Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying neural circuits and computational principles, we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning, and motor control. We focus on cast and surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule, the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast-and-surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.

Postural adjustments associated with the task of rising on tiptoes were investigated in a reaction time paradigm in 10 normal subjects and 18 patients with cerebellar disorders. Cerebellar dysfunction was due to either degenerative cerebellar disease, tumor, or ischemia. Displacements of the center of foot pressure (CFP) were recorded. The task, accomplished by the triceps surae muscle (executional activity, mean latency of 411 ms), is mechanically effective only if the center of gravity has been shifted forward in advance. To this effect, a phasic burst of preparatory EMG activity in the tibialis anterior normally occurs at a mean latency of 163 ms, shifting the center of gravity forward. Shortly thereafter, activity of the quadriceps femoris (175 ms) extends the knee and aids the forward shift of the center of gravity. Different aspects of this motor sequence were disturbed in individual patients: Latencies of preparatory and executional activity were uncorrelated in 15 of the 18 patients. Executional (n = 16) or preparatory (n = 13) EMG activity was tonic instead of phasic. Latencies of either preparatory or executional EMG activities or both were prolonged (n = 10). The time interval between motor preparation and execution was increased (n = 9). The trial-to-trial variability of biomechanical parameters and EMG latency was increased. Preparatory EMG activity in the quadriceps was entirely missing (n = 9), resulting in knee bending at the unsuccessful attempt to rise on tiptoes. Patients who were most severely affected had no preparatory activity at all (n = 2), and therefore were unable to perform the task.(ABSTRACT TRUNCATED AT 250 WORDS)

Insects are able to solve basic numerical cognition tasks. We show that estimation of numerosity can be realized and learned by a single spiking neuron with an appropriate synaptic plasticity rule. This model can be efficiently trained to detect arbitrary spatiotemporal spike patterns on a noisy and dynamic background with high precision and low variance. When put to test in a task that requires counting of visual concepts in a static image it required considerably less training epochs than a convolutional neural network to achieve equal performance. When mimicking a behavioral task in free-flying bees that requires numerical cognition, the model reaches a similar success rate in making correct decisions. We propose that using action potentials to represent basic numerical concepts with a single spiking neuron is beneficial for organisms with small brains and limited neuronal resources.

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The cerebellar-thalamo-cortical (CTC) system plays a major role in controlling timing and coordination of voluntary movements. However, the functional impact of this system on motor cortical sites has not been documented in a systematic manner. We addressed this question by implanting a chronic stimulating electrode in the superior cerebellar peduncle (SCP) and recording evoked multiunit activity (MUA) and the local field potential (LFP) in the primary motor cortex ([Formula: see text]), the premotor cortex ([Formula: see text]) and the somatosensory cortex ([Formula: see text]). The area-dependent response properties were estimated using the MUA response shape (quantified by decomposing into principal components) and the time-dependent frequency content of the evoked LFP. Each of these signals alone enabled good classification between the somatosensory and motor sites. Good classification between the primary motor and premotor areas could only be achieved when combining features from both signal types. Topographical single-site representation of the predicted class showed good recovery of functional organization. Finally, the probability for misclassification had a broad topographical organization. Despite the area-specific response features to SCP stimulation, there was considerable site-to-site variation in responses, specifically within the motor cortical areas. This indicates a substantial SCP impact on both the primary motor and premotor cortex. Given the documented involvement of these cortical areas in preparation and execution of movement, this result may suggest a CTC contribution to both motor execution and motor preparation. The stimulation responses in the somatosensory cortex were sparser and weaker. However, a functional role of the CTC system in somatosensory computation must be taken into consideration.