Adam Ponzi

Sequentially Switching Cell Assemblies in Random Inhibitory Networks of Spiking Neurons in the Striatum

The striatum is composed of GABAergic medium spiny neurons with inhibitory collaterals forming a sparse random asymmetric network and receiving an excitatory glutamatergic cortical projection. Because the inhibitory collaterals are sparse and weak, their role in striatal network dynamics is puzzling. However, here we show by simulation of a striatal inhibitory network model composed of spiking neurons that cells form assemblies that fire in sequential coherent episodes and display complex identity–temporal spiking patterns even when cortical excitation is simply constant or fluctuating noisily. Strongly correlated large-scale firing rate fluctuations on slow behaviorally relevant timescales of hundreds of milliseconds are shown by members of the same assembly whereas members of different assemblies show strong negative correlation, and we show how randomly connected spiking networks can generate this activity. Cells display highly irregular spiking with high coefficients of variation, broadly distributed low firing rates, and interspike interval distributions that are consistent with exponentially tailed power laws. Although firing rates vary coherently on slow timescales, precise spiking synchronization is absent in general. Our model only requires the minimal but striatally realistic assumptions of sparse to intermediate random connectivity, weak inhibitory synapses, and sufficient cortical excitation so that some cells are depolarized above the firing threshold during up states. Our results are in good qualitative agreement with experimental studies, consistent with recently determined striatal anatomy and physiology, and support a new view of endogenously generated metastable state switching dynamics of the striatal network underlying its information processing operations.

Optimal balance of the striatal medium spiny neuron network.

Slowly varying activity in the striatum, the main Basal Ganglia input structure, is important for the learning and execution of movement sequences. Striatal medium spiny neurons (MSNs) form cell assemblies whose population firing rates vary coherently on slow behaviourally relevant timescales. It has been shown that such activity emerges in a model of a local MSN network but only at realistic connectivities of and only when MSN generated inhibitory post-synaptic potentials (IPSPs) are realistically sized. Here we suggest a reason for this. We investigate how MSN network generated population activity interacts with temporally varying cortical driving activity, as would occur in a behavioural task. We find that at unrealistically high connectivity a stable winners-take-all type regime is found where network activity separates into fixed stimulus dependent regularly firing and quiescent components. In this regime only a small number of population firing rate components interact with cortical stimulus variations. Around connectivity a transition to a more dynamically active regime occurs where all cells constantly switch between activity and quiescence. In this low connectivity regime, MSN population components wander randomly and here too are independent of variations in cortical driving. Only in the transition regime do weak changes in cortical driving interact with many population components so that sequential cell assemblies are reproducibly activated for many hundreds of milliseconds after stimulus onset and peri-stimulus time histograms display strong stimulus and temporal specificity. We show that, remarkably, this activity is maximized at striatally realistic connectivities and IPSP sizes. Thus, we suggest the local MSN network has optimal characteristics – it is neither too stable to respond in a dynamically complex temporally extended way to cortical variations, nor is it too unstable to respond in a consistent repeatable way. Rather, it is optimized to generate stimulus dependent activity patterns for long periods after variations in cortical excitation.

Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network.

The striatal medium spiny neuron (MSN) network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri-stimulus time histograms (PSTH) of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioral task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviorally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and outline the range of parameters where this behavior is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response which could be utilized by the animal in behavior.

Input Dependent Variability in a Model of the Striatal Medium Spiny Neuron Network

In previous work we have shown how a biologically faithful medium spiny neuron (MSN) network model of the striatum generates highly irregular firing and coherent population dynamics on slow timescales. Here we investigate how the firing irregularity depends on cortical activity. We find that irregularity is suppressed for many hundreds of msecs after cortical stimulus onset in good agreement with several neural observations. We also find that most cells spike count time series display non-normal diffusion and fractal charasteristics.

