Emergence of cognitive priming and structure building from the hierarchical interaction of canonical microcircuit models

Abstract

The concept of connectionism states that higher cognitive functions emerge from the interaction of many simple elements. Accordingly, research on canonical microcircuits conceptualizes findings on fundamental neuroanatomical circuits as well as recurrent organizational principles of the cerebral cortex and examines the link between architectures and their associated functionality. In this study, we establish minimal canonical microcircuit models as elements of hierarchical processing networks. Based on a combination of descriptive time simulations and explanatory state-space mappings, we show that minimal canonical microcircuits effectively segregate feedforward and feedback information flows and that feedback information conditions basic processing operations in minimal canonical microcircuits. Further, we derive and examine two prototypical meta-circuits of cooperating minimal canonical microcircuits for the neurocognitive problems of priming and structure building. Through the application of these findings to a language network of syntax parsing, this study embodies neurocognitive research on hierarchical communication in light of canonical microcircuits, cell assembly theory, and predictive coding.

Keywords: Adaptive mechanisms; Canonical microcircuit; Hierarchical model; Neural computations; State-dependent operation; Syntax parsing.

Fig. 1

Model architecture and principle analysis approach. a The investigated canonical microcircuit model considers three neural masses—pyramidal cells (Py), excitatory interneurons (EIN), and inhibitory interneurons (IIN)—that interact through mean firing rates φ(t) scaled by connectivity gains N_ab. The mean membrane potential V_Py, integrating both positive and negative local feedback from the interneuron populations (V_Py = V₂ − V₃), serves as output signal. b The efferent signal V_Py (blue line) is compared to a firing threshold u_th (red line) in three consecutive analysis windows (gray planes) to characterize the response behavior following a transient stimulation (green line). c This response behavior was classified into three types: nonresponsive, transfer, and memory behavior (color figure online)

Fig. 2

Concomitant stimulation of the mCMC with feedforward and feedback information. a The mCMC model simultaneously received transient forward (p_EIN) and constant feedback (p_Py) stimulations. b–f For increasing levels of constant feedback input (p_Py) the left-hand bifurcation diagrams exhibit stable (solid lines) and unstable (dashed lines) states of the mean membrane potential V_Py for a range of feedforward input values (p_EIN). These bifurcation plots explain the right-hand characteristic fingerprints that illustrate the consequent response behaviors for transient p_EIN stimulation. The characteristic fingerprints color-coded the stimulation-induced response behaviors: nonresponsive (green), transfer (gray), and memory behavior (orange). The markers (cross, triangle, and circle) denoted stimuli whose specific response behavior changed according to the level of concomitant feedback input (p_Py) (color figure online)

Fig. 3

Modulating influence of simultaneous stimulation of EIN and Py. a For constant levels of p_Py and p_EIN input, the two parameter bifurcation diagram mapped the course of fold and Hopf bifurcations (colored lines). Colored surfaces marked input configurations for which monostable (gray and yellow shadings) or bistable states (green shadings) were present. b–e Contrary to Fig. 2, we applied increasing levels of constant input to EIN concomitant to a transient Py stimulation. b Without modulating input, the characteristic fingerprint (right) shows only sparse traces of memory behavior (orange). c–e For higher levels of modulating p_EIN input, characteristic fingerprints (right) show increased stimulation ranges that induced memory behavior. This is due to the extinction of the separatrix that delimits the memory behavior (left-hand-side state-space projections). In the fingerprints the intensity threshold that separated nonresponsive (green) and transfer (gray) behavior declined, because distance between working point (p_Py = 0) and the lower fold bifurcation decreased (color figure online)

