Neural coding: time contraction and dilation in the striatum

The neural basis of somatosensory temporal discrimination threshold as a paradigm for time processing in the sub-second range: An updated review

BACKGROUND AND OBJECTIVE

The temporal aspect of somesthesia is a feature of any somatosensory process and a pre-requisite for the elaboration of proper behavior. Time processing in the milliseconds range is crucial for most of behaviors in everyday life. The somatosensory temporal discrimination threshold (STDT) is the ability to perceive two successive stimuli as separate in time, and deals with time processing in this temporal range. Herein, we focus on the physiology of STDT, on a background of the anatomophysiology of somesthesia and the neurobiological substrates of timing.

METHODS

A review of the literature through PubMed & Cochrane databases until March 2023 was performed with inclusion and exclusion criteria following PRISMA recommendations.

RESULTS

1151 abstracts were identified. 4 duplicate records were discarded before screening. 957 abstracts were excluded because of redundancy, less relevant content or not English-written. 4 were added after revision. Eventually, 194 articles were included.

CONCLUSIONS

STDT encoding relies on intracortical inhibitory S1 function and is modulated by the basal ganglia-thalamic-cortical interplay through circuits involving the nigrostriatal dopaminergic pathway and probably the superior colliculus.

Encoding Temporal Regularities and Information Copying in Hippocampal Circuits

Discriminating, extracting and encoding temporal regularities is a critical requirement in the brain, relevant to sensory-motor processing and learning. However, the cellular mechanisms responsible remain enigmatic; for example, whether such abilities require specific, elaborately organized neural networks or arise from more fundamental, inherent properties of neurons. Here, using multi-electrode array technology, and focusing on interval learning, we demonstrate that sparse reconstituted rat hippocampal neural circuits are intrinsically capable of encoding and storing sub-second-order time intervals for over an hour timescale, represented in changes in the spatial-temporal architecture of firing relationships among populations of neurons. This learning is accompanied by increases in mutual information and transfer entropy, formal measures related to information storage and flow. Moreover, temporal relationships derived from previously trained circuits can act as templates for copying intervals into untrained networks, suggesting the possibility of circuit-to-circuit information transfer. Our findings illustrate that dynamic encoding and stable copying of temporal relationships are fundamental properties of simple in vitro networks, with general significance for understanding elemental principles of information processing, storage and replication.

Supervised learning in spiking neural networks with FORCE training

Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.