A Compositionality Machine Realized by a Hierarchic Architecture of Synfire Chains

The composition of complex behavior is thought to rely on the concurrent and sequential activation of simpler action components, or primitives. Systems of synfire chains have previously been proposed to account for either the simultaneous or the sequential aspects of compositionality; however, the compatibility of the two aspects has so far not been addressed. Moreover, the simultaneous activation of primitives has up until now only been investigated in the context of reactive computations, i.e., the perception of stimuli. In this study we demonstrate how a hierarchical organization of synfire chains is capable of generating both aspects of compositionality for proactive computations such as the generation of complex and ongoing action. To this end, we develop a network model consisting of two layers of synfire chains. Using simple drawing strokes as a visualization of abstract primitives, we map the feed-forward activity of the upper level synfire chains to motion in two-dimensional space. Our model is capable of producing drawing strokes that are combinations of primitive strokes by binding together the corresponding chains. Moreover, when the lower layer of the network is constructed in a closed-loop fashion, drawing strokes are generated sequentially. The generated pattern can be random or deterministic, depending on the connection pattern between the lower level chains. We propose quantitative measures for simultaneity and sequentiality, revealing a wide parameter range in which both aspects are fulfilled. Finally, we investigate the spiking activity of our model to propose candidate signatures of synfire chain computation in measurements of neural activity during action execution

A reafferent and feed-forward model of song syntax generation in the Bengalese finch

Adult Bengalese finches generate a variable song that obeys a distinct and individual syntax. The syntax is gradually lost over a period of days after deafening and is recovered when hearing is restored. We present a spiking neuronal network model of the song syntax generation and its loss, based on the assumption that the syntax is stored in reafferent connections from the auditory to the motor control area. Propagating synfire activity in the HVC codes for individual syllables of the song and priming signals from the auditory network reduce the competition between syllables to allow only those transitions that are permitted by the syntax. Both imprinting of song syntax within HVC and the interaction of the reafferent signal with an efference copy of the motor command are sufficient to explain the gradual loss of syntax in the absence of auditory feedback. The model also reproduces for the first time experimental findings on the influence of altered auditory feedback on the song syntax generation, and predicts song- and species-specific low frequency components in the LFP. This study illustrates how sequential compositionality following a defined syntax can be realized in networks of spiking neurons

Compositionality in neural control: an interdisciplinary study of scribbling movements in primates

This article discusses the compositional structure of hand movements by analyzing and modeling neural and behavioral data obtained from experiments where a monkey (Macaca fascicularis) performed scribbling movements induced by a search task. Using geometrically based approaches to movement segmentation, it is shown that the hand trajectories are composed of elementary segments that are primarily parabolic in shape. The segments could be categorized into a small number of classes on the basis of decreasing intra-class variance over the course of training. A separate classification of the neural data employing a hidden Markov model showed a coincidence of the neural states with the behavioral categories. An additional analysis of both types of data by a data mining method provided evidence that the neural activity patterns underlying the behavioral primitives were formed by sets of specific and precise spike patterns. A geometric description of the movement trajectories, together with precise neural timing data indicates a compositional variant of a realistic synfire chain model. This model reproduces the typical shapes and temporal properties of the trajectories; hence the structure and composition of the primitives may reflect meaningful behavior

Functional Anatomy: Dynamic States in Basal Ganglia Circuits

The most appealing models of how the basal ganglia function propose distributed patterns of cortical activity selectively interacting with striatal networks to yield the execution of context-dependent movements. If movement is encoded by patterns of activity then these may be disrupted by influences at once more subtle and more devastating than the increase or decrease of neuronal firing that dominate the usual models of the circuit. In the absence of dopamine the compositional capabilities of cell assemblies in the network could be disrupted by the generation of dominant synchronous activity that engages most of the system. Experimental evidence about Parkinson's disease suggests that dopamine loss produces abnormal patterns of activity in different nuclei. For example, increased oscillatory activity arises in the GPe, GPi, and STN and is reflected as increased cortical beta frequency coherence disrupting the ability to produce motor sequences. When the idea of deep brain stimulation was proposed – it was supported by the information that lesions of the subthalamus reversed the effects of damage to the dopamine input to the system. However, it seems increasingly unlikely that the stimulation acts by silencing the nucleus as was at first proposed. Perhaps the increased cortical beta activity caused by the lack of dopamine could have disabled the patterning of network activity. Stimulation of the subthalamic nucleus disrupts the on-going cortical rhythms. Subsequently asynchronous firing is reinstated and striatal cell assemblies and the whole basal ganglia circuit engage in a more normal pattern of activity. We will review the different variables involved in the generation of sequential activity patterns, integrate our data on deep brain stimulation and network population dynamics, and thus provide a novel interpretation of functional aspects of basal ganglia circuitry