Fig. 4

Prototypical meta-circuits of cooperating mCMCs. a In the initial meta-circuit a lower mCMC A₂ receives a facilitative feedback signal (dashed line) from a higher mCMC A₁ that modulates A₂’s response to a feedforward stimulation p_ff-Stim. Two computationally relevant derivations are investigated: b Facilitation modifies perception (priming), where a recurrent feedforward coupling from $A_2^{\ast }$ to $A_1^{\ast }$ (solid line) allows $A_2^{\ast }$ to activate $A_1^{\ast }$. The consequent facilitative feedback signal lowers the perceptual threshold in $A_2^{\ast }$. Note that the facilitative feedback signal, conveyed by $A_1^{\ast },$ may also modify the perceptual threshold of yet another circuit. c Facilitation modifies memorization (structure building), where ${\tilde{A}}_2$ is considered a higher microcircuit, but still receives a facilitative feedback signal, p_fac,in, from ${\tilde{A}}_1$. The feedback signal conditions the memorization of a feedforward stimulation p_ff-Stim arriving from a lower mCMC ${\tilde{A}}_3$. In case of such an activation of ${\tilde{A}}_2$, a facilitative feedback signal p_fac,out is conveyed to connected circuits, effectively cascading this local operation and supporting the incremental build-up of sustained activity patterns

Fig. 5

Principle mechanism for the dynamic shift of a perceptual threshold. a A target stimulus, applied to a mCMC $A_2^{\ast }$, is not able to cause a considerable output in $A_2^{\ast }$. However, as soon as a priming stimulus activates the higher mCMC $A_1^{\ast }$ (light blue line in b), $A_1^{\ast }$ emits a facilitative feedback signal (green dashed line in c) that allows $A_2^{\ast }$ to perceive the target stimulus. d Time course of afferent feedforward stimulations of the mCMC $A_2^{\ast }$. e Efferent signals of the mCMCs (color figure online)

Fig. 6

Analysis of the priming mechanism. a For the characterization of the priming mechanism, we applied stimulation streams that comprised two target stimuli separated by a priming stimulus. b We evaluated the response behaviors of $A_1^{\ast }$ and $A_2^{\ast }$ by comparing V_Py to a firing threshold of 4 mV in seven analysis windows: before, after (i.e., resting level, light gray sections), and during a stimulation (dark gray sections). As indicated by the arrows, a stimulation stream was said to be effectual, i.e., evoked a perceptual threshold shift, if (a) the priming evoked a sustained high activation in the higher mCMC $A_1^{\ast }$, but not $A_2^{\ast }$, and (b) the target stimulus evoked a transient high activation only after priming. c Target stimuli of different duration (t_dur) and intensity were applied. The sum of all effectual stimulation streams was scaled by the total number of considered stimulation streams. d This color-coded percentage was mapped to a range of connectivity gains c_f and c_b in order to characterize their constraining influence on the priming mechanism

Fig. 7

Impact of connectivity gains, priming intensity and network balance to the priming mechanism. Connectivity gains constrain effectual stimulation streams. Each plot varies over the connectivity gains c_f and c_b and sums over stimulations of different length and intensity (see Fig. 6). Colors code the percentage of effectual stimulation streams that evoked priming. Priming and target stimulus were equally long, but the primer’s intensity was 20% (left column) or 80% (right column) larger. Larger primers lead to more effectual stimulations streams both in terms of the maximum rate and the range of suited connectivity gains. Compared to the default configuration of inhibitory synaptic gains (a), a slight decrease of inhibition in $A_1^{\ast }$ combined with an increase of inhibition in $A_2^{\ast }$ (b) enhances the maximum rate of effectual stimulation streams and the range of suited connectivity gains. Note the different color scaling in the single subplots and that the percentages are specific for the chosen stimulation parameter ranges (see Table 2)

