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Festa, D.

Publications and source records attributed to Festa, D..

3 recordsLinked to original sources

Heterogeneous plasticity rules generate diverse structures in recurrent networks

How do local changes at individual synapses give rise to structured recurrent connectivity? Experiments have revealed diverse forms of spike-timing-dependent plasticity (STDP) across the brain, making it difficult to predict which circuit structures different rules will generate. We developed an analytical framework mapping excitatory Hebbian, anti-Hebbian, and symmetric STDP rules to directed three-neuron motifs. Individual homogeneous rules generate restricted subsets of motifs, and no single rule produces all motifs. Combining different rules generates motifs inaccessible to individual rules and yields the full motif repertoire, and these predictions generalize to larger recurrent networks. The mapping is nevertheless highly non-unique, with distinct rules and parameters producing similar motif distributions, including distributions compatible with experimental connectivity. Thus, circuit structure constrains candidate plasticity rules but does not uniquely identify them. Finally, networks with similar motif distributions can nonetheless differ in their firing rates and pairwise correlations, suggesting that activity may provide an additional constraint on candidate plasticity rules.

neuroscience↗

Structured stabilization in recurrent neural circuits through inhibitory synaptic plasticity

In cortical circuits, inhibitory interneurons play a dual role: they regulate overall activity levels to prevent runaway excitation, and contribute to diverse computations. While unstructured inhibitory synaptic connections achieve the first role by homeostatically regulating firing rates, computational tasks often require structured excitatory-inhibitory (E/I) connectivity. Here, we consider a broad class of pairwise inhibitory spike-timing dependent plasticity (iSTDP) rules, demonstrating how inhibitory connections can self-organize to both stabilize excitation and generate functionally relevant connectivity motifs--a process we call "structured stabilization". We show that in both small E/I circuits and large spiking recurrent neural networks the choice of iSTDP rule can lead to either mutually connected E/I pairs, or to lateral inhibition, where an inhibitory neuron connects to an excitatory neuron that does not directly connect back to it. In a one-dimensional ring network with two inhibitory subpopulations following these distinct iSTDP rules, the effective connectivity within the excitatory units self-organizes into a Mexican-hat-like profile, with excitatory influence in the center and inhibitory influence away from the center. This leads to emergent network responses such as contextual modulation effects as in the visual cortex and spatially modular activity characteristic of developmental spontaneous activity. Our theoretical work introduces a family of rules that retains the broad applicability and simplicity of spike-timing-based plasticity, while stabilizing activity and promoting specific connectivity motifs which support emergent network computations. Significance StatementNeural circuits are faced with the dual challenge of keeping activity stable while modifying synaptic strengths during development and learning. A prominent mechanism to keep excitation in check is inhibitory synaptic plasticity. In recurrent circuit models, we show that plasticity rules based on spike timing at synapses from inhibitory to excitatory neurons can stabilize activity while reinforcing specific connectivity motifs. The shape of these rules dictates the resulting motifs, and determines whether inhibitory neurons form strong reciprocal connections with their excitatory partners or lateral connections onto excitatory neurons that do not connect back. In recurrent networks with local excitatory connectivity, these motifs generate surround suppression as in the visual cortex, and long-range spatial correlations characteristic of developmental spontaneous activity.

neuroscience↗

Neuronal variability reflects probabilistic inference tuned to natural image statistics

Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike-count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses -- their variability -- can be explained by a probabilistic representation tuned to naturalistic inputs.

neuroscience↗