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1. Functional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making

   https://doi.org/10.3389/fnins.2022.801847  

   Barranca, Victor J.; Bhuiyan, Asha; Sundgren, Max; Xing, Fangzhou (February 2022, Frontiers in Neuroscience)

   The notion that a neuron transmits the same set of neurotransmitters at all of its post-synaptic connections, typically known as Dale's law, is well supported throughout the majority of the brain and is assumed in almost all theoretical studies investigating the mechanisms for computation in neuronal networks. Dale's law has numerous functional implications in fundamental sensory processing and decision-making tasks, and it plays a key role in the current understanding of the structure-function relationship in the brain. However, since exceptions to Dale's law have been discovered for certain neurons and because other biological systems with complex network structure incorporate individual units that send both positive and negative feedback signals, we investigate the functional implications of network model dynamics that violate Dale's law by allowing each neuron to send out both excitatory and inhibitory signals to its neighbors. We show how balanced network dynamics, in which large excitatory and inhibitory inputs are dynamically adjusted such that input fluctuations produce irregular firing events, are theoretically preserved for a single population of neurons violating Dale's law. We further leverage this single-population network model in the context of two competing pools of neurons to demonstrate that effective decision-making dynamics are also produced, agreeing with experimental observations from honeybee dynamics in selecting a food source and artificial neural networks trained in optimal selection. Through direct comparison with the classical two-population balanced neuronal network, we argue that the one-population network demonstrates more robust balanced activity for systems with less computational units, such as honeybee colonies, whereas the two-population network exhibits a more rapid response to temporal variations in network inputs, as required by the brain. We expect this study will shed light on the role of neurons violating Dale's law found in experiment as well as shared design principles across biological systems that perform complex computations. 

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2. Balanced networks under spike-time dependent plasticity

   https://doi.org/10.1371/journal.pcbi.1008958  

   Akil, Alan Eric; Rosenbaum, Robert; Josić, Krešimir (May 2021, PLOS Computational Biology)

   Gjorgjieva, Julijana (Ed.)

   The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory–inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a theory of spike–timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity–induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input. 

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3. Input correlations impede suppression of chaos and learning in balanced firing-rate networks

   https://doi.org/10.1371/journal.pcbi.1010590  

   Engelken, Rainer; Ingrosso, Alessandro; Khajeh, Ramin; Goedeke, Sven; Abbott, L. F. (December 2022, PLOS Computational Biology)

   Aljadeff, Johnatan (Ed.)

   Neural circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control spontaneous network activity. We show that in firing-rate networks in the balanced state, external control of recurrent dynamics, i.e., the suppression of internally-generated chaotic variability, strongly depends on correlations in the input. A distinctive feature of balanced networks is that, because common external input is dynamically canceled by recurrent feedback, it is far more difficult to suppress chaos with common input into each neuron than through independent input. To study this phenomenon, we develop a non-stationary dynamic mean-field theory for driven networks. The theory explains how the activity statistics and the largest Lyapunov exponent depend on the frequency and amplitude of the input, recurrent coupling strength, and network size, for both common and independent input. We further show that uncorrelated inputs facilitate learning in balanced networks. 

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4. Sparse balance: Excitatory-inhibitory networks with small bias currents and broadly distributed synaptic weights

   https://doi.org/10.1371/journal.pcbi.1008836  

   Khajeh, Ramin; Fumarola, Francesco; Abbott, LF (February 2022, PLOS Computational Biology)

   Soltani, Alireza (Ed.)

   Cortical circuits generate excitatory currents that must be cancelled by strong inhibition to assure stability. The resulting excitatory-inhibitory (E-I) balance can generate spontaneous irregular activity but, in standard balanced E-I models, this requires that an extremely strong feedforward bias current be included along with the recurrent excitation and inhibition. The absence of experimental evidence for such large bias currents inspired us to examine an alternative regime that exhibits asynchronous activity without requiring unrealistically large feedforward input. In these networks, irregular spontaneous activity is supported by a continually changing sparse set of neurons. To support this activity, synaptic strengths must be drawn from high-variance distributions. Unlike standard balanced networks, these sparse balance networks exhibit robust nonlinear responses to uniform inputs and non-Gaussian input statistics. Interestingly, the speed, not the size, of synaptic fluctuations dictates the degree of sparsity in the model. In addition to simulations, we provide a mean-field analysis to illustrate the properties of these networks. 

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5. Printed MoS2 memristive nanosheet networks for spiking neurons with multi-order complexity

   https://doi.org/10.1038/s41565-026-02149-6  

   Hadke, Shreyash S; Klingler, Carol N; Brown, Spencer T; Holla, Meghana; Zhuang, Xudong; Li, Linda; Utama, M_Iqbal Bakti; Diaz-Arauzo, Santiago; Chapagain, Anurag; Li, Siyang; et al (May 2026, Nature Nanotechnology)

   Artificial neurons that reproduce the rich dynamical behaviour of biological spiking are essential for neuromorphic hardware and biohybrid interfaces, yet scalable solution-processed devices with physiologically relevant spiking characteristics remain elusive. Here we demonstrate aerosol-jet-printed memristive networks of MoS2 nanosheets that exhibit thermally activated filamentary switching and snap-back negative differential resistance, enabling volatile threshold switching in fully printed graphene/MoS2/graphene devices on flexible substrates. In situ thermal imaging and circuit modelling reveal that current-constricted filaments formed through Joule heating govern the nonlinear switching dynamics. These printed memristors enable oscillatory and spiking neuron circuits with tunable frequencies up to 20 kHz and stable operation over more than 10^6 cycles. Simple neuristor circuits realize first-, second- and third-order spiking complexity, including integrate-and-fire behaviour, spike latency, tonic firing, class 1 excitability, tonic bursting and phasic dynamics. The generated spike waveforms match physiological timescales and stimulate Purkinje neurons in mouse cerebellar slices. Our results establish printed nanosheet memristive networks as a scalable platform for bio-realistic neuromorphic hardware and flexible brain–machine interfaces. 

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