Recurrent networks expect

How the brain anticipates future events is one of the most intriguing questions in neural information processing. The brain does not simply respond to the external world but in predictably structured environments like natural language, birdsong and somatosensory flow in fluids predicts it [1]. Reactions are improved by predictive encoding of what sensory stimuli may occur as well as when they may occur. Quantitative studies of implicit timing in streaming perceptual discrimination tasks show that performance is enhanced if stimuli occur at expected times according to an established rhythm. The neural basis for implicit timing is not fully understood but oscillatory entrainment mechanisms have been suggested [2]. Recent studies confirm that low-frequency brain oscillations do become phase entrained in such tasks. Phase entrainment increases with the temporal regularity of the sensory stream and correlates with enhanced discrimination performance [3]. It is not limited to sensory cortices; larger scale cortical networks, as well as subcortical networks like the basal ganglia [4], may also be coherently modulated by the predictability of stimuli streams. The presence of large scale oscillatory entrainment suggests that network mediated neuronal ensemble dynamics may be involved. Recurrent neural networks generate complex but reproducible temporally extended dynamical activity patterns in response to input stimuli [5,6]. Such transient activity patterns have been suggested to provide a natural substrate for working memory and a representation of elapsed time. Here we add to the understanding of how random recurrent neural networks support neural information processing by demonstrating that temporal expectation also naturally emerges from their dynamics. We show that the weakly chaotic oscillations generated by recurrent networks can be phase synchronized [7] by temporally regular stimulus sequences. We show network responses are maximally discriminative when stimuli fall at their preferred phase in phase entrained networks (Figure ​(Figure1).1). Discriminability increases continuously with both temporal regularity and stimulus type predictability which also interact. Our results do not depend on specific network characteristics, are resilient to the presence of network noise and random distractor stimuli and are consistent with multiple streaming perceptual discrimination studies. Figure 1 Excess correct classification probability of on-time target stimuli compared to mistimed target stimuli (100 ms early, black ; 100 ms late, red) in a temporally regular streaming task with inter-trial-interval 800 ms. Results are plotted versus the Lyapunov ...

The inhibitory network of the striatum at the edge of chaos

The striatum forms the main input structure to the Basal Ganglia (BG), a subcortical structure involved in the selection and reinforcement learning of action sequences. It is 90% composed of medium spiny neurons (MSNs) which inhibit each other through collaterals, receive excitatory projections from cortex and are the only cells projecting outside the striatum. Because of its inhibitory structure the MSN network is often thought to act selectively, transmitting the most active cortical inputs downstream in the BG while suppressing others. However studies show that local MSN network connections are too sparse and weak to perform global selection and their function remains puzzling. Here we suggest that rather than generating a static stimulus dependent activity pattern the MSN network is optimized to generate stimulus dependent dynamical population activity patterns for extended time periods after variations in cortical excitation. Indeed MSNs form cell assemblies whose population firing rates vary coherently on slow behaviourally relevant timescales [1]. Furthermore individual MSNs display diverse response profiles locked to task and reward predicting events [2,3] with phasic activity peaks broadly distributed across the whole spectrum of delays after task events [4-6]. We have previously shown [7,8] that such activity emerges in a model of a spiking MSN network but only at realistic connectivities of ~15% and only when MSN generated inhibitory post-synaptic potentials (IPSPs) are realistically sized. Here we suggest a reason why the MSN network generates such activity. We investigate how network generated population activity interacts with temporally varying cortical driving activity, as would occur in a behavioral task. We find [9] that at unrealistically high connectivity a stable winners-take-all regime is found where network activity separates into fixed stimulus dependent regularly firing and quiescent components. In this regime only a small number of population firing rate components interact with cortical stimulus variations. Around 15% connectivity a transition to a more dynamically active regime occurs where all cells constantly switch between activity and quiescence. In the low connectivity regime MSN population components wander randomly and here too are independent of variations in cortical driving. Only in the striatally realistic transition regime do weak changes in cortical driving interact with many population components so that sequential cell assemblies are reproducibly activated for many hundreds of msecs after stimulus onset and PSTH display strong stimulus and temporal specificity. We show that this activity is maximized at striatally realistic connectivities and IPSP sizes and cortical stimuli are also maximally distinguished at striatally realistic parameter settings. In fact Lyapunov exponent computations show that, quite remarkably, the MSN network sits precisely at a marginally stable point, the edge of chaos. Thus we suggest the local MSN network has optimal characteristics - it is neither too stable to respond in a dynamically complex temporally extended way to cortical variations, nor is it too unstable to respond in a consistent repeatable way. We discuss how these properties may be utilized in temporally delayed reinforcement learning tasks strongly recruiting the striatum.