Limits to the Development of Feed-Forward Structures in Large Recurrent Neuronal Networks

Spike-timing dependent plasticity (STDP) has traditionally been of great interest to theoreticians, as it seems to provide an answer to the question of how the brain can develop functional structure in response to repeated stimuli. However, despite this high level of interest, convincing demonstrations of this capacity in large, initially random networks have not been forthcoming. Such demonstrations as there are typically rely on constraining the problem artificially. Techniques include employing additional pruning mechanisms or STDP rules that enhance symmetry breaking, simulating networks with low connectivity that magnify competition between synapses, or combinations of the above. In this paper, we first review modeling choices that carry particularly high risks of producing non-generalizable results in the context of STDP in recurrent networks. We then develop a theory for the development of feed-forward structure in random networks and conclude that an unstable fixed point in the dynamics prevents the stable propagation of structure in recurrent networks with weight-dependent STDP. We demonstrate that the key predictions of the theory hold in large-scale simulations. The theory provides insight into the reasons why such development does not take place in unconstrained systems and enables us to identify biologically motivated candidate adaptations to the balanced random network model that might enable it

Limits to the Development of Feed-Forward Structures in Large Recurrent Neuronal Networks

Spike-timing dependent plasticity (STDP) has traditionally been of great interest to theoreticians, as it seems to provide an answer to the question of how the brain can develop functional structure in response to repeated stimuli. However, despite this high level of interest, convincing demonstrations of this capacity in large, initially random networks have not been forthcoming. Such demonstrations as there are typically rely on constraining the problem artificially. Techniques include employing additional pruning mechanisms or STDP rules that enhance symmetry breaking, simulating networks with low connectivity that magnify competition between synapses, or combinations of the above. In this paper, we first review modeling choices that carry particularly high risks of producing non-generalizable results in the context of STDP in recurrent networks. We then develop a theory for the development of feed-forward structure in random networks and conclude that an unstable fixed point in the dynamics prevents the stable propagation of structure in recurrent networks with weight-dependent STDP. We demonstrate that the key predictions of the theory hold in large-scale simulations. The theory provides insight into the reasons why such development does not take place in unconstrained systems and enables us to identify biologically motivated candidate adaptations to the balanced random network model that might enable it

Integrative (Synchronisations-)Mechanismen der (Neuro-)Kognition vor dem Hintergrund des (Neo-)Konnektionismus, der Theorie der nichtlinearen dynamischen Systeme, der Informationstheorie und des Selbstorganisationsparadigmas

Der Gegenstand der vorliegenden Arbeit besteht darin, aufbauend auf dem (Haupt-)Thema, der Darlegung und Untersuchung der Lösung des Bindungsproblems anhand von temporalen integrativen (Synchronisations-)Mechanismen im Rahmen der kognitiven (Neuro-)Architekturen im (Neo-)Konnektionismus mit Bezug auf die Wahrnehmungs- und Sprachkognition, vor allem mit Bezug auf die dabei auftretende Kompositionalitäts- und Systematizitätsproblematik, die Konstruktion einer noch zu entwickelnden integrativen Theorie der (Neuro-)Kognition zu skizzie-ren, auf der Basis des Repräsentationsformats einer sog. „vektoriellen Form“, u.z. vor dem Hintergrund des (Neo-)Konnektionismus, der Theorie der nichtlinearen dynamischen Systeme, der Informationstheorie und des Selbstorganisations-Paradigmas