int), aligned by the intracellular action potential. Note short-latency (<2 msec) discharge of the interneuron after the spike of the presynaptic pyramidal cell (visible also as an artifact on the extracellular trace). Bottom, dependence of spike transmission probability on the frequency of the presynaptic pyramidal cell spikes. Note that the highest spike transmission probability occurs at approximately 10 Hz. B. Short-term cross-correlograms between a putative pyramidal cell-interneuron pair in the prefrontal cortex as a function of the rat’s position during left-turn trajectories. The most effective transmission occurred near the choice point (positions 0.3–0.5). Top right, session mean. C. Task-dependent changes of synaptic strengths (red arrows) between putative pyramidal cells (triangles) and interneurons (circles) in a small prefrontal network. A, reprinted from Marshall et al. (2002). B and C, reprinted from Fujisawa et al. (2008).

Figure 9

Segregation of cell assemblies by inhibition. A. A ring of pyramidal neurons (1–6), mutually innervating an interneuron (i). The synaptic strength between the interneuron and pyramidal cell 4 is stronger than between other pairs. When pyramidal cell one receives an input (arrow), cells 1 to 3 are activated while 4 to 6 remain silent (segregated). B. CA1 hippocampal interneuron carrying spatial information. Firing rate map of the neuron in a figure 8-shape maze. Red, 56 Hz, dark blue ~ 0 Hz. Note silent ‘place field’ (arrow) in the lower arm. C. An interneuron can belong to two assemblies within the same theta cycle. Firing rate, spikes in each lap and theta phase of spikes of the interneuron as a function of position. Phase distributions are shown twice for better visibility. Place field boundaries were assigned according to the phase precession of the interneuron (black ellipsoids). D. Event-triggered average of spike times (top), aligned on the time of the theta peak that occurred nearest (in time) to the point of maximum spatial overlap of the two fields (blue, first field encountered; red, second field encountered). Time is in units of phase relative to the trigger point in the middle of the field. Bottom, spikes of the interneurons in successive laps. Note that the systematic phase precession of spikes uncovers two independent but overlapping fields (blue and red spikes), an indication that the interneuron can switch assembly partnerships even within the same theta cycle. E. Firing rates of two pyramidal cells (red, P1; magenta, P2) and a putative basket cell (black, IN) as a function of position (top). Bottom, mean phase precession of P1 (stars) and P2 (dots), superimposed interneuron’s color-coded smoothed density of firing. Note the similar phase slope of P1 and the interneuron. F, Temporal cross-correlation between P1 and interneuron IN and P2 and the interneuron shown in E. Note positive temporal correlation between P1 and IN and negative correlation between P2 and IN. The dashed lines indicate the 95% confidence interval for shuffled spike trains. B, courtesy of K. Mizuseki. C and D are reprinted from Maurer et al., 2006b; E and F are reprinted from Geisler et al. (2007).

Figure 10

Reader-initiated transfer of information. (a) The reader sends an output command to optimize the sensor. Brain-initiated synchronizing-blanking mechanisms are used in all modalities (such as eye movement, sniffing, whisking, active touch, licking, contraction of middle ear muscles, etc.), which generate transient ‘gains’. (b) Reader-initiated transfer is used at all levels of the brain. In this example, the hippocampus (reader)-generated theta oscillation synchronizes computations in widespread neocortical areas (reflected by transient gamma oscillations). (c) The duty phase of hippocampal theta (white arrow) biases the timing of neocortical circuits so that the results of the local computations are presented to the reader during the accrual (‘readiness’) phase of the oscillation.