Fig. 8

Characterization of stimuli that evoked a perceptual threshold shift. Each subplot varies over stimulus duration and intensity and shows the number of effectual stimulations (summed above all c_f–c_b combinations) in grayscales. Supraliminal target stimuli, i.e., very strong or long stimuli, were already recognized, making priming superfluous (sandy coloring). Subliminal target stimuli, i.e., weak and brief target stimuli, were not suited to initiate the priming, because they failed to evoke a response memory behavior in the higher circuit $A_1^{\ast }$ (light blue coloring). Columns relate to the priming intensity, where the priming stimulus is 20% (80%) larger than the target stimulus. A stronger priming intensity increased the range of perceived target stimuli. Compared to the default configuration of the inhibitory synaptic gains (a), a decreased inhibitory synaptic gain in the higher circuit $A_1^{\ast }$ combined with an increased inhibitory synaptic gain in the lower circuit $A_2^{\ast }$ did not only promote the number of perceived target stimuli, but also their invariance to variations in intensity or duration, marked by the larger range of effectual stimulations. Note the different grayscales for each subplot

Fig. 9

Importance of the prototypical meta-circuits’ topology. a For the priming mechanism, we considered a specific topology within the prototypical meta-circuit, where the higher microcircuit connected to the lower microcircuit via a backward connection, i.e., targeting the Py, and the lower microcircuit connects to the higher microcircuit via a forward connection, i.e., targeting the EIN. This arrangement supported a functional specialization: the forward connection allowed storing events and favors memory in the higher circuit. The backward connection allowed the lower microcircuit to perform a dynamic signal flow gating. In contrast, alternative topologies, such as consideration of pure feedforward (b), pure feedback connections (c), or a permutation of feed forward and feedback connections (d) failed to support the priming mechanism, but may be relevant for other cooperative neural operations

Fig. 10

Facilitative signal enable the memorization of stimulation events. A mCMC ${\tilde{A}}_3$ received stimuli of distinct length and intensity and in turn stimulated a mCMC ${\tilde{A}}_2$ via a forward connection. a For default inhibitory synaptic gains, few stimuli to ${\tilde{A}}_3$ enable the selective activation of ${\tilde{A}}_2$ while keeping ${\tilde{A}}_3$ responsive (i.e., memorization and responsiveness, green area). Weak and brief stimuli fail to activate ${\tilde{A}}_2$ (no memorization, gray area), whereas strong and long stimuli activate both ${\tilde{A}}_2$ and ${\tilde{A}}_3$ (total memorization, black area). Higher levels of the facilitative feedback signal p_fac,in promote the selective activation of ${\tilde{A}}_2$. b A slight increase of the inhibitory synaptic gain H_i, favoring a transfer response behavior in ${\tilde{A}}_3$, promotes the selective activation of ${\tilde{A}}_2$. Red crosses denote an exemplary stimulation of defined intensity and duration applied to ${\tilde{A}}_3$ (color figure online)

Fig. 11

Architecture of the syntax-parsing network. a The structure-building meta-circuit was used as a building block for the syntax-parsing network. We interpreted the higher mCMC as a syntax node, representing syntactic categories, and the lower as a word node, representing single words of a vocabulary. Note that there can be several syntax nodes for each role the word can assume. b In the syntax-parsing network 17 interacting mCMCs represented either syntax nodes (1–12) or word nodes (13–17). One word and several syntax nodes form a word web (color-coded), while all syntax node of the same kind form a syntactic pool (dotted frames). Word nodes project unidirectionally to syntax nodes via feedforward connections (black arrows). Syntax nodes in different pools are interconnected by lateral connections (gray arrows) and exchange facilitative signals that condition the establishment of sustained activity patterns. Mutual inhibition within syntax pools ensures that a particular syntactic role is only assumed by one word (not shown for simplicity). Contextual information assists the semantic interpretation (dashed arrows)

Fig. 12

Analysis of the syntax-parsing network. a Top plot: word nodes respond to their consecutive stimulation (gray areas) and selectively activate syntax nodes. Bottom plot: activations of syntactic pools as sums of their respective syntax nodes. b Changing contextual information, i.e., blocking the object modifier, changes the interpretation of the sentence