Biology subjects

Claudia Clopath

Publications and source records attributed to Claudia Clopath.

4 recordsLinked to original sources

bioRxiv2016

Receptive field formation by interacting excitatory and inhibitory synaptic plasticity

The stimulus selectivity of synaptic currents in cortical neurons often shows a co-tuning of excitation and inhibition, but the mechanisms that underlie the emergence and plasticity of this co-tuning are not fully understood. Using a computational model, we show that an interaction of excitatory and inhibitory synaptic plasticity reproduces both the developmental and - when combined with a disinhibitory gate - the adult plasticity of excitatory and inhibitory receptive fields in auditory cortex. The co-tuning arises from inhibitory plasticity that balances excitation and inhibition, while excitatory stimulus selectivity can result from two different mechanisms. Inhibitory inputs with a broad stimulus tuning introduce a sliding threshold as in Bienenstock-Cooper-Munro rules, introducing an excitatory stimulus selectivity at the cost of a broader inhibitory receptive field. Alternatively, input asymmetries can be amplified by synaptic competition. The latter leaves any receptive field plasticity transient, a prediction we verify in recordings in auditory cortex.

Neuroscience↗

bioRxiv2016

Beyond spike-timing-dependent plasticity: a computational study of plasticity gradients across basal dendrites.

Synaptic plasticity is thought to be the principal mechanism underlying learning in the brain. Models of plastic networks typically combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule. However, a point neuron does not capture the complexity of dendrites, which allow non-linear local processing of the synaptic inputs. Furthermore, experimental evidence suggests that STDP is not the only learning rule available to neurons. Implementing biophysically realistic neuron models, we studied how dendrites allow for multiple synaptic plasticity mechanisms to coexist in a single cell. In these models, we compared the conditions for STDP and for the synaptic strengthening by local dendritic spikes. We further explored how the connectivity between two cells is affected by these plasticity rules and the synaptic distributions. Finally, we show how memory retention in associative learning can be prolonged in networks of neurons with dendrites.

Neuroscience↗

bioRxiv2016

Cerebellar learning using perturbations

The cerebellum aids the learning and execution of fast coordinated movements, with acquired information being stored by plasticity of parallel fibre--Purkinje cell synapses. According to the current consensus, erroneously active parallel fibre synapses are depressed by complex spikes arising when climbing fibres signal movement errors. However, this theory cannot solve the credit assignment problem of using the limited information from a global movement evaluation to optimise behaviour by guiding the plasticity in numerous neurones. We identify the possible implementation of an algorithm solving this problem, whereby spontaneous complex spikes perturb ongoing movements, create an eligibility trace for plasticity and signal resulting error changes to guide plasticity. These error changes are extracted by adaptively cancelling the average error. This framework, stochastic gradient descent with estimated global errors, generates specific predictions for synaptic plasticity rules that contradict the current consensus. However, in vitro plasticity experiments under physiological conditions verified our predictions, highlighting the sensitivity of plasticity studies to unphysiological conditions. Using numerical and analytical approaches we demonstrate the convergence and estimate the capacity of learning in our implementation. Finally, a similar mechanism may operate during optimisation of action sequences by the basal ganglia, where dopamine could both initiate movements and signal rewards, analogously to the dual perturbation and correction role of the climbing fibre outlined here.

Neuroscience↗

bioRxiv2015

On the distribution and function of synaptic clusters

Local non-linearities in dendrites render neuronal output dependent on the spatial distribution of synapses. A neuron will activate differently depending on whether active synapses are spatially clustered or dispersed. While this sensitivity can in principle expand neuronal computational capacity, it has thus far been employed in very few learning paradigms. To make use of this sensitivity, groups of correlated neurons need to make contact with distinct dendrites, and this requires a mechanism to ensure the correct distribution of synapses contacting from distinct ensembles. To address this problem, we introduce the requirement that on a short time scale, a pre-synaptic neuron makes a constant number of synapses with the same strength on a post-synaptic neuron. We find that this property enables clusters to distribute correctly and guarantees their functionality. Furthermore, we demonstrate that a change in the input statistics can reshape the spatial distribution of synapses. Finally, we show under which conditions clusters do not distribute correctly, e.g. when cross-talk between dendrites is too strong. As well as providing insight into potential biological mechanisms of learning, this work paves the way for new learning algorithms for artificial neural networks that exploit the spatial distribution of synapses.

Preprint↗